Strategic insight and guidance to assist higher education institutions in leveraging the benefits of technology&nbsp; – from artificial intelligence to campus infrastructure

Strategic insight and guidance to assist higher education institutions in leveraging the benefits of digital technology. Find practical advice on using artificial intelligence (AI) in teaching, assessment, research and other university functions as well as broader insights on edtech, hybrid working, virtual reality, cybersecurity, digital literacy and managing campus infrastructure.&nbsp;

Digital transformation

Working closely with suppliers is essential to maintaining a strong supply chain. Unfortunately, many businesses continue to face challenges like poor communication, unclear procedures and delayed responses, which can cause inefficiencies and disrupt operations.Poor supplier collaboration not only delays procurement cycles but also exposes companies to compliance risks and cost overruns. Without a cohesive strategy, vendors encounter varying onboarding requirements, misaligned expectations and excessive administrative challenges. To resolve these issues, a transformation is essential – one that can be achieved through process orchestration.Understanding process orchestration in procurementProcess orchestration is more than just automation; it is the strategic coordination of procurement workflows across multiple systems, teams and stakeholders. Unlike traditional workflow automation, which focuses on isolated tasks, orchestration ensures end-to-end visibility, control and adaptability in supplier-related processes.By leveraging&nbsp;procurement orchestration tools, procurement teams can synchronise activities such as vendor onboarding, contract management and compliance verification. This results in reduced manual effort, fewer errors and enhanced decision-making capabilities. Ultimately, process orchestration allows procurement leaders to manage supplier relationships proactively rather than reactively.Enhancing supplier communication and collaborationBuilding strong relationships with suppliers depends on trust, which is fostered through clear and consistent communication. Unfortunately, many businesses still rely on old methods and isolated systems, which can lead to confusion and delays.&nbsp;Improved process organisation allows businesses to share information in real time using centralised systems. This enables suppliers to monitor order statuses, compliance and workflows, reducing the need for constant back-and-forth communication. By integrating procurement data with supplier management tools, companies can achieve more efficient and transparent collaboration.Streamlining supplier onboarding and complianceThe process of bringing new suppliers on board often encounters delays due to extensive paperwork and lengthy approval processes. Automation can alleviate these issues by streamlining approval workflows and embedding compliance checks directly into procurement activities.AI-driven risk assessments enable businesses to evaluate suppliers’ compliance in real time. Automated alerts help to ensure that suppliers meet regulatory standards, while digital workflows reduce reliance on manual intervention. This approach not only speeds up onboarding but also strengthens supply chain integrity.Implementing process organisation for supplier collaborationEffective process organisation begins with a comprehensive assessment of existing supplier collaboration challenges. Identifying bottlenecks is a critical step. Companies should map out their processes to uncover inefficiencies, unnecessary steps and communication gaps. This often reveals delays caused by excessive approval layers, isolated information and errors from manual data entry.Establishing communication protocols is a key component. Process organisation should define the sequence of tasks and the flow of information. This includes setting thresholds for notifications, procedures for urgent issues and selecting appropriate communication channels. Well-designed protocols ensure timely and relevant information exchange, preventing overload and gaps.Process organisation for sustained supplier successSupplier collaboration is an ongoing effort that requires continuous optimisation. Process organisation assists procurement teams in monitoring supplier performance, tracking key metrics and implementing data-driven improvements.A well-organised procurement process supports long-term supplier success by minimising friction, enhancing efficiency and aligning strategies. When suppliers and procurement teams work together effectively, businesses can achieve higher levels of operational excellence, better risk management and sustainable growth.

Effective supplier collaboration is crucial for a resilient supply chain. Process orchestration enhances communication, streamlines onboarding and ensures compliance, helping businesses reduce inefficiencies and strengthen supplier relationships

Improving supplier collaboration through process orchestration

AI literacy as a subject area has percolated to all levels of education. The identified key skills from elementary (primary) school level to adult education appear to overlap significantly, even though they come with an “age appropriate” qualification. For example, advocates of AI literacy for K12 education listed the need to “understand how AI works, how it can be used to solve problems, how to evaluate and creatively engage with AI, and the potential social impact of AI”. These skills are not so different from those identified in frameworks for higher education, which generally include:an understanding of how AI worksusing and applying AI ethically to lead meaningful livesanalysing and evaluating AI for bias and accuracythe ability to create AI solutions for a given problem and contribute to the world.A key question we therefore need to ask is: how would “age appropriate” AI literacy look for university students, who are expected to be contributing members of society and industry upon graduation?&nbsp;How can we teach AI literacy skills?Is AI literacy an information skill?Spotlight guide: Bringing GenAI into the university classroomConsensus is that AI literacy will be an essential requirement for the future world of work. So, students will need a range of skills beyond basic competency. Recent studies, such as one conducted by LinkedIn, suggest that prompt engineering is a skill in demand. However, prompt engineering is specific to current variants of generative AI and just one of many skills that AI-related technology requires. Furthermore, agentic AI (also known as autonomous AI) appears to be the next major development and would require people to develop skills to design AI tools and enhancements that are specific to their organisation’s needs.&nbsp;AI for learningMany faculty around the world have deployed generative AI learning assistants for their courses. For example, we have Project NTU AI learning assistants (NALA), where faculty can build their own chatbots based on their course’s pedagogical needs. While the intent is to enable faculty to design and control learning experiences with generative AI, we have also noticed its potential to uncover and develop AI literacy in students. One of our studies reveals that even when a generative AI learning assistant is designed to be Socratic, the way students interact with the assistant would result in significant differences in learning outcomes. Students who raise questions about fundamental concepts and solicit more examples with the AI learning assistant have significant learning gains. Based on this finding, we can educate students on how they should engage with AI for better learning outcomes.&nbsp;In addition, the way students construct their prompts may result in a non-response. For example, one of our students did not receive a response from the learning assistant when he requested a “cheat sheet”. Had he requested for a “list of formulas”, he would have had the desired response.This is a learning opportunity to share the guard rails in some of the enterprise GenAI implementations with students. Finally, one of the faculty learning communities has also used NALA to teach prompting skills and ethics as a part of AI literacy.&nbsp;AI as learningAI learning assistants may have provided opportunities for students to gain an understanding of AI while learning. However, such uses do not help university students to develop higher-order AI literacy.&nbsp;Some researchers have connected Bloom’s Taxonomy to higher levels of AI literacies. At the highest level, students would evaluate and create their own GenAI applications. These competencies match the earlier point regarding designing AI tools and enhancements in the future workplace.&nbsp;Like platforms developed at other universities, Project NALA offers a front-end interface (known as the builder) for faculty to create their own learning assistant. An idea we have is to open the builder up to students to allow them to create their own GenAI assistant as part of our AI literacy curriculum. As they design, configure and test their own assistant, they will learn firsthand how generative AI works. They get to test performance-enhancement approaches beyond prompt engineering, such as grounding the learning assistant with curated materials (retrieval-augmented generation) and advanced ideas such as incorporating knowledge graphs.&nbsp;The learning activities can also include conducting evaluative studies on how the performance of the GenAI can be enhanced with these techniques and how to better implement ethical principles. Faculty would have access to the changes students make to their learning assistants to provide feedback. This would further enhance students’ AI literacy. In fact, it could be a student-as-partner model, where faculty learn alongside the students.&nbsp;AI literacy is a skills progressionAI is a transformative technology that is here to stay. As universities support students to build AI literacy, institutions and educators need to reflect on whether our AI literacy programmes are elementary or supporting students to develop the higher-order skills they will need for the workplace. They should have the opportunity to analyse, evaluate and create responsible AI solutions. Offering students the opportunity to build their own AI assistants could be a way forward to develop these much-needed skills.Fun Siong Lim is head of the Centre for the Applications of Teaching &amp; Learning Analytics for Students (ATLAS) in the Institute of Pedagogical Innovation Research and Excellence (InsPIRE) at Nanyang Technological University, Singapore.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

What does ‘age appropriate’ AI literacy look like?

As AI literacy becomes an essential work skill, universities need to move beyond developing these competencies at ‘primary school’ level in their students. Here, Fun Siong Lim reflects on frameworks to support higher-order AI literacies

What does ‘age appropriate’ AI literacy look like in higher education?

How can we leverage AI tools to enhance active learning? AI can enable students to engage more deeply and can support their problem-solving processes. It can also automate tasks, personalise learning experiences and enable real-time feedback.Active Blended Inclusive Learning means using digital technologies, such as AI, to help learners to engage inclusively with a series of activities, which require them to produce evidence of their learning. Active learning has been shown to have a range of benefits, promoting equity and enhancing student outcomes. Using active learning in our classrooms has led to increased engagement and academic achievement, development of critical thinking and higher order thinking skills, and deeper retention of knowledge.How to apply AI to promote active learningGeneration of images: Applications like&nbsp;DALL·E,&nbsp;Leonardo.ai and&nbsp;Flux can be used to generate images from simple text, to support discussions and encourage creativity. They also can be used to generate images that may not yet exist. For example, a reconstruction of a marketplace in Ancient Mesopotamia can help bring history to life.&nbsp;Generation of videos: Many students use YouTube or other videos to support learning. Tools such as&nbsp;Synthesia enable users to create videos incorporating&nbsp;AI avatars that can present text: Students can convert textual course content to videos, making it more engaging. Videos are simple to edit and can be translated into any language.&nbsp;Open AI’s Sora model can create photorealistic video models and special effects.Intellectual play and philosophical inquiry:&nbsp;Tools like&nbsp;OpenAI’s voice mode,&nbsp;ElevenLabs’ conversational agents and&nbsp;Google NotebookLM can be used for thought experiments, philosophical dialogues, roleplays, podcasts or scenario-based learning. For example, students can roleplay with a thinker (eg,&nbsp;bel hooks on education or&nbsp;Ada Lovelace on mathematics) or explore a scenario or thought experiment relevant to their field (eg,&nbsp;a play-oriented university or&nbsp;Laplace’s Demon and quantum mechanics).Generate code:&nbsp;Anthropic Claude,&nbsp;OpenAI ChatGPT and&nbsp;Google Gemini can generate code snippets, suggest improvements and debug errors.&nbsp;Codecademy provides step-by-step coding tutorials, including how to use AI in coding.Scaffold complex topics: AI tools can generate supporting material to scaffold comprehension of difficult topics, such as mathematical problems, timelines of historical events and explanations of policy documents.Feedback on writing: Students can use AI tools, such as Chat GPT’s canvas feature and&nbsp;Microsoft Copilot’s integration with Microsoft Word, to get feedback on any specific aspects of their writing (such as clarity, spelling, critical thinking, subject knowledge) without compromising academic integrity. For example, a student could request the following feedback: “Please review my draft for clarity, conciseness, grammar and style. Offer guidance and examples of how I might improve my writing skills but do not rewrite any content.”Develop critical thinking: AI can help support critical thinking by advising on alternative sources and providing evaluations of these sources. This is more powerful than a web search because it can infer what the user is looking for and suggest optimised keywords for searches. Yes, AI can hallucinate sources, but this provides more opportunities for students to critically evaluate output. With fine-tuning and refined prompts, hallucinations can be reduced to a minimum. In particular, models such as&nbsp;Perplexity and&nbsp;GPT-4o are getting better at avoiding such hallucinations, with the ability to directly link to contemporary source materials and referencing real-world links outside the training data.&nbsp;Interactive and learning games:&nbsp;Teaching technical terminology can be easily reframed within a responsive fill-in-the-blanks game, rivalling Wordle. Using&nbsp;Anthropic’s Claude for coding,&nbsp;Black Forest Labs’ Flux for visuals, and&nbsp;Google Labs’ MusicFX for sound, educators can create stunning multimedia games that make learning and revision engaging, rewarding and something students actively return to.Let’s look at AI as a reasoning partner, not a shortcutTweaks to make when teaching political science and social policies in the GenAI ageAn AI toolkit for all aspects of academic lifeChallenges and possible solutions:Culture of resistance: There is often resistance from colleagues who may fear new technology. Addressing this requires accessible tools, clear ethical guidelines and training to support staff.&nbsp;Data privacy: We need to make ourselves aware of whether tools use our data to train their models, which types of data are appropriate to input and if our organisations have non-disclosure agreements with these technology vendors.Accuracy of information: AI tools cannot always provide accurate and impartial output. For now, we always need to have a human in the loop to apply their expertise to confirm the accuracy of AI-generated content.Student misconduct issues:&nbsp;Students may simply get AI to do the things their instructors have asked them to do. In the past, in-person exams, penalties and detection tools helped mitigate misconduct, but AI is forcing universities to rethink authentic assessment design.We’ve suggested a number of ways that AI tools can be used to promote active learning in higher education teaching. We encourage you to be cautious and judicious, but also adventurous in experimenting, innovating and collaborating with your learners to make educational experiences more engaging and inclusive.Tab Betts is a lecturer in higher education pedagogy at the University of Sussex. Shelini Surendran is an associate professor in biosciences;&nbsp;Martin Hawes is a senior lecturer in veterinary pharmacology and therapeutics; Stella Kazamia is senior lecturer in computer science and electronic engineering; Joey Sikchun Lam is a lecturer in computer science and electronic engineering, all at the University of Surrey.&nbsp;Shaun Le Boutillier is associate professor of sociology;&nbsp;Uwe Matthias Richter is associate professor and academic lead: digital pedagogic innovation; Jason Williams is media specialist, all at Anglia Ruskin University.&nbsp;Roman Moiseev is a lecturer in pharmaceutics at the University of Reading.&nbsp;Jon Pugh is senior lecturer for applied drama: education, well-being, community at the University of Wales Trinity Saint David. Geyen Sasha Surendran is lecturer in anatomy and physiology at British International University. All are part of the Active Learning Network.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Eight ways to use AI in active learning 

Use AI in active learning by deploying these eight strategies to integrate tools in your teaching. Plus, how to address four challenges when doing so

Eight ways to use AI in active learning – and four challenges it brings

Imagine you’re juggling flaming torches (or maybe just grading at 2am). Enter artificial intelligence (AI), promising to carry half those torches – and let you catch up on some sleep. AI can transform teaching and research but it also has the power to scorch everything in sight if it’s not developed and used responsibly.Why does it matter? Because:Data privacy breaches can harm studentsBiased AI systems could unfairly limit access to opportunitiesOver-reliance on AI can erode critical thinking.With great tech comes great responsibilityYou wouldn’t invite a house guest who insults the family, so why bring&nbsp;in AI tools that clash with your institution’s mission? Developing AI tools begins with aligning tools with your institution’s values. For example, if an institution values diversity, AI can facilitate holistic admissions processes&nbsp;that consider a wider range of student backgrounds, ensuring fairness in admissions and countering negative biases.Transparency is also essential. No one likes hidden ingredients in their food, and people certainly don’t&nbsp;like “black box” AI tools&nbsp;that they can’t scrutinise. Educators, students and administrators should understand how an AI system processes data and why it makes certain recommendations. This clarity helps you spot bias and course-correct early. To&nbsp;make sure AI decisions stay above board, you can publish summaries of your AI’s logic, explain how it collects and uses data and involve an ethics committee in ongoing reviews. You might&nbsp;want to look at Unesco’s AI and Education: Guidance for policy-makers for recommendations on implementing transparency and explainability of these systems.AI often sifts through vast amounts of student data,&nbsp;all of which need to be stored and managed securely, so privacy protection should also be front and centre. That means gathering only what you need, encrypting it and ensuring strict controls on who can access it and why. Finally, none of these matters if no one knows how to use AI responsibly. Investing in AI literacy for faculty, students and staff can be a game changer. Simple workshops on how AI works, how algorithmic bias creeps in and how to engage with AI ethically can foster a campus culture that benefits from technology without being blindsided&nbsp;by it.&nbsp;Spotlight guide: Bringing GenAI into the university classroomAI as tutor and critic: using tech to personalise educationPromoting ethical and responsible use of GenAI toolsPutting AI to work: teaching, learning and everything in betweenOne of the most transformative ways that AI can improve education is by personalising the learning journey. As leaders, you can support faculty and departments in adopting adaptive learning platforms that tailor lessons in real time. This real-time responsiveness can boost engagement because students feel the content addresses their needs and offers advanced challenges for high achievers and targeted help for&nbsp;those who are struggling. This frees your staff to focus on the creative and relational sides of teaching, but&nbsp;do ensure the necessary resources, training and infrastructure are in place.Additionally, AI can handle many administrative and instructional support tasks, such as answering “When is the registration deadline?” or “What time is class for course X?” queries that usually flood your faculty’s inbox. That way, teachers and teaching assistants can devote more energy to lively debate, brainstorming sessions and personalised feedback.Beyond the classroom, AI can be&nbsp;seen as a strategic investment for your campus. Tools like&nbsp;ChatGPT with deep research;&nbsp;Google’s Gemini with Deep Research;&nbsp;Perplexity and&nbsp;Consensus can automate most of the&nbsp;grunt work for literature reviews, generate summary reports, manage cross-disciplinary collaborations or map out emerging research trends. However, remind your faculty and research teams that AI-generated insights require human oversight to verify accuracy.Looking to the future – with our humanity intactHigher education is being reshaped by the promise of AI but as leaders, you&nbsp;bear the responsibility of guiding your institution towards ethical practices that protect student privacy and preserve human connections. Stay curious about emerging AI tools, demand clarity from commercial developers, maintain high standards for transparency, and build a campus culture that understands AI’s strengths and limitations.AI’s true power lies in complementing – not overshadowing – insight, empathy and creativity that only people bring. Let AI handle the mundane tasks. Let your educators focus on shaping minds and nurturing new ideas. By striking this balance, you ensure that higher education remains&nbsp;a place where technology is in service, not the other way around, and where innovation and integrity go hand in hand.&nbsp;Craig J. Ramlal is head of control systems in the department of electrical and computer engineering at the University of the West Indies, St. Augustine.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the&nbsp;THE Campus&nbsp;newsletter.

Ensure AI serves institutions, not the other way around

We’d all prefer that AI tools helped us to do research and grade papers rather than take over campuses. Here’s how to develop AI tools for your institution responsibly

How many of your colleagues are talking about artificial intelligence? Better yet, how many in careers advice and guidance are talking about how to use AI? The space between these questions might seem small, but it creates a chasm when it comes to how higher education is integrating AI to help our students transition into the workplace.Young people are overwhelmingly using AI, as are the recruiters who will decide which of our graduates find employment. If we do not respond swiftly, then students and the job market will outpace service provision in terms of AI use and expertise. Yet careers services are perfectly placed to make interventions, both by becoming experts on AI-enhanced careers advice and by training students on how better to use tools to facilitate their recruitment journeys.Here are four practical methods for careers counsellors to use AI and support their students.1. Develop a departmental strategy – then upskill staff based on thisMost university careers services do not have a specific strategy for AI use – although many institutions have adopted AI policies in areas such as assessment. The problem with this oversight is twofold. First, without a departmental strategy, even staff comfortable with AI will tend to use different platforms with inconsistent results. Students are therefore more likely to receive conflicting advice from advisers. Second, many staff simply do not have appropriate AI skills.&nbsp;Spotlight guide: Getting students workplace-readyAre you a jack of all GenAI?Resource collection: AI transformers like ChatGPT are here, so what next?These problems can be ameliorated by writing a departmental policy that outlines which tools you will focus on using, as well as how best to implement them. A strong policy should cover key uses for AI in your department, a training and development plan, ethical compliance, and a roadmap for integration into regular service.&nbsp;It’s always better to test new technologies on a small scale before adopting them wholesale. Once you have chosen the best AI-powered services for your department, you can develop bespoke training. Better yet, appoint different staff members as “experts” on certain tools and factor in time for them to develop extended practical knowledge of your chosen platforms. These staff can then be on hand to troubleshoot FAQs and support colleagues, promoting professional development and ensuring an even-handed approach to career-enhancing technology.2. Create custom chatbots as cost-effective support for your student bodyIntegrating AI into careers advice and guidance can often feel like the Wild West. As with essays, students are already using AI to write CVs and cover letters – though to varying degrees of success. At the same time, the internet is awash with tools: everything from help finding scholarly consensus to turning any article into a digestible podcast. However, most of the options on offer are expensive and still relatively new to the market, meaning we remain unsure of their long-term impact.&nbsp;So, how can we tangibly use AI to support student employability?At London South Bank University Careers Hub, we have found that the most cost-effective and high-yield solution is creating a range of custom chatbots that offer advice and guidance on a range of subjects from refining CVs to developing a LinkedIn profile or practising job applications. These chatbots are deployed for out-of-hours usage and as pre-screening tools before one-to-one appointments, allowing students to troubleshoot common mistakes before seeing an employability consultant, who can then offer deeper personalised advice. The chatbots are trained to give the answers our department wants to prioritise, ensuring that students receive relevant, institution-specific guidance. This approach allows us to maintain quality control over the advice provided while streamlining support for common queries related to CV writing, interview preparation and job-search strategies.&nbsp;3. Respond to the job marketAI is changing recruitment processes. Recruiters are already expressing exhaustion with AI-generated application material and changing the hiring process to focus on interview skills. Increasingly, recruiters are asking if the CV will become less important in years to come. As careers services, we must respond to market changes.Primarily, we should be developing careers-specific AI training sessions for students, teaching them how to best use new tools to write submissions. The mock interview is no longer enough. Instead, we must incorporate real-world examples and interactive exercises to help students navigate new AI-powered interviews. Training students how to write effective prompts and critically assess AI-generated documentation such as CVs and cover letters will empower them to use these tools responsibly. At the same time, different kinds of workplace simulations are necessary, from how to record the perfect video interview to completing AI-style questionnaires. Across many institutions, advisers have even reached out to the companies that create these AI tools to hear more about how they work, what their metrics for success are, and how advisers can support students to navigate virtual environments.4. Avoid AI griftsEducation, and edtech in particular, is particularly susceptible to false promises. From primary education right through to university, staff often have fads thrust upon them at the drop of a hat. A whole generation can tell you what "learning style" works best for them – even if there is no evidence to support this. Though alarm bells have been raised in the wider sector, careers advice and guidance is still in search of a golden goose. AI technology is changing rapidly, and we must first establish secure parameters for success before we can claim our metrics are working. What measurable output do we want to achieve through adopting AI technology? Our proposition is that new AI tools should be tethered to industry standards, most specifically graduate outcomes. To avoid edtech that over-promises and under-delivers, we need a sector-wide focus on helping students better use AI to enter graduate-level work, where they can continue to fruitfully employ new technologies.By embracing AI with a strategic, critical approach, careers services can enhance student support without falling for trends or ineffective tools. The key is to integrate AI thoughtfully, ensuring it serves both student needs and the evolving job market.Sean Richardson is the employability resources manager and Keiran Whitwell is an employability resources consultant, both at London South Bank University.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

How to make the most of AI in careers advice

Careers services are perfectly placed to use artificial intelligence to help students transition into the workplace, both by developing expertise themselves and by training students to use AI tools in their recruitment journeys

How to make the most of AI in careers advice – and avoid the grifts

GenAI is bringing both opportunities and challenges to academia. At the same time, universities are shifting towards more flexible, task-based work structures that operate in a gig economy. While typically linked to freelance industries, this model is increasingly shaping academia, altering traditional employment patterns and shifting responsibilities.&nbsp;Full-time, permanent academic positions are giving way to short-term contracts, project-based roles and part-time teaching, leaving many educators and researchers in precarious employment. AI tools are further accelerating these changes by automating key tasks, such as content creation, assessment and data analysis, raising concerns about the long-term stability of academic careers.&nbsp;What does this mean for academic jobs? Will AI replace or redefine the roles of educators and researchers? How can we adapt to this evolving landscape?A new employment modelThe rise of AI may push academia towards a more task-based employment model, where academic roles become fragmented. AI can automate tasks such as grant writing, data analysis and peer review, leading to a shift that sees universities contract staff for specific projects rather than as permanent staff. This raises concerns about job security and career progression.&nbsp;Universities must strike a balance between AI adoption and sustainable employment, ensuring academics are not pushed into precarious work.Spotlight guide: Is your academic career cleared for take-off?Pathways to prosperity in casual academiaWhat is tenure in higher education – and how to get itFuture-proofing academic careersAs academics, to navigate these changes, we must develop skills that are uniquely human, such as critical thinking, which is essential for evaluating AI-generated content, challenging assumptions and forming well-reasoned arguments. Along with that, consider building mentorship skills such as emotional intelligence.&nbsp;Developing expertise in interdisciplinary AI applications and advocating for fair AI employment policies will also be crucial, as will be honing skills in identifying knowledge gaps, coining novel methodologies and fostering interdisciplinary collaborations. Expertise in AI ethics and policy is also becoming vital to universities seeking leaders who can integrate AI responsibly.&nbsp;By enhancing these skills, we can position ourselves as indispensable contributors to an academic landscape increasingly shaped by AI.&nbsp;The future of academic rolesAs AI becomes more embedded in academia, new roles will emerge, reshaping university operations and workforce structures.&nbsp;AI integration specialists will support departments in embedding AI into research and teaching, helping educators design AI-enhanced curricula, automate grading and develop adaptive learning environments.&nbsp;Digital ethics advisors will guide universities in setting ethical policies for AI use, ensuring transparency in automated decision-making, preventing bias in admissions algorithms and maintaining academic integrity in AI-assisted research.&nbsp;AI-assisted researchers will use AI for complex data analysis, systematic reviews and predictive modelling, particularly in STEM and social sciences.&nbsp;AI-assisted student support coordinators will personalise learning support, track student progress and provide early interventions using AI-driven insights.&nbsp;AI policy and compliance officers will ensure institutions meet evolving legal and regulatory requirements for AI implementation.&nbsp;Human-AI collaboration specialists will train academics and students to effectively integrate AI into their work while maintaining human creativity and critical thinking.&nbsp;These roles will be crucial in ensuring AI enhances, rather than replaces, academic expertise.&nbsp;However, there is a risk of increasing disparities between well-resourced universities that can fully adopt AI and those that struggle to keep up. Ensuring AI accessibility and equity will be vital in maintaining fair and inclusive academic environments. Academics can prepare by staying informed about AI’s impact on their fields, advocating for AI training and engaging in policy discussions to shape AI’s role in higher education.Dr Patrice Seuwou is an associate professor of learning and teaching and the director of the Centre for the Advancement of Racial Equality at the University of Northampton.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

How will AI reshape academic employment?

The rise of AI in higher education highlights the need for academics to upskill and adapt to new roles and ways of working. Read about how to future-proof your career

How will AI reshape academic employment? 

The inclusion of generative AI (GenAI) in assessment design is an acknowledgment of its ubiquity – and how important it will be in the future workplace. But it’s also an opportunity to reimagine learning, and how we foster creativity and higher order thinking.&nbsp;Reflecting on my experience designing a digital media production module for hospitality business students, GenAI can transform assessments into authentic, future-focused applied learning experiences. Here, I’ve identified key practices that can help instructors meaningfully integrate GenAI into assessments.Redefine authentic assessmentA major insight I gained is the importance of creating assessments that prepare students for real-world challenges. In my module, I collaborated with a hospitality business client to sponsor a design brief for students, which would ideate a preferred future while addressing their key challenges. This element of authenticity allowed the students to see themselves in a future workplace, and prototype solutions for a transformation of the hospitality industry. The theme resonated deeply with them, encouraging deep reflections on their roles as current students and future industry professionals.&nbsp;AI as a catalyst for assessment innovationThe evolution of assignments in the age of generative AIAssessing the GenAI process, not the outputOne of the most rewarding aspects of the module was the collaborative energy it inspired. Students worked together, iterating on prompts and refining outputs, which led to richer, more creative outputs. The studio-like environment encouraged experimentation and provided a safe space to take risks, fail and try again – a crucial aspect of fostering innovation. Importantly, as their instructor, I collaborated with them as well. Modelling curiosity and a willingness to experiment with the tools created a shared journey of discovery between us. I showed curiosity rather than scepticism, to foster a culture of trust and shared exploration.&nbsp;By leveraging GenAI tools, the authentic assessment moved beyond routine summative tasks and allowed students to engage deeply with a human-centred approach to problem-solving and creativity. This approach aligns with Ruben Puentedura’s SAMR model, which encourages instructors to design assessments at a redefinition level – the creation of tasks that would otherwise be inconceivable. In the past, students relied on low-fidelity tools such as Post-It notes or sketches for prototyping. In my class, students envisioned transformative ideas and prototyped visuals for the client using technological tools.&nbsp;So, consider how your assessments can leverage GenAI to go beyond substitution or augmentation. You can also focus on creating tasks that challenge students to think critically and creatively, allowing them to apply their learning and design multimedia prototypes for real-world contexts.Align technology with pedagogical intentI firmly believe that the integration of GenAI tools into assessments must be grounded in clear pedagogical goals. The module aligned the use of GenAI tools with Critical Design Futures Thinking, a framework that helps students envision alternative scenarios with an instructional approach that uses Question Starters, which are deliberately open-ended prompts so students learn to ask good questions. They practise divergent and convergent thinking more deeply on multifaceted real-world issues and propose innovative solutions. This approach helped students expand their cognitive boundaries, as they engage in what I call “intellectual sparring” with the GenAI tools and among themselves.&nbsp;The Question Starters guide students in anticipating future scenarios and uncertainties. The prompts help them develop flexibility in futures thinking, as they integrate emerging trends and technologies into their projects in ways that demonstrate forward thinking and adaptability. For example, by asking “Why now?”, students became aware of the urgency for timely innovation, compelling them to unearth unseen possibilities and prepare for future shifts in their chosen domain.This accessibility helps every student believe they can be a designer, levelling the playing field in disciplines traditionally constrained by technical expertise. Drawing on Punya Mishra and Matthew J. Koehler’s TPACK framework, I ensured that technology was not just a convenient add-on, but a catalyst for intentional and meaningful applied learning. This alignment between technology, pedagogy and content helped students develop critical competence, design dexterity and futures flexibility – mindsets essential for navigating the uncertain, complex landscapes of the future workplace.&nbsp;I would recommend that you reflect on your pedagogical goals before introducing AI tools when redesigning your assessment. Ask yourself how technology can serve these objectives. Design assessments that integrate AI thoughtfully, ensuring it enhances learning outcomes rather than being a superficial addition.Embed ethical and societal awarenessThe assessment design for this module reflects the evolving needs of students and industries. Students did not just learn to use GenAI tools, they learned to question, critique and apply them thoughtfully. This experience mirrored tasks they are likely to encounter in their future workplaces, blending technical proficiency with critical thinking, and ethical and societal awareness – crucial as we empower students to thrive in an AI-enhanced world.&nbsp;As instructors, we must embrace this era of co-learning and co-creating with GenAI, creating a safe environment for experimentation while addressing implications. Our role is not to shy away from AI but to model curiosity and adaptability, ensuring we see these tools as enablers rather than threats. Assessments must remain holistic to prepare students for the ever-changing landscapes of Education 4.0 and Industry 4.0. Consider integrating ethical and societal considerations intentionally into your curriculum from the outset. Encourage students to reflect on the societal impact of their work, ensuring they learn that innovation is not just about what we can do with technology – it is about the choices we make in shaping its use.GenAI has transformed the landscape of higher education, particularly in assessment design. By integrating these tools into authentic assessments, we can prepare students to thrive in a future where AI is ubiquitous.&nbsp;This journey is as much about modelling mindsets as it is developing skills. We ought to show students that we are co-learners and co-creators in this space. By encouraging play and experimentation, and embedding ethical considerations into our teaching, we create a foundation for applied learning and innovation for life.Nadya Shaznay Patel is assistant professor in business, communication and design at Singapore Institute of Technology.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Rethink authentic assessment for the generative AI era

How can we integrate AI into authentic assessment? Consider inspiring a collaborative spirit, aligning technology with pedagogy and modelling a new mindset for your students 

Artificial intelligence (AI) is rapidly evolving from a simple memory retrieval tool into a sophisticated agent, capable of nuanced reasoning. We can see this in advanced models such as DeepSeek’s R1, OpenAI’s Deep Research and xAI’s Grok. These systems no longer act merely as extensions of search engines but serve as interactive partners that can help break down complex problems and encourage inquiry. For higher education, this shift offers a powerful opportunity to strengthen the critical thinking skills of our students, which will be essential for the workforce of the future.From memory-based retrieval to reasoningTraditional AI tools functioned as encyclopaedic repositories, quickly recalling facts – rather like using a calculator for basic arithmetic. In contrast, today’s reasoning AI models have been engineered to deconstruct complex queries into logical steps and to engage in dialogue that mirrors human reasoning.For example, DeepSeek R1 is designed to work through problems in mathematics, coding and logic by offering reasoning steps along the way, while OpenAI’s Deep Research supplements its answers with clear explanations of its thought process. xAI’s Grok 3 takes these capabilities further, by handling intricate tasks such as creating new games that combine two completely different games into one, which requires an advanced understanding of context and nuance.These new AI models can no longer be seen solely as tools for rote memorisation. Instead, they provide a medium through which students can engage in a dynamic conversation about ideas, prompting them to think critically and independently. Working with an AI that “thinks out loud” encourages students to explore the rationale behind each step and question the reasoning process. This deepens their understanding of the outcome.The importance of critical thinking for the future workforceThe most valuable skill for the next generation of workers is critical thinking. In an era characterised by information overload, the ability to analyse, evaluate and synthesise data is more important than ever. Employers are looking for graduates who can solve novel problems, adapt to rapidly changing environments and make well-reasoned decisions. While AI can quickly provide information, the human capacity for reflection and judgement ultimately drives innovation.DeepSeek and shallow moats: what does it mean for higher education?Spotlight guide: Bringing GenAI into the university classroomIs critical thinking the answer to generative AI?Higher education plays a crucial role in nurturing these skills. When students engage in deep analysis and reflection rather than simply retrieving answers, they build a foundation for lifelong learning. Critical thinking gives students the ability to assess the credibility of information, draw connections between disparate ideas and arrive at creative solutions. If we are to prepare graduates for a world where machines and humans collaborate seamlessly, managing the transition of AI from shortcuts to partners in reasoning will prove essential.Over-reliance on AI as an answer generatorDespite all the advantages AI brings, we know students might misuse it by treating it as a shortcut. When learners rely solely on AI to provide answers, they can bypass the necessary cognitive struggle that leads to deep learning. Over-reliance on AI-generated responses can lead to superficial understanding and inhibit the development of robust critical thinking skills. Even advanced AI systems can produce confidently stated yet flawed or biased answers, which students accept without scrutiny.To avoid these pitfalls, we as educators must frame AI as a tool to enhance learning, rather than a substitute for it. Focus on fostering an environment where students critically engage with AI outputs –&nbsp;evaluating, questioning and refining them –&nbsp;rather than accepting them uncritically.Integrating reasoning AI into higher educationTo harness the full potential of reasoning AI, higher education institutions should adopt several best practices:Socratic engagement: Design assignments that encourage dialogue with AI. Instead of requesting direct answers, prompt students to use AI to generate ideas, then require them to critique and build on these suggestions. This approach forces learners to actively engage with the outcome of the reasoning and articulate the reasoning behind their conclusions.Iterative learning: Leverage AI’s capacity to provide immediate feedback. For example, students can draft essays or solutions with AI assistance, and then revise their work based on the AI-generated suggestions. This process reinforces learning by emphasising the importance of revision and reflection.Prompt engineering and metacognition: Teach students to develop effective prompts for AI, a process that requires clarity and precision in their thinking. By reflecting on both the quality of their prompts and the AI responses, students enhance their metacognitive skills –&nbsp;learning not just to solve problems but understand how they solve them.Ethical and transparent use: Establish clear guidelines for AI usage that emphasise responsibility and transparency. Require that students document how they use AI in their work to ensure it remains a support tool rather than a crutch. Clear policies help maintain academic integrity while encouraging the thoughtful integration of AI into the learning process.What does this mean for the future of work?The integration of reasoning AI into higher education represents more than a technological upgrade –&nbsp;it signals a shift in how we perceive learning. Future employers will value those who not only know how to operate advanced tools but also possess the ability to think critically, analyse complex problems and adapt creatively to new challenges. In this new paradigm, AI models such as DeepSeek’s R1, OpenAI’s Deep Research and xAI’s Grok 3 become collaborative partners that enhance rather than diminish human intellect.By engaging with these AI systems, students develop a deeper understanding of complex subjects. They learn to question assumptions, evaluate evidence and generate innovative ideas – all of which are key attributes for a successful career in a technology-driven world. The use of AI as a reasoning partner encourages students to become active learners who take responsibility for their own intellectual development. Instead of passively receiving pre-packaged answers, they become participants in a dynamic learning process that mirrors the demands of the modern workplace.AI will continue its march toward more sophisticated reasoning – and higher education must adapt. The goal should be to transform AI from a mere tool for memorisation into a collaborative partner that fosters critical thinking. This requires a shift in pedagogy: moving away from simply obtaining answers and toward engaging in the process of reasoning itself. When students learn to interact with AI in a dialogic and reflective manner, they build the cognitive skills that are essential for navigating the challenges of the future workforce.In the end, the true value of AI in education will not be measured by its capacity to recall facts but by its ability to empower students to think, question and innovate.Xiangen Hu is director of the Institute for Higher Education Research and Development at Hong Kong Polytechnic University.If you’d like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Let’s look at AI as a reasoning partner, not a shortcut

The latest reasoning AI models, such as DeepSeek, offer an opportunity for universities to strengthen critical thinking in the next generation of workers

Digital humanities is the application of digital tools, methods and theory to the study of human culture and society. It’s a research methodology that can apply to many academic fields. At the most basic level, writing an essay using a word-processing program is a digital process, so you may be a digital humanist without even knowing it.&nbsp;To explain what differentiates the practice of digital humanities from traditional humanities,&nbsp;Miriam Posner, a digital humanist at the University of California, Los Angeles, created the framework of “sources, processing and presentation”. Here’s an overview of how each of those elements might apply in a classroom, followed by some helpful hints for getting started with digital humanities.&nbsp;&nbsp;&nbsp;Wide-ranging digital sourcesDigital humanities projects can use sources ranging from a collection of novels to a list of flour prices in medieval England. What differentiates them from their hard-copy precursors is that they’re in a digital format, which allows you to build a digital collection from those sources – a database, for example – and&nbsp;then interrogate it computationally. While the process of gathering data may take considerable time, taking a computational approach to asking and answering research questions empowers researchers to work with data at scale quite swiftly.Processing textsProcessing texts computationally, also known as “text mining”, may include building a dataset or text corpus, writing code to interpret it and sometimes creating a front-end experience to present your findings. Before any of that can happen, though, the texts must be clean.The process of text cleaning seeks to remove any extraneous characters you don’t want to consider in your analysis. It’s necessary because, when optical character recognition (OCR) text is generated from an original primary source, it's often riddled with misinterpreted letters and words, along with a peppering of “garbage characters”. If you’ve ever worked with books from the 1900s on the Internet Archive, you know what I mean.Text cleaning also involves compiling a list of “stop words”, which are the most common in the English language (or whatever language you’re&nbsp;doing research in) that you exclude from your text analysis. By eliminating stop words, your analysis results won’t be skewed by, for example, 12,000 instances of the word “the”, so you can instantly work with more meaningful data. This&nbsp;pre-processing of text probably takes up about 80 per cent of the digital humanities process.&nbsp;This type of work lends itself well to project-building in a classroom using digital humanities methods.&nbsp;In my classes, each week a digital humanities course involves some&nbsp;type of&nbsp;data curation or analysis, culminating in students building a digital project. This process teaches students the difference between quantitative and qualitative tools and which might be most appropriate for answering a given research question. A text-mining platform such as&nbsp;Gale Digital Scholar Lab offers digital tools to answer both those types of questions: it provides three quantitative and three qualitative tools, allowing students to experiment to see if the answers they’re getting when they run a chosen tool or tools are meaningful.&nbsp;Other platforms&nbsp;to consider include&nbsp;Voyant, which offers similar text-mining options&nbsp;to&nbsp;dig into your research data.&nbsp;Working with qualitative tools requires a great deal of interpretation. With topic modelling, for example, students must discern the connections between the individual words that make up a topic, decide why the algorithm has grouped them a certain way and then name the&nbsp;topic accordingly. This higher-level thinking enables them to understand the data that they’re working with. For students who may not have in-depth background knowledge, the digital humanities process is an effective, non-traditional way to provide a bird’s-eye view of a subject.For instance, I taught a class about the&nbsp;centenary of the discovery of King Tut’s tomb. We ran topic modelling on the digital edition of&nbsp;The Times, which yielded collections of words such as “tomb decoration”, “sarcophagus” and “mummy” that were grouped into topics. Students would then decide what to name each&nbsp;topic, based on their interpretation of the thematic connections between words. We also analysed all the different proper names and names of artefacts, from “Carter” and “Carnarvon” to “Anubis” and “canopic jar”. The varied topics students discovered gave them a snapshot&nbsp;view of areas they could explore further and eventually present to their classmates.Spotlight guide: Back to the basics of pedagogyCreating internship opportunities: showing employers the value in humanities and social sciencesCollection: Teaching critical thinkingInteractive, collaborative presentationsIn a traditional, long-form essay, students develop a research question and support a thesis through a written introduction, conclusion and four paragraphs in between. The difference in the digital humanities is where and how students share their work. Non-traditional assignments and final projects might include a story map or a timeline, exploring a research narrative and offering citations as links. It might&nbsp;include images or video. Unlike traditional essays, digital platforms&nbsp;offer the possibility of interactivity and active reader engagement.&nbsp;While essays tend to be solo endeavours, digital humanities is inherently collaborative, so it offers students the opportunity to work in groups and thereby develop&nbsp;social-emotional skills as well as practical digital literacy skills.Getting started with digital humanitiesFrom my experience teaching digital humanities, here are key dos and don’ts to help you get started:Do break work down into 20- to 30-minute intervals to allow for periods of collaboration followed by&nbsp;periods of conversation.&nbsp;Do factor in time for some of the technology to fail.&nbsp;Do encourage&nbsp;experimentation as long as there is process documentation.&nbsp;Don’t try to use too many tools or platforms at once, because you’ll spend more time troubleshooting than you do teaching.&nbsp;When you’re ready to create your first digital humanities project, I’ve found that using&nbsp;Bloom's Taxonomy as a framework is an excellent way to start teaching the basics. To answer questions digitally such as:&nbsp;“How do you log into platforms?” or:&nbsp;“How do you find X, Y and Z?” I use a treasure hunt as an activity to create a foundation of comfort within whatever tool I happen to be using. Then I progress through the levels of the taxonomy to help students gain understanding to apply what they’re learning to analyse and evaluate it, and then ultimately to create something they can present to their classmates or a wider audience.No matter what subject you teach, if you approach digital humanities as an expansive framework that offers possibilities rather than problems, I believe you’ll find that it opens new doorways for research, pedagogy and creativity.&nbsp;Sarah Ketchley is an Egyptologist and a faculty member in the department of Middle Eastern languages and cultures at the&nbsp;University of Washington. She also teaches in the humanities and philosophy department at the University of Maryland Global Campus.&nbsp;If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus&nbsp;newsletter.

What makes ‘digital humanities’ different from ‘traditional humanities’, and how could it make your classroom more collaborative and creative?

Digital humanities 101

With international student recruitment becoming increasingly competitive, universities must find new ways to engage prospective learners. However, heavy teaching workloads make it difficult for staff to travel and meet them in person. While it is possible to combine academic and student recruitment activities in international trips, it is not always feasible.&nbsp;Sending email newsletters offering useful content to prospective students is an effective way to outline the benefits of studying at your institution. So is posting regularly on social media channels. Building on this, I suggest running live streams on Facebook, LinkedIn and YouTube to connect with these students in real time.&nbsp;Expanding engagement through live streaming&nbsp;At my institution, our live Facebook Q&amp;As have been very popular. Our Facebook page has a large following, and advance email and social media promotion help attract large audiences. We’ve created series such as the “International Citizen Student Show” based on our&nbsp;Citizen Student Strategy,&nbsp;streamed on Facebook, YouTube&nbsp;and LinkedIn simultaneously using StreamYard. Co-hosted by academic and non-academic staff, students, alumni and external guests, it covers topics of interest to international students. We record these sessions and&nbsp;then post them on our social media channels.&nbsp;We have also hosted a “Micro MBA” with eight one-hour taster sessions covering different topics integrating quizzes and digital content. Topics we cover include strategy, leadership, sustainability, finance, supply chain, marketing and digital transformation.&nbsp;Make it appealing&nbsp;To maximise engagement, it’s essential to tailor content accordingly. Conduct research about your target audience and what would appeal to them to help you decide what kind of content to cover. If you want to promote a particular programme, you can use&nbsp;content from it to create some taster sessions. Offer an automated certificate as an incentive to complete the course.Inviting alumni guests to live-streamed events and ensuring the content highlights their experiences can boost word-of-mouth marketing. As graduates become active ambassadors for the university, their stories and insights can foster trust and authenticity in the recruitment process.&nbsp;Structuring your sessionsStart your live streams with a 10-minute introduction to the topic and updates about your institution, then allow an expert speaker to deliver a 15-minute presentation. The remaining 30 minutes can be set aside for a Q&amp;A.&nbsp;Five to 10 one-hour sessions work well.Encouraging interactionWe use StreamYard, which allows participants to post questions in the comments section of their chosen social media channel. The co-hosts then choose audience comments to display on the screen for the speaker to answer. This ensures that irrelevant questions are not visible to the audience. We also create a ticker tape banner with information on how to submit questions and make enquiries after the session.You can also ask participants to submit feedback in the comments section and create a short exit survey to find out what audience members found valuable and identify any areas for improvement.&nbsp;To maximise insights, consider making surveys a required component of the course.&nbsp;One participant said that&nbsp;taking part in the live-streamed course helped build their confidence, make connections and learn about the university, allowing them to make an informed decision about whether or not to apply. These kinds of comments encourage us to seek further ways of building confidence in our institution’s offering.Six ways to boost international student recruitmentHow to enhance recruitment and retention at your universityHow to support international students’ smooth transition to a new countryThe benefits of live streaming for institutionsThese sessions offer opportunities for more targeted and cost-effective efforts while maximising the benefits of working with agents. In our case, some of our agents and local businesses also participated in our Micro MBA course and it further strengthened our relationships with them.&nbsp;The integration of prospective student feedback from agent networks, student surveys and live-streamed events also allows universities to refine their recruitment strategies,&nbsp;create better personas and better understand which agents, markets and communication channels are most effective. This can lead to more strategic marketing efforts, maximising return on investment. &nbsp;Furthermore, these live events allow staff to directly interact with students in real time to build relationships, increase connections, address concerns and provide useful content, strengthening trust. This can lead to increased enrolment from individuals who feel more informed, reassured and connected to the institution after the sessions.&nbsp;By embracing live streaming as a recruitment tool, universities can build global connections, foster trust and attract a more diverse student body.&nbsp;Alex Fenton is associate dean for international and associate professor in the Faculty of Science, Business and Enterprise at the University of Chester.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the&nbsp;THE Campus&nbsp;newsletter.

Go live on social media to engage with prospective students 

Meeting prospective international students in person isn’t always possible. Here’s how to create engaging live streams on social media, from selecting valuable content to structuring sessions and encouraging interaction

The anxiety some lecturers experience around generative AI (GenAI) may stem from a traditional view that education is an intellectual endeavour that requires students to develop their own thinking and cultivate an academic voice. This process goes beyond having something to say; it is about fostering original thought and perspective. The fear, therefore, is that students’ excessive reliance on GenAI could lead to intellectual stagnation.&nbsp;Simply having an opinion on AI, however, is insufficient to address the realities of its prominence in higher education. Practical solutions are needed for those expected to engage with GenAI pedagogically, ensuring both staff and students become AI literate and can “capitalise on the opportunities technological breakthroughs provide for teaching and learning”, as&nbsp;noted by the Russell Group’s 2023 AI guidelines.So far, UK universities’ responses to the use of GenAI and large language models remain mixed. While many embrace, or even actively encourage, the educational opportunities presented by AI, narratives continue that equate its use with cheating or worry that it may conflict with academic integrity. Some universities have&nbsp;banned the use of AI technology in assessed work, classifying it as academic misconduct.&nbsp;Nevertheless, the proportion of students using GenAI tools such as ChatGPT for assessments has risen from 53 per cent in 2023-24 to 88 per cent in 2024-25, according to research published by the Higher Education Policy Institute/KortextAI.Instead of bans, we should focus on rethinking assessment strategies to integrate AI as a collaborative tool in academic work.Spotlight guide: Bringing GenAI into the university classroomResource collection: AI transformers like ChatGPT are here, so what next?Beyond the black box: how DeepSeek could transform higher educationWith this in mind, we have compiled a list of activities that we have successfully used in our lectures that leverage GenAI to support teaching and learning. These activities are grounded in the constructivist approach to education, which emphasises collaborative knowledge construction. In this context, AI serves as a mediator, tool, tutor or a more knowledgeable other to facilitate the co-construction of knowledge. These four key categories of AI-enhanced learning and teaching show how AI can be used not just as a content generator, but as a tool to draw out ideas and insights from students.AI-supported analysis and critical thinkingIncorporating AI into these three analysis and critical-thinking activities allows students to engage with complex topics in a structured manner.&nbsp;AI transcribes a class discussion and identifies key themes; students rank them in order of importance to the topic and use them to develop their essays.Students analyse AI-generated case studies (which can be pre-programmed with intentional flaws) to identify ethical concerns and problem-solving strategies.Students compare AI-generated information with academic sources, identifying biases, misinformation and credibility gaps.AI-assisted writing and academic skills developmentThese activities can support students in refining their academic writing, structuring arguments and improving coherence through AI-generated exemplars and structured feedback.AI generates multiple versions of good, bad and ugly examples of academic writing (such as abstracts); students identify writing aspects and group versions based on common features.Students use AI-guided prompts to receive targeted feedback on academic writing, experimenting with different focus areas to refine their work and enhance self-assessment skills.Students use AI to create structured task lists for dissertations/essays, breaking them down into smaller activities with downloadable checklists.AI for creative and interactive learningThese activities engage students through interactive tasks, such as AI-generated escape rooms or AI-driven role-playing.&nbsp;Students participate in AI-driven simulations and role-play activities, allowing them to develop professional skills and apply theoretical knowledge in practical scenarios.&nbsp;Students engage with AI-powered tools such as Padlet, Google Docs or Trello to participate in discussions via blog-style posts, offering diverse ways to interact and share ideas.AI generates an “escape room” activity using a given text as a source, incorporating tasks like word fill-ins, definition matching, code cracking and data analysis.AI for fostering inclusion&nbsp;Addressing diverse learning needs, supporting accessibility and personalising learning experiences is a core part of these activities.Students access AI-generated podcasts, transcripts and alternative content formats to support diverse learning needs and enhance accessibility.Students use AI for text-to-speech and speech-to-text conversion, helping those with dyslexia, visual impairments or processing difficulties engage with content in their preferred mode.Students use AI for real-time translation, translating key academic concepts between their native language and English.We should move away from viewing GenAI as a tool for generating ready-made answers, and instead reframe it as a resource that helps draw out ideas and insights from students. In this way, GenAI can be seen to develop the skills educators have long aimed to nurture: critical thinking, creativity and independent thought. So, we must empower, amplify and encourage student voice through GenAI, rather than sleepwalking into a “tech knows best” approach.Rebecca Mace is a senior lecturer at the University of Worcester. Viktoria Magne is an associate professor; Sarah Hooper is a lecturer; and Sharon Vince is a senior lecturer, all at the University of West London.If you’d like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

How to avoid a ‘tech knows best’ approach to teaching

As GenAI is used by almost nine in 10 students, these practical strategies help educators to rethink assessment and integrate AI as a collaborative tool in academic work

‘We shouldn’t sleepwalk into a “tech knows best” approach to university teaching’

As important as accreditation is as a mark of quality assurance, for academic excellence and institutional credibility, the intricate processes involved – ranging from achieving compliance and faculty engagement, through to data management and strategic programme oversight – can be daunting. Very often, there is a disconnect between teams that manage accreditation, and the faculty and students in higher education institutions.Enter generative artificial intelligence, which is revolutionising how business schools can correctly approach accreditation in an ethical manner.&nbsp;An AI toolkit for all aspects of academic lifeHow to integrate AI into strategy and business educationFour objectives to guide artificial intelligence’s impact on higher educationHere, follow our top tips on how institutions can harness GenAI to help simplify their accreditation operations, make communications more accessible and enhance overall informational quality.1.&nbsp;Streamlining accreditation processesAccreditation demands meticulous documentation, data synthesis and alignment with established standards. GenAI can automate the drafting of comprehensive reports by integrating data from various sources, ensuring consistency with accreditation criteria. For example, AI-powered chatbots can be used as tools to streamline complex accreditation tasks linked to reporting and assessment.2.&nbsp;Enhanced visual communicationThe visual representation of data can play a pivotal role in conveying complex information succinctly. GenAI can be used to craft compelling visuals, such as infographics summarising assurance of learning results, or competency maps that detail programme-specific learning outcomes. These visual tools make data more accessible, and can improve stakeholder engagement during accreditation reviews.3.&nbsp;Content delivery innovationBeyond text and visuals, GenAI further extends its capabilities to video and speech applications. By deploying text-to-video tools, business schools can produce engaging content that clarifies the assurance of learning process, condenses strategic plans or highlights programme competencies. AI-generated narrations can also transform traditional material into dynamic and interactive experiences, boosting faculty and student learning.4.&nbsp;Data management and insightsEffective data management is crucial for informed decision-making. GenAI can develop automated key performance indicator dashboards, offering real-time tracking of essential accreditation metrics, including faculty qualifications, student outcomes and assurance of learning cycle progress.&nbsp;These dashboards minimise manual data aggregation and present actionable insights at a glance. Additionally, AI-powered notifications can be programmed to send reminders or updates based on accreditation milestones, for efficient communication with faculty and staff.5.&nbsp;Immersive 3D experiencesGenAI’s capabilities extend to creating immersive 3D applications – like virtual campus tours, which provide accreditation reviewers or prospective students with an engaging way to explore facilities remotely. 3D exploration of classrooms, labs and other campus features remotely are an excellent method of encouraging in-person visits.6.&nbsp;Fostering innovation and engagementGenAI opens avenues for creative solutions that transcend traditional methods. For example, gamifying assurance of learning processes can make faculty and student participation more interactive and enjoyable, while still fulfilling compliance requirements. AI-generated audio explanations can also present accreditation standards in a more relatable and less intimidating manner.We’ve put a number of these approaches in place, integrating GenAI tools with our School of Business Administration’s accreditation efforts. Successful examples include:Automated faculty CV analysis: A custom-trained GPT-4 agent evaluates faculty CVs against our academic and professional qualification criteria, streamlining the assessment process.Programme-specific informational videos: AI-generated videos summarise learning objectives and competencies for various programmes. These videos are displayed throughout our campus and intranet to raise awareness among internal stakeholders.AI-assisted meeting documentation: AI tools automatically transcribe faculty meetings, ensuring accurate record-keeping and the easy retrieval of information.Gamification of accreditation concepts: Interactive exercises during faculty meetings gamify accreditation-related ideas, fostering engagement and deeper understanding.Infographic and chart generation: AI generates infographics and charts from accreditation data to enhance the clarity and impact of our reports.Key considerations for AI implementationWhile the benefits of GenAI are substantial, institutions should approach its implementation thoughtfully and with a level of caution, considering:Data privacy: Implement robust data protection measures to safeguard student and faculty information.Human oversight: Use AI to augment human expertise, not replace it. Human judgement remains an essential factor in interpreting AI-generated insights.Revisit ethical implications: Remain vigilant about potential biases in AI algorithms, and strive to ensure fair treatment of all students and staff.Our experience is that GenAI offers the power to transform accreditation processes in business schools and universities. By embracing it, institutions can streamline their accreditation tasks, democratise the process by embedding quality assurance throughout all operations and bridge the communication gaps between the various stakeholders involved in accreditation efforts.This integration ultimately allows universities to better communicate their achievements and goals to both internal and external stakeholders, while reducing manual effort to favour more impactful areas. This empowers staff to meet accreditation demands with confidence and efficiency, ensuring academic quality and fostering continuous improvement.Yassine Benrqya is professor of logistics and supply chain management and Lakshmi Goel is dean of the School of Business Administration, both at Al Akhawayn University. Isabelle Fagnot is associate dean of quality and accreditation at Kedge Business School.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Six ways to integrate AI for business school accreditation

Streamline the process of accreditation, create multi-modal communications and effectively manage data by harnessing the power of generative AI

Learning challenges often arise from a lack of self-direction, engagement and motivation. To address these, we integrated an AI-powered learning platform into our Moodle-based course. The centrepiece of this platform is a sophisticated chatbot named Smart Agent, designed to foster self-directed learning and offer personalised support in listening, reading, writing and speaking.Strategies for implementation:1. Creating short-term goalsWe restructured our curriculum by breaking down learning outcomes and tasks into achievable short-term goals. We displayed these prominently on our online learning platform to provide students with a clear road map for their learning journey.For example, the learning outcome of one listening task was to summarise a short video. To support students, we divided the task into seven incremental steps:Listen for missing wordsOutline the structureTake notesWrite a short summaryAsk Smart Agent to summarise the video content using the transcriptAsk Smart Agent to compare both summariesAsk Smart Agent for suggestions on what to focus on based on eachWe provided specific guidance to students who missed words in the listening task, directing them to relevant parts of the video (e.g., "You missed the fourth word; please watch the video from 2:14–2:29 again for the answer"). This targeted approach helps students focus on areas needing improvement.Additionally, we developed structured templates to guide students in writing summaries. These templates helped students organise their thoughts and include all necessary elements.Since students often struggle to phrase effective AI queries, we created a question bank containing well-crafted questions that students can use or adapt when engaging with the Smart Agent chatbot. This approach helps students learn how to ask questions that elicit useful responses from AI, enhancing their ability to extract relevant information and guidance.To keep students motivated, we implemented a visual progress tracker on the learning platform, which included scores for each step, checklists for tasks and badges for completed stages. This helped students monitor their progress.Spotlight guide: Bringing GenAI into the university classroomAn insider’s guide to how students use GenAI toolsCan AI offer everyone a personal tutor 24/7?2. Embedding an AI Chatbot into the learning platformWe integrated Smart Agent into our Moodle platform through the following steps:a) Selecting an appropriate AI chatbot framework: We researched and selected a chatbot solution compatible with Moodle, such as Dialogflow by Google or MoodleBot, which are specifically designed for Moodle integration.b) Creating a comprehensive dataset: We compiled a database of course materials, including lecture transcripts, assignment instructions and key concepts to train the chatbot. We also developed a list of frequently asked questions and appropriate responses to ensure the chatbot could provide relevant information.c) Rigorous testing: We conducted extensive testing to ensure the chatbot functioned effectively and provided a positive user experience.Tips for implementation:a) Start with a small-scale pilot to identify and resolve technical issues before full implementation.b) Regularly update the chatbot’s dataset based on student interactions and feedback to improve its accuracy and relevance.3. Implementing biweekly reflection sessionsTo complement the AI-driven support, we introduced biweekly reflection sessions to encourage metacognitive thinking and self-assessment. We scheduled 30-minute reflection slots every two weeks and provided students with prompts to guide their reflections, such as:“What were your key achievements in the past two weeks?”“What challenges did you face, and how did you address them?”“What are your goals for the next two weeks?”Students wrote 100-word reflections and pasted them into Smart Agent for analysis. The chatbot provided personalised suggestions for improvement. Based on the chatbot’s feedback, students set goals for the coming two weeks and tracked their progress using the visual progress tracker.Tips for meaningful reflectionCarefully analyse student reflections to identify common themes, questions or areas of difficulty and update the AI’s dataset using relevant keywords and phrases. For example, if many students mention they struggle with paraphrasing, you could add or enhance content related to paraphrasing techniques in the AI’s knowledge base. This way, when students use terms such as “paraphrase”, “rephrase” or “reword” in their interactions with the chatbot, it can provide specific, tailored responses to scaffold their learning.By continuously updating the AI system with relevant keywords from student reflections, we ensure that the Smart Agent remains responsive to students’ evolving needs and challenges.By integrating an AI-powered chatbot into our Moodle-based course, we successfully enhanced students’ self-directed learning behaviours. The combination of short-term achievable goals, personalised AI support and biweekly reflection sessions provided students with the tools and motivation they needed to succeed. This approach enhances engagement, improves learning outcomes and equips students with valuable skills for lifelong learning.Ying Shao and Xue Yao are associate language lecturers in Xi’an Jiaotong-Liverpool University’s School of Languages.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Using AI to foster self-directed learning

You can promote self-directed learning by breaking down tasks and integrating an AI chatbot into your course management system. Here’s how

Providing practical and personalised feedback is vital to promoting meaningful learning. However, it can be a challenge when students are only focused on grades and do not use the feedback to improve their work. Tools like Canva and ChatGPT can make feedback more accessible and engaging. These platforms allow comments to be converted into more visual and interactive formats, helping students reflect on feedback.Canva for interactive visual feedbackCanva is an intuitive design platform that allows teachers to create visual feedback in various formats. Its extensive library includes graphic resources, templates, fonts, videos, audio and it has recently integrated artificial intelligence (AI) into its&nbsp;Magic Studio section, which expands on creation and customisation potential. You can use it to generate images and transform text to speech to make feedback visually interesting. Here are the ways I use it to deliver feedback:Custom videos: combine feedback with animated backgrounds and voice narrationInfographics: summarise strengths, areas for improvement, practical tips and additional resources in a schematic formatDynamic presentations: use charts, diagrams and graphics to explain key pointsDigital posters and flashcards: integrate personalised feedback in a dynamic formatGamified questionnaires: create interactive features that provide instant feedbackComics: create stories using images and text.I enjoy using infographics to deliver feedback because, instead of overwhelming students with lengthy comments, I can summarise my comments in a more visually engaging way to provide more focused, precise and structured guidance. In addition, using images and colours makes it more engaging and helps students better understand and remember key points. This makes them more likely to apply the feedback to their future work.&nbsp;It is also very easy to share Canva materials using a&nbsp;public link. You can also download the files in various formats such as PNG, JPG or PDF.Spotlight guide: Bringing GenAI into the university classroomAn insider’s guide to how students use GenAI toolsIs critical thinking the answer to generative AI?ChatGPT for instant and personalised supportChatGPT allows teachers to create customised virtual assistants, known as GPTs, to support students’ learning needs. No programming skills are required; teachers just need to define the task, interaction style and any limitations. You can create custom GPTs in the paid version of ChatGPT through the “Explore GPTs” section.ChatGPT can also act as a specialist in a subject or even as a historical figure, enhancing student engagement and understanding. ChatGPT can enhance learning in several ways, including:Draft reviewers: providing feedback on grammar, formatting and clarityTest preparation: generating key questions based on class materialLanguage practice: supporting grammar, vocabulary and pronunciationExplanation of complex concepts: breaking down complex topics into simpler terms.In class, we use a custom GPT to help students review their homework drafts. To create this GPT, we crafted a structured prompt, specifying its role, tone and scope. The prompt included activity instructions, the evaluation rubric, relevant academic references and response style guidelines. This set-up ensured constructive feedback that was aligned with learning objectives, helping students refine their work, improve arguments and make more informed decisions. The impact has been remarkable: students produce higher-quality work, ask more specific questions and develop greater independence.Teachers without access to custom GPTs can still benefit from ChatGPT by designing detailed prompts to guide students in assignments, discussions and study sessions. They can also explore GPTs created by other users, which are available in both free and paid accounts. These assistants offer tools for academic writing, scientific research and prompt design, as well as subject-specific aids from organisations like Khan Academy, which supports learning in maths, science and the humanities. Accessing these tools is simple: teachers can visit the “Explore GPTs” section in the ChatGPT platform, search for relevant assistants and integrate them into their teaching practices to enrich the learning experience.Transforming feedback and learningTools like Canva and ChatGPT are changing how teachers provide feedback and support students. Canva allows you to create visual and personalised feedback that encourages reflection and more meaningful learning. Meanwhile, customised GPTs provide instant and personalised support, helping students to become more autonomous and knowledgeable.&nbsp;Mariana Elizondo and Eunice Costilla are digital education professors at Monterrey Institute of Technology.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Canva and ChatGPT for feedback and academic support

Don’t bore your students with lengthy, written feedback. Here are ways to make it more visually appealing and how to use custom GPTs to offer extra academic support

It started with&nbsp;a simple set-up: a mic, a camera and a sign that read: “Tell us something good that happened to you today.”“I woke up on my first alarm.”“I had a really productive morning.”“I got an internship offer today.”This style of “standing mic” campus interviews, where students can share their thoughts in an unfiltered, unscripted way, came out of a goal to elevate the quality of our university social media content in both composition and sentiment. When we created a video around Thanksgiving last year, asking students to share what they were thankful for, and shared it across platforms, the response was overwhelmingly positive. The honesty and authenticity of students voluntarily approaching the camera and sharing their gratitude made the video a success.&nbsp;Inspired by this, we continued exploring this style.&nbsp;In February, we set up a mirrorless camera and microphone in front of Wilson Library, a central landmark on campus, for about two hours. A sign next to the microphone displayed the prompt: “Tell us something good that happened to you today for a chance to be featured on UNC’s social media.” This allowed students to approach the camera with minimal intervention, while further guidance was provided if they had any questions. We received more than 20 responses, selecting the best ones based on length and audio quality to keep the final video within 90 seconds. While most participants were students, one faculty member also added her perspective to the mix, citing her excitement to teach her upcoming lesson on The Sound and the Fury.Spotlight guide: How to make social media an academic friend not foeWhy universities in emerging countries should invest in social media marketingWe should be using social media more to understand studentsWhat followed exceeded expectations. Just days after posting, the&nbsp;Instagram reel had reached more than 4 million people (and counting), with viewers answering the prompt in the comments section. As of 1 March 2025, the video has had more than 5 million views, 650,000 engagements and 3,900 comments. The video has resonated far beyond the Carolina community.&nbsp;Former Today Show news anchor Hoda Kotb reposted it to her Instagram feed. The Washington Post wrote an Inspired Life piece on it. A local news station, WRAL, inspired by the concept, posed the same question to their viewers, further amplifying the message of positivity from the Carolina community.The post’s success wasn’t because of an overly curated message but in capturing the real, everyday joys of student life.Simplicity stands out in a blur of social media contentWe set out to create fun and impactful content that spreads positivity and resonates with our viewers. We were looking to remind people that happiness doesn’t have to be this big, life-changing thing but can be found in any typical Carolina day. Like the idea and the set-up, the post itself was minimal: no quick edits or trends, just genuine, voluntary, unscripted responses from 17 students, offering moments of vulnerability and joy. This positivity was not manufactured or forced; it happened naturally, with minimal solicitation. The authenticity of the answers drove engagement, with comments reflecting similar emotions and experiences, while the simplicity of the standing microphone allows viewers to focus solely on the students and their answers.Understanding the video’s success on social mediaWhy did this work so well? The power of one straightforward question should not be underestimated. In a fast-scrolling digital world, simplicity stands out. The goal was to give the Carolina community a chance to share their voices in the most authentic way possible, so they felt seen and heard on our platforms. Throughout the production process, the focus remained on amplifying these voices and ensuring the message of positivity resonated with viewers.Authenticity and student representation were key to the video’s success. The heart of the story lies with the students, and it grew from there. By centring Carolina voices, the video provided an opportunity for students – as well as alumni, family members and the broader community – to see an uplifting, relatable campus environment. It reinforced a sense of belonging and nostalgia while welcoming and engaging those who have never been to Carolina.Beyond its virality, the video created a ripple effect. The comment section engaged viewers emotionally; some asked for more, hoping for a recurring series, tagging other universities to ask for their version, while others shared their own stories.&nbsp;Lessons for social media in higher educationCreating “positivity” content in higher education is a fine balance. It’s not just about feel-good moments; it’s about fostering genuine connection to build trust. Connecting with students through honesty versus generic well-being messaging also helped amplify the message of the video. Universities should embrace simplicity, student voices and authenticity to engage their audiences, making social media a vital tool for communication and trust between institutions and audience members.What makes this video so special isn’t just the number of views or likes it gets but how relatable it is. Its viral success highlights that social media is more than a platform for announcements or a way to highlight the best-of-the-best moments. It can be a powerful tool for building community, fostering positivity and creating emotional impact. And sometimes it’s just about noticing the good in the ordinary.&nbsp;Andrew Lam is social media content producer and Riley Phillips is director of social media, both at the University of North Carolina at Chapel Hill.If you’d like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

The power of positive messaging in university social media

The viral success of a social media post offers insights on how universities can use simple, authentic and emotionally resonant content to drive engagement and strengthen community

The power of positivity: how a simple question went viral

DeepSeek, developed by a Chinese tech company, has recently taken the AI community by storm. This AI model showcases&nbsp;exceptional reasoning capabilities at a fraction of the usual training costs, potentially reshaping the higher education landscape. The implications of DeepSeek’s features for universities, educators and students are profound, offering both opportunities and new challenges. Here, I will explore how DeepSeek is impacting education through its transparent reasoning process and cost-effective development model. We will examine its potential benefits for educators and students alike, while discussing future directions for integrating AI tools such as these into educational workflows.DeepSeek’s reasoning processThe transparency in DeepSeek’s reasoning process, as demonstrated in tasks such as computing 24 out of 5, 5, 5, and 1, offers a significant shift from the often&nbsp;criticised “black box” nature of AI. By detailing each arithmetic step, while describing the process of considering and testing various approaches to a problem, DeepSeek acts much like a human tutor. This facilitates a deeper learning experience, allowing users to see beyond mere calculations to understand the thought process behind problem-solving. This transparency strengthens trust in AI’s capabilities while enabling constructive feedback on its reasoning methods, fostering improvements and adaptations in AI behaviour.For educators, this comprehensive insight into AI’s problem-solving approach goes beyond traditional teaching methods. It allows them to deepen their understanding of the nature of the tasks and the critical thinking involved. This is particularly beneficial in designing a curriculum because it tests knowledge and, at the same time, emphasises the development of problem-solving skills. By observing how AI navigates different solutions, educators can better guide students to explore various approaches to challenges, enriching their learning experience and promoting more profound engagement with the subject matter.DeepSeek and shallow moats: what does it mean for higher education?ChatGPT and generative AI: 25 applications in teaching and assessmentHow can we teach AI literacy skills?From an assessment perspective, this level of transparency can significantly hamper&nbsp;students’ attempts to misuse AI in order to cheat. By emphasising the problem-solving process instead of simply delivering answers, DeepSeek encourages more authentic engagement with learning material. This focus on understanding and applying logical steps dissuades students from using AI merely as a shortcut to solutions. It promotes genuine learning and comprehension. Consequently, educators can leverage this feature to develop assessment methods that require students to demonstrate their thought processes, and foster a learning environment that prioritises analytical skills and educational integrity.Reduce costs and expanding accessDeepSeek’s development at a lower cost than similar technology challenges the view that high-quality AI requires vast computational resources. Advanced tools such as DeepSeek have the potential to extend their reach beyond affluent educational sectors, becoming accessible to a broader audience. In contrast to expensive AI solutions such as OpenAI’s DeepResearch, which demands a subscription fee of&nbsp;USD $200 (£158) per month, DeepSeek is a financially viable alternative, crucial for students and teachers in the developing world, where even a monthly fee of USD $10 to $20 (£8 to £16) can be prohibitive.DeepSeek’s global accessibility, powered by its open-source model, provides a competitive alternative to restricted AI tools such as OpenAI’s ChatGPT, which operates on a closed-source framework and has excluded countries such as China and Russia from accessing its services. While DeepSeek has been banned from offering AI services in countries or regions such as Italy, Taiwan and Australia, thanks to its open-source nature, third-party developers can still build on DeepSeek’s source codes to provide fine-tuned large language models or AI applications. In this way, open-source AI like DeepSeek acts as an antidote to geopolitical turmoil and protectionism, promoting inclusivity and technological equity by empowering global communities to overcome barriers imposed by closed systems and restrictive policies.Improve functionality through APIs and overcoming barriersThe web browser and keyboard interface is the most common way to interact with generative AI chatbots but the linear, text-based interactions may not fully leverage the tool’s capabilities in complex tasks. However, the use of&nbsp;application programming interfaces (APIs)&nbsp;could be game-changing for the functionality and relevance of AI in education. Instead of starting from scratch, APIs allow developers to access DeepSeek’s functionalities and create tailored solutions that align with specific educational frameworks and cultural contexts. This customisation meets the diverse needs of global educational communities and supports various learning and teaching styles. Researchers and teachers may also leverage APIs to process large amounts of qualitative data and teaching materials more efficiently, without repetitive copy-pasting.Despite the potential of APIs, developers often face financial barriers that can limit their effective use of AI technologies, such as licensing fees and the costs of accessing cloud services where AI models run. DeepSeek’s open-source model counters these challenges by allowing developers to access and adapt the model’s source code directly. This reduces costs associated with AI development and makes its educational applications more sophisticated. Developers can modify the AI’s algorithms and training data to better suit local needs, which can then lead to further training of the model on specific educational content or teaching methodologies. This adaptability is beneficial in regions where resources are limited and in educational settings where AI’s knowledge about local context is crucial.By leveraging DeepSeek’s open-source model and strategic use of APIs, educational institutions and developers can overcome many of the barriers we currently face, fostering a more inclusive and innovative educational landscape. This approach can democratise access to advanced AI tools and encourage local adaptation and innovation. It’s a pivotal development in the field of educational technology.Practical advice for academiaCustomise for innovation: Universities should encourage and support their IT and educational technology departments to explore and experiment with DeepSeek’s open-source capabilities. Customising AI tools to fit specific academic programmes or research initiatives can lead to more effective and engaging educational experiences.Capacity-building and API strategy development: Invest in workshops and training sessions for faculty and technical staff to learn about AI integration and API usage. This knowledge will empower them to design and implement AI-driven solutions independently. Universities should develop API strategies at institutional levels that&nbsp;involve API lifecycle management, API security, educational alignment and measuring educational value, as well as AI-empowered developer enablement.&nbsp;Learning from deep reasoning: Encourage teachers and students to engage with DeepSeek’s transparent reasoning process as a dynamic learning tool. By revealing the inner workings of its problem-solving approach, DeepSeek offers educators a chance to adopt techniques inspired by the machine learning community, where unpacking complex algorithms fosters innovation. Teachers can use this transparency to model critical thinking and adaptability, sparking richer classroom discussions and encouraging students to value deep understanding over surface-level answers. This approach cultivates a culture of analytical rigour and intellectual curiosity, bridging insights from AI development and transformative pedagogical practices.The advent of DeepSeek has the potential to transform the educational landscape by decentralising the concentration of advanced AI technologies. Its low cost and open-source attributes challenge the dominance of major AI suppliers, fostering a more inclusive and diversified technological ecosystem. This shift makes cutting-edge AI tools more accessible to a broader audience, and encourages innovation and customisation through local developer engagement. The strategic use of APIs further enhances this effect, allowing for tailored solutions that meet the unique demands of various educational settings around the world. As a result, DeepSeek democratises educational technology, while powering a global movement towards more equitable and effective learning environments.Acknowledgement: This resource was written with the assistance of DeepSeek, Perplexity, GPT-4-turbo, Grok-3 and Kimi. Simon Wang is lecturer and innovation officer in the Language Centre at Hong Kong Baptist University.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Beyond the black box: how DeepSeek could transform HE

DeepSeek’s transparent reasoning process, accessibility, affordability and open-source model could have significant implications for higher education. Simon Wang explains how

Beyond the black box: how DeepSeek could transform higher education 

As artificial intelligence (AI) continues to reshape the accounting industry, students increasingly worry about their careers. Many fear that AI could replace human workers. As educators, we must help them understand its likely role in their future workplaces.Understanding AI’s role in accountingAI can automate routine accounting tasks, enhance data analysis and improve decision-making processes. It is reshaping the profession by enabling more efficient data processing and providing deeper financial insights.&nbsp;A practice-led approach can ease anxiety and provide a realistic understanding of AI as a tool that enhances, rather than replaces, human capabilities. By engaging in discipline-specific, hands-on learning opportunities via questionnaires, interviews, reflection, critical debate and coursework, students can gain insights into AI’s applications in the workplace.&nbsp;Spotlight guide: Getting students workplace-readyEmbedding sustainability accounting into the curriculumTried and tested ways to teach your students soft skillsWe took students to accounting firms to interview professionals about how AI is impacting the industry. These visits were open to students who expressed interest and 13 volunteered. Each interview involved between three and five students.&nbsp;The planning, execution and post-activity involved:&nbsp;Designing interview questions and identifying discussion topics: we designed questions to educate our students on how digital tools have impacted auditing processes. They included:&nbsp;“Do you use the following digital tools: blockchain, machine learning, data mining, natural language processing or AI in your daily work?”&nbsp;“How do you feel the above tools suit each audit phase: client investigation, risk assessment, control testing, substantive testing and analytical procedures?These questions not only align with audit learning outcomes but also clear students’ doubts.Interviewing accounting professionals in their workplaces: students interviewed nine partners at nine accounting firms of different sizes to gain a comprehensive view of AI applications across the industry.Setting expectations: we communicated our area of focus to the firm representatives beforehand to facilitate the smooth recruitment of interviewees.Prioritising students’ safety: two faculty members and an administrator oversaw and supported each visit.Designing post-visit tasks: reflective practices encourage students to critically analyse their experiences and apply insights to future situations, which is integral to experiential learning. We asked students to summarise the key points from the interviews and faculty members to write a 10-20 page report featuring all of the interview content. We gave students who were unable to participate a copy of the report.&nbsp;We also organised debates on the impact of AI on the accounting industry based on insights gained from the visits. Reading the report beforehand helped students to explore multiple viewpoints and strengthen their critical thinking skills. Discussion prompts included:&nbsp;“During which auditing phase would it be better to use AI?”&nbsp;“What are the risks of auditors using AI in their daily work?”&nbsp;Finally, we developed written evaluation coursework for students to complete based on their visits. This can be broken up by sub-questions that encourage students to delve into more detail, deepening their understanding of how AI is being used in the workplace while at the same time alleviating career anxiety caused by such technological advancements.&nbsp;Questions to consider include:How does AI application in audit work impact audit efficiency and cost?&nbsp;What ethical and audit risks are associated with AI applications?&nbsp;What AI skills do future auditors need?&nbsp;What is the outlook for AI use in auditing?&nbsp;Grounding education in real-world experiences helps students build both practical skills and a nuanced understanding of technology’s role in their future careers. By embracing this practice-led approach, accounting educators can give students the tools and confidence to succeed in an evolving field, equipping them to meet future challenges with resilience and adaptability.&nbsp;While this project focused on accounting, the approach can be applied across various disciplines facing technological disruption to teach students an important lesson: AI augments human capabilities, rather than replaces them.&nbsp;&nbsp;&nbsp;Peng Cheng is&nbsp;a senior associate professor and head of the department of accounting; Zhiying Huang is a senior teaching fellow at International Business School Suzhou (IBSS) and a fellow of CPA Australia; Yue Jiang is a senior teaching fellow at IBSS and director of postgraduate studies; Shi-Min How is an associate professor at School of Management &amp; Business, MILA University, Malaysia.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Reassure accounting students that AI won’t take their jobs

Hearing first-hand from industry professionals about AI’s impact on workplaces can alleviate career anxiety. Here is how to organise firm visits and prompt post-activity reflection 

How we reassure accounting students that AI won’t take their jobs

It was the limitations of existing tools that inspired us. We wanted an app that would allow students to receive immediate feedback as they brainstormed ideas throughout the writing process. We envisioned a tool that would encourage self-evaluation and, where appropriate, draw on GenAI to help students develop ideas.Back in 2022, colleagues from the&nbsp;Language and Communication Centre were chatting about their experiments with the tool Mentimetre and its then-new asynchronous feature that could offer students set explanations based on responses to questions. We created the app Waai the following year, with the aid of an EdeX teaching and learning grant, and working collaboratively with student assistants.

  
      
  
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The landing page for writing assistant app Waai. Courtesy of Nanyang Technological University.

Finding our target audienceWe decided that the students who most needed such an app would be the wide range taking a general writing class that is compulsory for all Nanyang Technological University (NTU) students. This course begins with students closely observing a lived experience, analysing their observations to formulate a question and then engaging with sources to develop a compelling argument that sheds new light on their topic. This design, which roots student writing in experience and enquiry, has become even more relevant with the pervasiveness of GenAI.&nbsp;Before we began designing the app, we surveyed about 240 students from the writing class. This survey showed that students would be excited to try an app that could assist them throughout the researching and writing process. Yet they were also worried about losing their voices if they relied on the seeming convenience of large language models.Pedagogical frameworkThe course is premised on the idea that writing is a tool for thinking, so students follow scaffolded steps that take them from fieldwork to an exploratory argumentative final essay. We strive to help each student discover their argument through the messy process of researching, drafting and revising.We designed the sequence of pages in Waai to reflect the order in which the scaffolded assignments are tackled. Students are guided from the initial stage of making observations all the way to reflecting on their overall argument. The flow chart (below) shows how the stages in Waai complement the course curriculum:

  
      
  
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Suggested ways to integrate the AI writing assistant app into course units. Courtesy of Nanyang Technological University.

In addition, design thinking was adapted as a non-linear framework for our app to make the writing process more relatable to STEM students, who might enter the course with this prior knowledge. The objective of the iterative process is for students to test out concepts to arrive at a possible new insight for their chosen topic.

  
      
  
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The Waai roadmap uses non-linear design thinking. Courtesy of Nanyang Technological University.

To emphasise the non-linear aspect of the writing and research process, a side panel or menu allows students to move forward and backwards while brainstorming.&nbsp;Incorporating AI into the app&nbsp;Based on the coursebook, pre-designed prompts were created to mediate students’ engagement with AI. We paid particular attention to the kinds of information students could receive through the app to avoid limiting or overly influencing their ability to generate their own ideas. When designing the app’s dropdown menu of prompts, establishing backend prompts was equally instrumental to the success of the AI tool. Our app developer, Yajat Gulati, a Year 3 student from the College of Computing and Data Science, was central to coming up with the initial backend prompts through, perhaps ironically, the help of ChatGPT.Spotlight guide: Bringing GenAI into the university classroomResource collection: AI transformers like ChatGPT are here, so what next?Campus podcast: The pros and cons of AI in higher educationThe AI chatbot named Nudgy was designed from scratch by our user interface and user experience designers, Putri Azra Besim Kukuljac and Alicia Ng Ying Xuan, Year 3 students from the School of Art, Design and Media. Its dropdown menu allows users to accumulate the “chats” they have with the chatbot, based on the prompts they choose on the respective pages. The chatbot has a “save” function that stores all information provided by users, from their areas of interest to their source summaries, and feeds this information to ChatGPT-4 with the help of a system prompt.Prompt engineeringOur goal was to avoid having the GenAI chatbot evaluate a student’s writing in a way that might contradict the teacher’s assessment. To that end, we presented students with front-end prompts that focused on helping them go deeper into their observations and analysis, such as: “Help me uncover useful definitions of concepts relating to my AEIOU observations”; “Identify any patterns or trends in my observations that I might have missed”; “Give me phrases that can emphasise the five senses from the AEIOU observations”. These prompts are found in the Introduce Op-Ed page, which includes pre-designed prompts that address both explicit and implicit observations based on students’ fieldwork experiences using an activities, environment, interactions, objects, users (AEIOU) framework.For the back-end prompt engineering, we included a system prompt that instructs the Nudgy about its role as well as to clarify that it should not generate writing for the student. The Nudgy prompt templates ensure that the Nudgy identifies concepts and makes suggestions in terms of phrasing. This also avoids one of the issues students face when using GenAI for a written assignment – the potential for plagiarism. The back-end prompts thus define everything from phrases to patterns or trends. These prompt templates were tested, refined and modified before being implemented in Waai.Optimising for mobile accessibilityWaai, which continues to be supported by NTU’s Institute for Pedagogical Innovation, Research &amp; Excellence and Application of Teaching and Learning Analytics for Students, has most recently been optimised for mobile&nbsp;accessibility, which should increase frequency of use. While Waai surpasses writing apps designed for editing, it remains experimental. Our research suggests that it can enhance the writing and thinking process only if students are able to decode the feedback and apply parts of it to the revision of their drafts. As with any writing app, feedback is most beneficial when partnered with a human reader response.&nbsp;Joanne Chia&nbsp;is lecturer&nbsp;and&nbsp;Angela Frattarola is&nbsp;senior lecturer and director of the Language &amp; Communication Centre, both in the School of Humanities at Nanyang Technological University, Singapore.&nbsp;This is an edited version of the blog post “Empowering student writing: how we built an AI writing assistant app”, which was first published by NTU’s Institute for Pedagogical Innovation, Research &amp; Excellence.If you’d like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

User needs, smart prompts and functionality were central to creating an AI tool that supports student writing and thinking. Here, Joanne Chia and Angela Frattarola share the pedagogical and technical considerations involved in the process

Brainstorming benefits of an AI writing assistant app

You might think that a few well-directed prompts can produce a PhD-worthy thesis or a new app. Generative artificial intelligence (GenAI) and its&nbsp;latest achievements are hard to avoid, after all, feted in blog posts and news articles. But can it (and a short course in prompt engineering) replace the deep expertise that comes from years of study and experience? Where does human intelligence meet artificial intelligence and how can we better support humans in this interaction?&nbsp;In short, what is GenAI proficiency, and how do we acquire it?Beyond prompt engineering, effective use, our research has found, maximises benefits such as quality outputs, innovation and productivity, while&nbsp;avoiding risks. Ineffective use, on the other hand, includes failure to realise productivity benefits, adopting incomplete or inaccurate output or uploading sensitive data.&nbsp;The role of human skills and knowledge as use of AI (and GenAI, in particular) has proliferated has been a focus of our work in the Collaborative Intelligence Future Science Platform (CINTEL FSP), a strategic research initiative of Australia’s national science agency, CSIRO. Over the past year, we have interviewed expert users of GenAI tools to explore what proficient use looks like and what competencies support it. Proficiency was inferred from examples of effective and ineffective use provided by&nbsp;knowledge workers across roles and industry sectors (such as scientists, designers, teachers, legal practitioners and organisational development advisers) who are recognised as expert GenAI users in their respective fields.GenAI proficiency boiled down to three aspects:&nbsp;effective promptinginformed and responsible choicesdiversity and complexity of use and tasks.&nbsp;Prompting is how a user asks the GenAI tool to perform a task. Our research revealed that effective prompting requires more than basic knowledge and practice. Rather, it necessitates an array of competencies (and, yes, practice as well).&nbsp;AI transformers like ChatGPT are here, so what next?DeepSeek and shallow moats: what does it mean for higher education?Here are seven AI tools you should be using for your teaching and researchInformed and responsible choices mean using the right model for each task, taking privacy into account and making ethical decisions when delegating tasks to the technology (such as planning workflow) or while prompting. It also involves calibrating trust and staying engaged with the output to make educated decisions on how to use it.Diversity and complexity of use and tasks involve working with a variety of tools for a range of tasks, with varying complexity (for example, using it to polish one’s writing versus to build a multi-agent GenAI system).&nbsp;Essential competencies that underpin GenAI proficiencySo, what are the skills, knowledge and mindsets that support proficient use of GenAI tools?Domain expertiseNearly all interviews identified&nbsp;knowledge relevant to the task being performed as a requirement for proficient use of GenAI tools. Even though GenAI can reproduce a vast amount of information and knowledge, it is only when a human employs their domain expertise to prompt the GenAI that it will produce relevant, appropriate output. This expertise includes understanding terminology and contextual information. Human expertise is also required to verify and potentially augment the outputs and weed out hallucinations.&nbsp;GenAI literacyAnother key form of knowledge is GenAI literacy. Understanding the relative strengths and weaknesses of these tools, specifically knowing which tools are available, how to interact with them and their risks, will enhance prompting. The frequent arrival of new models has made GenAI literacy particularly challenging.&nbsp;Thinking and communication skillsTraditional academic skills – including communication skills, critical thinking and metacognitive skills – balance the temptation to naively or absent-mindedly outsource tasks to AI. Verbal communication,&nbsp;including perspective-taking, was mentioned as important for crafting good prompts and providing feedback that elicits the best response from the GenAI tool with fewest iterations.&nbsp;Critical thinking is the ability of users to apply their own judgement about the GenAI’s outputs rather than simply accepting what is produced. Metacognitive skills involve self-reflection, planning the work, monitoring progress and reflecting on performance. This helps users identify how GenAI can be applied, how to create appropriate prompts and the ways in which GenAI output could be improved.&nbsp;Prior programming knowledgeExperts who use GenAI in more complex or novel ways often have a programming background, our interviews found. GenAI tools can be used via simple text- or voice-based interactions, but programmers who could access the models through coding languages and bypass the interface limitations (for example, the number of tokens – typically, a token is a word, character or subword allowed in input or output) achieve more innovative and novel outcomes.&nbsp;Play, curiosity and an inquisitive mindsetAs this technology does not yet come with an instruction manual, curiosity and inquisitiveness, plus persistent play and experimentation, help users to gain proficiency. The mindset is to be continually learning and sharing information to keep up with evolving GenAI tools. This approach is likely to remain necessary. &nbsp;Proficient users are also flexible, adaptable and diligent. Flexibility is useful not only in refining prompts to improve results but also in thinking about the work in general and aligning tasks with what GenAI can do. Expert users are diligent, aware of the potential for errors, and carefully inspecting the GenAI’s responses rather than blindly trusting them. &nbsp;

  
      
  
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Figure 1: the elements of GenAI proficiency and their relationships

&nbsp;How AI leads to a revaluing of human intelligenceEarlier versions of AI could only perform well-defined tasks. Today, GenAI tools can be used for a wide variety of tasks, offering a path to enhancing knowledge workers’ performance. While mastering GenAI requires good prompting skills, it is merely the beginning. To harness its full power, users must be strategic, informed and adaptable. Embracing an active approach, continuously learning and staying updated with the developments in this rapidly changing area, experimenting with different tools, expanding the range of tasks the tools are used for, investing time and not being discouraged if desired results are not immediate are the building blocks of proficiency.&nbsp;Although some technologists claim that super-intelligent AI is imminent, the GenAI competency framework we present suggests that current tools still lean on human intelligence as a complement. By recognising this and using them in tandem, one can unlock the potential of GenAI.Einat Grimberg is a postdoc in the skills project within the&nbsp;Collaborative Intelligence Future Science Platform (CINTEL FSP); Claire M. Mason is a principal research scientist who leads the technology and work team in Data61 and the CINTEL FSP’s Skills project; Andrew Reeson is the research leader of Humans &amp; Machines group in DATA61 and assistant director of CINTEL FSP; and Cécile Paris&nbsp;is the chief research scientist and director of the CINTEL FSP; all are at the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Effective use of generative AI draws on a suite of skills that go beyond well-crafted prompts. Getting the best out of tech’s ubiquitous tool requires informed choices, field expertise, flexibility, diligence and a willingness to play

Are you a jack of all GenAI?

Artificial intelligence is no longer a theoretical concept; it is a practical tool that is fundamentally transforming the landscape of education and research. AI renders educational experiences more visual and engaging, while also streamlining repetitive tasks associated with teaching and research activities.&nbsp;Building an AI toolkit can help in all areas of academic life, from the classroom to the laboratory, fostering innovation in research and sparking student engagement. Here are the most helpful ones I’ve found.&nbsp;Promote self-learning of foundational knowledgeHitWit.ai is an AI-powered self-learning tool that aids learning through personalised, high-intensity learning sessions. It allows users to engage deeply with various content types, such as documents and videos.An insider’s guide to how students use GenAI toolsThe ‘deep learn’ framework: elevating AI literacy in higher educationTHE podcast: the AI university is comingThe tool features a multimodal AI mechanism, which creates mind maps and charts for visualising the learning process and offers spaced quizzes to improve retention. Its interactive, conversational approach positions the AI as a tutor that helps identify gaps in knowledge.Unlike traditional education, HitWit.ai emphasises a user-guided learning path and helps users track progress with scores. Create, personalise and engageHow can we enhance the interactivity of and engagement with the learning process? Captivating visuals and virtual models grab students’ attention. Platforms such as Canva and Gamma AI help design visually appealing presentations, posters and infographics. Other AI image generators like EdCafe AI, NightCafe, Rabbithole, Scribeli AI, Ideogram and Leonardo can also be used.Tools such as Napkin, Notion AI, Map-this.com and Chat GPT help create mind maps and graphic organisers. Other tools like Graphy help visualise data with charts and graph generation to help spot data trends and changes.The following five AI image prompt generators are noteworthy for their ease of use, customisation and versatility: GravityWrite (user-friendly), PromptMania (free, simple interface and easy to generate prompts without technical knowledge), AI Text Prompt Generator (straightforward by requiring some adjustments for optimal performance), Prompt Hero (designed for users familiar with AI image generation, offering prompt templates and a wide range of examples) and PromptPerfect (advanced with optimisation tools, associated with learning curve).AI assistants for PDFsChatPDF, ChatGPT and Adobe AI Assistant enable users to easily and quickly read, compare and question PDF files in an interactive manner. These tools are excellent for extracting knowledge insights, making comparisons and identifying knowledge gaps. TextCortex is a complimentary AI PDF reader that allows users to upload PDF, PPTX and DOCX files to their workspace, facilitating a multi-file chat feature.&nbsp;Generate videosStudents retain information better when engaging using multiple senses. AI tools such as Hedra AI and Synthesia create talking avatars to enhance learning. Hedra AI animates images or descriptions into avatars, while Synthesia generates interactive video lessons, simplifying complex topics. Instructors can use these tools for course introductions or explaining biological processes like mitosis.&nbsp;A practical application of Synthesia or Hedra could involve an instructor making a course introduction video to a topic or explaining a process in biology such as the scientific method or mitosis. Synthesia can bring a static presentation to life by generating a virtual biology instructor who walks through each slide generated by Gamma AI, for example.&nbsp;AI avatars can be customised to resemble an educator and narrate short explainer videos, breaking down concepts with examples. They can provide assignment feedback using gestures and visual aids for clearer understanding. In diverse classrooms, avatars can enhance accessibility by delivering lessons in multiple languages for English language learners.Streamline administrative tasks with custom GPTsGrading and administrative tasks take time away from teaching and research. AI tools such as Google Gemini, ChatGPT, MagicSchool, Khanmigo, Monsa AI and OpenAI help streamline these duties. OpenAI’s paid version enables custom GPTs to generate custom or reusable prompts that can provide specific feedback to students on an assignment and create quizzes, worksheets, lesson plans, images and discussion questions.Visualise conceptsAI tools like Midjourney and Stability AI enhance student engagement by generating images that visualise complex concepts. A biology instructor could use Midjourney to illustrate a species’ evolution under environmental pressures, sparking discussions on survival advantages. Another activity involves students designing fictional species for extreme environments and visualising them with AI.Employ your own personal editorWebsites such as Futurepedia and Adademiq.io, along with specific tools such as Perplexity and Storm from Stanford University, offer a variety of free writing resources. Writing tools like PaperPal AI, Trinka AI, Writefull, Grammarly, ProWritingAid, Hemingway Editor and Wordvice AI proofreader can assist with academic and technical writing.Review relevant papers in secondsConducting thorough literature reviews is essential but can be time-consuming. For example, a researcher exploring artificial neural networks can use Connected Papers to visualise how different studies are interconnected, ensuring a comprehensive grasp of the field.Platforms such as Semantic Scholar, Research Rabbit, Litmaps and Connected Papers help researchers quickly identify relevant papers and understand the relationships between them. Other programs like Mendeley, Zotero and EndNote can help manage references. Rayyan is a free web tool and mobile app designed to help researchers working on reviews and knowledge synthesis by speeding up screening and selection studies.&nbsp;Urbi Ghosh is a faculty member at CSU Global.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

An AI toolkit for all aspects of academic life

Harness the power of technology to reshape the tasks that make up your day. Here are the best AI tools to use in higher education

“Aiyoh, you ask one very solid question lah!” *When students hear this distinctly Singaporean response from our AI teaching assistant, they know they’re engaging with something unique. But as universities worldwide rush to implement artificial intelligence in their classrooms, our experience offers crucial insights into what makes an effective AI teaching assistant – and, more importantly, why it isn’t suitable for every course.&nbsp;Our journey began with a fundamental question: in an era where AI tools are becoming as ubiquitous as search engines, how do we create meaningful educational experiences while preserving the essential aspects of traditional learning? Rather than fighting the tide of AI adoption, we developed our own pseudo-instructor: Professor Leodar, a Singlish-speaking AI teaching assistant with a carefully crafted personality and deep integration with our curriculum.Professor Leodar has handled more than 12,334 queries from 154 students since January 2024, but the numbers tell only part of the story. What makes our selective embrace of this technology distinctive isn’t just the creation of a virtual instructor with local cultural awareness – it’s our careful consideration of where and when such tools are appropriate.

  
      
  
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Courtesy of Nanyang Technological University

Let me be absolutely clear: there is no substitute for the fundamental academic experiences that shape deep understanding. The midnight oil must still burn as students work through complex derivations by hand, synthesise information from multiple sources and develop the critical thinking skills that come from wrestling with difficult concepts. Our AI teaching assistant is a supplement to – never a replacement for – these essential learning processes.Our development process focused on three key elements often overlooked in the rush to implement AI solutions. First, we carefully selected a technical subject – data science and artificial intelligence – where answers have clear right/wrong outcomes as a pilot course. We deliberately avoided subjects requiring deep conceptual synthesis, complex derivations or extensive critical analysis – areas where traditional study methods remain irreplaceable. Second, we ensured strong educator leadership throughout development, recognising that pedagogical expertise is crucial in determining appropriate use cases. Third, we built a comprehensive knowledge base from course materials before implementing any AI functionality.How AI teaching assistants are transforming student supportSpotlight guide: Bringing GenAI into the university classroomDeepSeek and shallow moats: what does it mean for higher education?The technical architecture relies on retrieval augmented generation (RAG) technology deployed via Amazon Web Services, allowing real-time updates for course materials and announcements. However, we are careful to limit its scope to appropriate applications – primarily in the particular subject where solutions follow clear patterns and methodologies.The user analytics tell a fascinating story about student engagement. During our first continuous assessment week, we recorded more than 1,200 queries – but the pattern of usage told us more than the volume. Most revealingly, our students used Professor Leodar when instructors were not available, either in the wee hours of the night or during the weekends. Also, our highest-performing students don’t use Professor Leodar as a crutch; they use it as one tool in a broader learning strategy. While struggling students often seek quick answers, top performers use the AI assistant to supplement their understanding after putting in the necessary groundwork through traditional study methods.In our data science and AI course, successful students consistently demonstrate an ability to grasp fundamental concepts through traditional learning before using AI to explore applications. They spend long hours in front of their computer not because they rely on AI but because they’re doing the essential work of understanding core principles, working through problems step-by-step and developing deep conceptual understanding.The impact has been significant: 97.1 per cent of students report positive experiences with Professor Leodar. However, this success comes with an important caveat: it’s limited to specific types of courses and specific types of questions. We actively discourage its use for tasks requiring deep analytical thinking, theoretical understanding or creative problem-solving. In these areas traditional study methods remain essential.For institutions considering their own AI teaching assistants, our experience offers three crucial lessons. First, resist the temptation to deploy chatbots across all courses; many subjects require traditional learning methods that cannot and should not be replaced. Second, carefully evaluate where AI assistance is truly beneficial versus where it might hinder deep learning. Finally, maintain rigorous academic standards that require students to demonstrate understanding through traditional means.As we prepare students for a workforce where AI proficiency isn’t optional, the question is not whether to embrace AI tools but how to integrate them meaningfully – and selectively – into education. Our experience suggests that success lies in finding the right balance: leveraging AI where appropriate while preserving the irreplaceable aspects of traditional academic rigour. The midnight oil will still burn in university libraries and laboratories, not because students are asking chatbots for answers but because genuine understanding requires time, effort and dedicated study.* “You have asked an excellent question.”Leonard Ng Wei Tat is assistant professor in the School of Materials Science and Engineering at Nanyang Technological University,&nbsp;Singapore.This is an edited version of the blog post “Eh, don’t play play! What we learned building a successful AI teaching assistant (and why you might not need one)”, which was first published by NTU’s Institute for Pedagogical Innovation, Research &amp; Excellence.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Selective embrace of artificial intelligence is key to its success in supporting student learning. Here, Leonard Ng Wei Tat shares lessons from building an effective AI teaching assistant 

When to use an AI professor (and why you might not need one)

Higher education discussions on artificial intelligence (AI) often gravitate towards utopian ideals or dystopian fears. But instead of looking to science fiction, we might learn from the benefits of regulating previously unregulated markets when attempting to address the potential for unethical use of AI in marking and assessment.&nbsp;To foster trust, accountability and equity, educators should be supported in conducting transparent AI trials for marking and feedback, reducing the risk of covert use.Low-stakes v high-stakes assessmentsAI has proved its value in&nbsp;low-stakes formative feedback, where its rapid and personalised responses enhance learning. However, in high-stakes contexts where grades influence futures, autonomous AI marking introduces risks of bias and distrust. We therefore suggest that for high-stakes summative assessments, AI should be trialled in a supporting role, augmenting human-led processes.&nbsp;In the first instance, it should only be used after educators have completed their usual marking and released their marks and feedback to students. In these AI-augmented trials, AI could provide complementary suggestions with human oversight and transparency. In these scenarios, students would be encouraged to “opt in” to provide feedback and “feed forward” on the process.&nbsp;Enhanced AI feedback generation systems&nbsp;Off-the-shelf generative AI tools such as ChatGPT, Copilot and Gemini often provide general or superficial feedback that lacks the depth, nuance and discipline-specific guidance needed for tertiary-level study. A more effective approach involves retrieval-augmented generation (Rag). Rag is a framework that combines a vector database with a generative AI model to retrieve relevant past information, such as documents (for example, past assignments) that have been uploaded into the vector database, for response generation. Rag requires access to an application programming interface (API), and setting it up typically involves coding. Numerous code examples and libraries are available from open-source platforms.To further refine AI-generated feedback, institutions can additionally use fine-tuning, which customises the model by training it on subject-specific datasets or higher-education-related data, enabling the model to produce even more relevant and contextually aware feedback. By combining Rag, which retrieves&nbsp;relevant past information for response generation, with fine-tuned AI models, which allow the model to learn from subject-specific data, educators can provide feedback that is not only more precise but also better aligned with course expectations. These AI-enhanced systems can highlight students’ skill gaps, suggest improvements and encourage deeper reflection through tailored prompts. This approach does not replace educators; instead, it supports them by providing tailored feedback suggestions that educators can incorporate into their own feedback.It’s easier than you thinkIn practice, implementing Rag and fine-tuned models is more straightforward than it may sound. Educators do not need to build AI models themselves – simply uploading proprietary course materials can significantly enhance AI-driven feedback. Even the technical set-up is increasingly accessible, with some educators able to integrate these tools independently rather than relying on central IT resources.What about data privacy?&nbsp;Concerns about data privacy in AI-driven feedback are valid, but Rag and fine-tuning do not increase these risks. Unlike general-purpose GenAI models that rely on broad, pre-trained datasets, Rag restricts AI’s access to specific, institution-approved sources, and fine-tuning embeds only vetted, proprietary knowledge.&nbsp;If educators and institutions do not take the lead in shaping responsible AI feedback systems, commercial technology firms will fill the gap, potentially prioritising profit over pedagogy. By engaging proactively, the sector can retain control over ethical AI deployment, ensuring that student data is handled securely while maximising AI’s potential to support learning.Spotlight guide: Bringing GenAI into the university classroomAssessing the GenAI process, not the outputDesigning assessments with generative AI in mindAddressing resistanceResistance to AI in marking and summative feedback remains strong among educators and students. When one of us discussed with students recently in a creating digital futures course, their reaction to the idea of educators using AI in marking and feedback provision was overwhelmingly negative. As one student put it: “I would be really upset if assessors started to use AI. I know it’s coming, but I hope I’ll have finished my degree by then.”This sentiment reflects broader concerns about fairness, transparency, potential dehumanisation and value for money, highlighting the need for thoughtful communication and ethical safeguards.&nbsp;To navigate resistance we recommend the following principles:Include students and colleagues in discussions about AI’s potential role and limitationsEmphasise the role of humans-in-the-loop (that is, with educators involved at every step) and human oversight (with educators being ultimately responsible) to ensure fairnessBegin with opt-in trials outside the traditional marking process and with research ethics approval in place to build trust and gather feedback.A proposed pilot might proceed as follows:Traditional marking first: educators complete grading and provide feedback as usualOpt-in participation: students are invited to try an experiment exploring AI-augmented feedback, with no impact on their previous communicated gradesAI feedback generation: AI supplements the original feedback, providing additional insights reviewed by the assessor for accuracy and relevanceCombined and augmented report: students who opt in receive a combination of human and AI-generated feedback to compare and evaluate (without the previous summative feedback or mark being amended)Critical reflection and feedback: participants are invited to share their experiences to inform future iterations.Rethinking assessments and building a shared visionBeyond integration, we should also rethink assessments altogether. Peer evaluations, oral exams and synoptic assessments might offer more holistic methods of evaluating learning and knowledge. Furthermore, such assessment innovations might reduce over-reliance on rigid marking schemes, embrace diverse learning styles and potentially increase student-educator contact time.Isabel Fischer is an associate professor (reader) of responsible digital innovation and education at Warwick Business School; Ashkan Eshghi is a Houlden fellow at Warwick Business School; Matt Lucas is a senior product manager at IBM and a teaching associate at the University of Warwick; Neha Gupta is an assistant professor in the Information Systems and Analytics Group at Warwick Business School.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Get students on board with AI for marking and feedback

AI can potentially augment feedback and marking, but we need to trial it first. Here is a blueprint for using enhanced feedback generation systems and gaining trust

A few years ago I led a team that developed an online feedback system for students. They could scan a QR code that linked to a web application and provide feedback on their understanding of the lesson content and ask the lecturer open questions. At first, the tool seemed useful, and the students liked it.However, I noticed that the easy way to ask questions online killed human interaction during the lessons. The classroom and lesson time should be perceived as a safe space for students to speak in public, ask questions, make mistakes and learn. With this new system, the students were indeed asking more questions but they were writing them in a different channel. The technology didn’t help them learn how to speak in public and ask questions verbally.&nbsp;In the end, we decided to turn the system off on the day of the lesson, and turn it back on for the rest of the week (when the students were doing their homework).As an educator, I am both excited and apprehensive about the potential benefits and drawbacks of integrating technology, including AI, into the classroom. Mainly, I believe that we should focus on the effect of the technology rather than the technology itself.My current project uses computer vision and AI in the classroom to automatically detect the teacher’s activity and the students’ responses. Our vision behind this project is that there is no ideal way of teaching, but that good lecturers efficiently adapt their teaching style to the audience. The system is transparent for the teacher and the students, mainly using microphones and cameras located in the classroom.

  
      
  
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Using AI to show teacher and student interaction in a classroom. Photo courtesy of Nanyang Technological University

&nbsp;The system creates a simple report for the lecturer, showing what activity he or she deployed during the lesson and the students’ response. The goal of this project is to highlight and reinforce the relationship and interactions between the lecturer and the students. In that sense, the system uses AI in a transparent way to emphasise the teacher-student relationship, and it provides feedback on this. If the teacher lectures for two hours non-stop but all the students stay highly captivated and active during those two hours, there might not be a need to change. However, if after 20 minutes of lecturing, all the students are starting to fall asleep and stop paying attention, the teacher should adapt their teaching approach. Otherwise, they are not keeping this relationship alive.&nbsp;When I was a student, my own involvement and participation in the classroom were strongly associated with my relationship with the lecturer (good or bad); teaching is, above all else, a personal relationship between individuals, and technology should focus on fostering this. Our pedagogical philosophies, innovative ideas and approaches should drive our teaching activities, while we use technology and AI to enhance their implementation.Spotlight guide: Bringing GenAI into the university classroomDeepSeek and shallow moats: what does it mean for higher education?How to create a higher education AI policyImplementing technology and AI solutions in the classroom also requires the teacher to undertake a continuous exploration process. If using technology has clear benefits, when it allows the teacher to get the students more involved in their tasks, to provide a more individualised teaching approach or to improve students’ intrinsic motivation towards learning, it is a positive development. But with benefits come an associated cost – for example, using new technology takes time. If learning how to use the system in a mathematics lesson takes longer than the time students have to practise and learn maths, it is counterproductive. Multiple factors, including teachers’ and students’ technology and AI literacy, can also have an impact.&nbsp;In that sense, like any pedagogical approach, there is probably no magical solution when it comes to the best uses of technology for education. What works in the morning lesson may not work as well in the afternoon; what works with one group of students may not work with another group; what works in Asia may not work in Europe.&nbsp;Teachers should always be reminded to use technology carefully when it comes to teaching and learning, constantly balancing the benefits and the costs (losing human relationships through the use of technology is a cost). Similarly, new tools and technologies should be co-created by teachers and engineers to ensure that the latest technological advances are available to the teachers, and to ensure that the engineers who develop this technology do so in an optimal way to serve a useful purpose for teaching and learning.&nbsp;John Komar is assistant dean of research support in the Office for Research, and assistant professor in the National Institute of Education – Physical Education &amp; Sports Science at Nanyang Technological University, Singapore.&nbsp;This is an edited version of the blog post “Balancing&nbsp;technology and&nbsp;personal&nbsp;connections in the&nbsp;classroom”, which was first published by NTU’s Institute for Pedagogical Innovation, Research &amp; Excellence.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

‘We should focus on the effect of the technology’

How much tech is helpful in the classroom? Highlighting both benefits and drawbacks, John Komar explains how computer vision and AI can reinforce personal connections and interactions between teacher and students

‘We should focus on the effect of the technology rather than the technology itself’

PowerPoint presentations are a staple in higher education teaching and learning. But creating effective and impactful ones isn’t easy. Fortunately, GenAI tools can help. I use ClassPoint to transform the classroom into a vibrant hub of learning and exploration.&nbsp;Get started by downloading the ClassPoint plug-in. Then, install the software, create an account and log in. While the paid version offers additional features, the free version offers more than enough to make a significant impact on your presentations. Next, try these activities:1.&nbsp;&nbsp;&nbsp;&nbsp;Collaborative brainstormingInteractive brainstorming activities are excellent ways to increase participation, manage the classroom effectively and foster a collaborative learning atmosphere. These activities not only break the monotony of traditional teaching methods but also keep learners on their toes. The “word cloud” feature is particularly effective, allowing you to collect student ideas in a fun way.&nbsp;As students submit their responses, the word clouds appear on the screen in real time, creating a visual representation of the collective thought process. This dynamic approach encourages students to contribute actively and observe how their peers think, which can lead to a deeper understanding and appreciation of diverse perspectives.2.&nbsp;&nbsp;&nbsp;&nbsp;Interactive visualisationOne of the standout features of ClassPoint is its interactivity. The “slide drawing” function enables students to work either individually or collaboratively on the presentation slides. You can assign students a variety of activities that cater to different learning styles, especially benefiting visual learners.&nbsp;Activities include identifying and circling grammar or spelling mistakes, completing crossword puzzles, engaging in matching exercises, etc. Additionally, the “image upload” function allows students to share images that are relevant to their lives. This personal touch can mitigate anxiety and scaffold their language output while students discuss and describe images that hold personal significance. Both the “slide drawing” and “image upload” functions allow you to record all discussions and interactions so that you can review them later and assess students’ contributions.Spotlight guide: Bringing GenAI into the university classroomThree ways to use ChatGPT to enhance students’ critical thinking in the classroomTo demystify AI for your students, use performance3.&nbsp;&nbsp;&nbsp;&nbsp;Real-time quizzes and pollsClassPoint’s quiz and poll functions allow instructors to effortlessly collect student input and responses in an engaging way. With just a few clicks, you can create polls or quizzes that instantly appear on students’ devices. This functionality serves multiple purposes, including gauging understanding, gathering opinions and sparking curiosity. You can display the real-time results on the PowerPoint slide, allowing you to dynamically adjust your teaching approach based on student responses. Here are some other ways to use ClassPoint’s polling and quiz features:•&nbsp;&nbsp;&nbsp;&nbsp;Use entrance polling to assess perception or prior knowledge•&nbsp;&nbsp;&nbsp;&nbsp;Set up exit quizzes to check understanding and application•&nbsp;&nbsp;&nbsp;&nbsp;Host competitive quiz sessions to review and reinforce learning.4.&nbsp;&nbsp;&nbsp;&nbsp;Customisable templates and AI quiz generatorClassPoint offers a variety of customisable templates that are both visually captivating and pedagogically effective. You can choose from different layouts, colour schemes and question types to craft slides that are professional and learner-friendly. Furthermore, ClassPoint’s AI-powered quiz function streamlines the process of generating questions aligned with Bloom’s Taxonomy. For instance, after reading a paragraph, you can deploy an AI-generated quiz to check students’ understanding immediately, so you can quickly identify and address misconceptions.Pro tips:•&nbsp;Keep it interactive: use interactive elements judiciously to capture and maintain your students’ interest. Keep a balance between entertainment and education, ensuring that the excitement ClassPoint generates does not overshadow the core learning objectives. For example, after an engaging “word cloud” activity, guide students to analyse the results of their submissions. Connect them to the lesson topic and teaching goals, ensuring that students consolidate their knowledge and apply it effectively.•&nbsp;Seek feedback: after using ClassPoint, proactively seek feedback from your students. Their perceptions can assist you in refining your instructional methods, ensuring that your lessons are engaging and aligned with their needs and preferences.•&nbsp;Stay updated: recognise that GenAI tools like ClassPoint are continuously being updated. Keep an eye on new updates and features and be open to experimenting with them. This agility will allow you to integrate new tools and features in creative ways, optimising your teaching strategy and enriching your students’ learning experience.By leveraging tools like ClassPoint, you can create a more dynamic classroom environment that captures students’ attention, fosters deeper understanding, rekindles a passion for learning, and enhances the overall academic experience. Use it as your secret weapon to bring your teaching to life.&nbsp;Chaonan Xu is an associate language lecturer at the English Language Centre at the School of Languages at Xi’an Jiaotong-Liverpool University.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Say goodbye to boring presentations

GenAI can supercharge your PowerPoint presentations to offer students interactive opportunities that increase participation and deepen learning. Here’s how 

“I dropped out of my business degree. It’s not worth it.”&nbsp;“We don’t hire people with business degrees.”&nbsp;Comments such as these are unfortunately more frequent as the price of higher education increases, and online business education becomes ubiquitous. In part, these comments are driven by student experiences of “ghost courses”, where the lecturer seems all too absent, forum posts go unanswered, non-appearances of other students proliferate, and recorded lectures mean that no attendance is needed. (At its most extreme, recorded lectures have reportedly been used after the&nbsp;professor has died.) Under these conditions, higher education feels like a solo pursuit, rather than an interactive experience requiring collaboration and engagement between the lecturer and fellow students. The message is that distractedly listening to a recorded lecture while vacuuming the floor, commuting or watching your child play sport on a Saturday morning will suffice. Images on university websites depicting students apparently “learning” in isolation, with nothing but a lonely cup of coffee and a curated range of online resources to keep them company, reinforces this illusion. Learning becomes focused on the completion of assessment rather than immersion in a journey. Such a learning experience is exacerbated by truncated learning sessions that only allow for surface-level engagement with any particular topic.&nbsp;Three ways to optimise your business school’s AI-related offeringCultivating emotional intelligence in business educationSpotlight guide: The business of online educationSo, where indeed is the value in an online learning experience like this?Online business courses are more and more built with a highly curated, homogenised design of “all on, all recorded”, where all resources are made available from the first day, and interactions with the lecturers are prerecorded. The message to students is there is no need to turn up because the teacher appears not to be turning up, either.This digital pedagogical approach takes away the space for a new or different lecturer to enter the online course and make it their own. When courses are delivered more or less to a prescribed script, this leaves little space for a unique teaching contribution. Highly centralised, standardised approaches to course design also stifle innovation. Not only does this cause conflict between academics and learning designers, but it also acts as a barrier to “teachership”. This is a particular concern for business education, given&nbsp;criticisms of the transactional, individualistic, credentialling-only focus of business curricula. Taking away lecturers’ ability to enact their academic freedom and autonomy leads to their giving in. Critically reviewing overbearing learning design guidelines that enforce the “all on, all recorded” design would help to ease the lack of autonomy lecturers feel, reinstating the space for flexibility, creativity and imagination in teachership.Learning occurs from reaction – and teachership provokes thisLearning occurs as a result of a reaction to some sort of stimulus (touching a hot plate, reading a book, coming across a new worldview). The learning partnership between students and lecturers is that the lecturer agrees to provide the provocation, while the student agrees to be provoked. Part of being provoked into learning is interacting with materials. However, passive interactions do not drive deep, critical immersion. Nor do they create conditions where a student’s inquisitiveness and curiosity can pique. Education is, after all, a pursuit that requires interactions within an engaged learning community.Designing online courses that preserve space for teachership honour the fundamental human interactions needed for critical thinking and learning. When teachership is a mandatory part of course delivery, students gain a companion on their learning journey. They find a lecturer who will walk beside them, provoking their learning along the way, and ensuring that inquisitiveness is reciprocated.As lecturers continue to express concern about empty Zoom rooms where only one or two students turn up (if you’re lucky), consideration needs to be given to the “attendance is not required for learning” message that the “all on, all recorded” pedagogical approach promotes. As this message becomes reinforced across multiple courses in an online business degree, students are taught that engagement is an unnecessary use of their time. They can pass their assessments without it.These ghost-like conditions mean those who do engage find that peer interaction falls far short of their hopes and expectations, resulting in a lacklustre experience of studying business at university. For the lecturer dedicated to teaching, logging on week after week to empty Zoom rooms is equally heartbreaking.No need for learning partnerships…or relationshipsAnother potential consequence of teacherless pedagogy should be considered: the receipt of&nbsp;student feedback that ranges from defamatory remarks about appearance, accents and attire and general insults and calls for punishment to outright racist, misogynistic or homophobic attacks. The lecturers on the receiving end of such feedback are left demoralised and traumatised. They feel threatened and may suffer with mental health issues as a result.&nbsp;Universities are deploying some solutions to mitigate this type of feedback, but they often involve hiding abusive feedback from targeted academics, not changing the fundamental behaviour. However, the “no personal interaction” pretence of some online courses that leaves students bereft of teachership does not support the formation of personal relationships between students and lecturers, and so does not support mutually respectful learning partnerships. In this relationship wasteland, personal attacks become easier to dish out. The trend for universities to use call-centre-like one-stop-shops for student enquiries and support compounds this detachment from their lecturers. This approach reinforces the positioning of students as consumers. Not learners and partners…customers.Adult students should expect and accept no less than a human experience to guide, facilitate and nurture their learning. University students need to show perseverance if they are to attain their degrees, and this perseverance is in part driven by the relationships they form with fellow students and with their lecturers. The sentiment that universities need to earn their students’ commute should flow through into their online environments, as well as on-campus courses.&nbsp;As business schools continue to struggle to demonstrate their legitimacy, ensuring the learning partnership and companionship between students and lecturers is a fundamental ingredient. The efforts of business students must not be frustrated by ghost online courses, but nurtured with determined teachership.Anita Wheeldon&nbsp;is a senior lecturer (management) in the School of Business at the University of Southern Queensland, Australia.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Online should not mean offline for business lecturers

When online courses are delivered more or less to a prescribed script, this leaves little space for an educator’s personalised contribution and autonomy, writes Anita Wheeldon. Here, she makes the case against ‘teacherless pedagogy’

The higher education sector has form for betting on technological moats that turned out to be mirages. In the early 2010s we rushed to build massive open online course (Mooc) platforms, convinced we’d need our own infrastructure to survive in the digital age. A decade later, most of those courses – and the custom platforms we built to host them – have been abandoned. As AI reshapes education, we risk repeating this costly mistake.DeepSeek R1 recently made a splash – you may have heard of it (or even downloaded it). It’s technically impressive, meeting or beating similar models from OpenAI on many benchmarks, and it’s also “open weight”, so anyone can download and run the model if they have the hardware to run it.DeepSeek didn’t even just release one model – it released a few different models (each with slightly different trade-offs). It also describes in a research paper how it trained them all for a significantly lower cost, in terms of time and money, than its competitors. The research community needs more time to evaluate these claims, but it does seem like this could be at least a small breakthrough in reducing the amount of resources it takes to train a new large language model.&nbsp;How to create a higher education AI policyVoice, agency and style: what goes missing when AI chats backThe AI genie is out of the bottle – now what?The market certainly seemed to think so: Nvidia’s shares lost nearly $US600bn in one day, based on fears that customers wouldn’t need to buy as many of their AI accelerator chips to train their models. Although even that story is complicated – DeepSeek is a “reasoning” model, which trades off less time and resources for training with more time and resources for inference (ie,&nbsp;running the model to generate text).Still, DeepSeek’s technical achievements are impressive, but the deeper story is what it tells us about the state of LLM research and development. The argument of the leaked 2023 Google memo asserting “we have no moat, and neither does OpenAI” seems to be holding up. Despite trillions of dollars of investment, it really does still seem like an upstart can come out of nowhere to release – and share – something that’s competitive with state-of-the-art offerings from the tech giants. This poses a strategic question for higher education leaders: how should institutions position themselves in response?Some universities have already placed significant bets, signing exclusive partnerships with major AI companies. The California State University System’s deal with OpenAI will provide ChatGPT access to 500,000 students and faculty. UNSW Sydney has inked a similar agreement (albeit on a smaller scale). These moves reflect an understandable desire to get ahead of the curve, but they may also lock institutions into particular tools and ecosystems at a time where new, and perhaps better, alternatives are emerging. The higher education sector is facing huge financial challenges, and these contracts take precious resources away from faculty salaries or tutor budgets or any of the other crucial functions of the institution.The emergence of models like DeepSeek R1 is a timely reminder that the AI landscape remains highly dynamic. Rather than pursuing exclusive relationships with specific providers, institutions might better serve their communities by staying provider-agnostic (where they engage at all). This approach acknowledges both the rapid pace of technical change and the likelihood that tomorrow’s leading models may come from unexpected sources.For individual educators, DeepSeek’s release reinforces what many of us have already realised: the specific model matters less than how we integrate AI capabilities into our pedagogical practice. Whether students use GPT-4, Claude or DeepSeek (and let’s face it, they will) the fundamental challenges remain the same. How do we design assessments that meaningfully evaluate learning in an AI-augmented world? How do we help students develop the critical thinking skills to effectively collaborate with AI tools?For university administrators and planners, these developments suggest a few key principles:avoid long-term exclusive commitments to specific AI platforms or providersinvest in developing institutional AI literacy and governance frameworksfocus on building adaptable infrastructure that can accommodate multiple AI tools.There may be other upsides to these developments. As model training and deployment costs decrease, universities may find it increasingly feasible to develop specialised models for academic domains or research applications. Such projects could focus on specific institutional needs rather than attempting to compete with general-purpose models.Ultimately, DeepSeek R1 reinforces a crucial message: in the AI era, competitive advantage will come not from controlling access to certain models, but from skilfully integrating AI capabilities into our core educational mission. Universities that build their strategies around particular AI platforms risk finding themselves trapped in technological dead ends, while those that focus on developing institutional AI literacy and adaptable frameworks will be better positioned to embrace whatever technological developments emerge.The real moat in higher education isn’t technological; it never has been. It’s the ability to create transformative learning experiences and generate new knowledge. Technology is merely a tool – in reality, a system of tools, people and other resources – in service of these fundamental goals. The winners won’t be those who bet early on the right AI platform, but those who most effectively help their communities master the art of learning and creating in an AI-augmented world.Ben Swift&nbsp;is a senior lecturer and cybernetic studio lead at the&nbsp;ANU School of Cybernetics.&nbsp;If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

DeepSeek and shallow moats: what does it mean for HE?

DeepSeek’s arrival may have spooked the markets, but what does it mean for the research and development of LLMs? Higher education should avoid putting all its eggs in one GenAI basket, writes Ben Swift

DeepSeek and shallow moats: what does it mean for higher education?

How should universities manage the rapid uptake of artificial intelligence across all aspects of higher education? We talk to three experts about AI’s impact on teaching, governance and the environment

THE podcast: the pros and cons of AI in higher education

How a liberal arts college can embrace AI tools

Lingnan University president Joe Qin offers advice on using generative AI, building up the science side of liberal arts education and how to encourage staff to share their ideas

Engineering students spend most of their time using highly structured mathematics to reach a numbered answer. So, for those like Patrick (an author of this article) – who learned English as a second language and then engineering to communicate using mathematics – it’s easy to think that following rules of spelling, grammar and paragraph construction will result in good writing. Just as following mathematical formulas will help you reach a technical solution.&nbsp;It’s also easy to feel that it would be wise to hide your shaky personal voice behind the more polished voice of artificial intelligence.&nbsp;So, as teachers, we have a new and interesting job to show students&nbsp;what exactly goes missing when AI chats back. And this lesson is relevant far beyond the classroom.An exercise with engineering students asks them to revise a rough sentence: “By having car suspension that changes the camber of the wheel during use, higher performance is possible.”&nbsp;Next the instructor asks the students: “What is automotive ‘high performance’?” Typically, students respond by talking about speed, handling and fuel economy. They can also see that the sentence needs fixing. Certainly, ZotGPT – University of California, Irvine’s (UCI) wrapper around ChatGPT – should be able to help students revise it. In this case, it gives us: “Improved performance can be achieved by using car suspension that adjusts the wheel’s camber during operation.” The subject of the sentence comes first, which helps. But what is&nbsp;“improved performance”? The generative AI has juggled the words syntactically but clarified nothing important. For that, we need a human who revises the sentence: “More road traction is generated when car suspension changes the camber as the vehicle turns.”Students get it. They might be smarter than GenAI, after all.Assessing the GenAI process, not the outputUsing GenAI tools to refine EAP assessmentThe (AI) sky isn’t fallingSo, what’s the big difference? It’s not better grammar or even clarifying the agent of an action. Our human can specify that a recognisable but ambiguous driving desire – high performance – must, in this case, mean better road traction. That’s what the camber adjustment delivers. GenAI will never be able to read the author’s mind nor figure out what they care about in a specific instance nor decide accurately what an audience needs to know – “high performance” can mean a few things depending on the context.We can put this another way: voice, agency and style appear differently in the age of AI.&nbsp;Sure, there are promising applications of GenAI in the classroom and for all sorts of professional writing jobs. Particularly promising is GenAI as a “thinking partner” that can, for instance, play the role of a skeptic when a writer is sharpening an argument. Also promising is GenAI as a trainable machine that can make evident patterns in a natural language corpus that might not be otherwise legible, or not as quickly. At the same time, GenAI capabilities will help us understand how real human intelligence works, decoupling it from the mere appearance of polished prose.&nbsp;Human intelligence, meanwhile, will show up more prominently in terms of common sense and in a capacity to navigate the world, where writing is one form of intelligence. “We are used to the idea that people or entities that can express themselves, or manipulate language, are smart – but that’s not true,” says Yann LeCun, a New York University professor, senior Meta AI researcher and a 2019 Turing Award winner. “You can manipulate language and not be smart, and that’s basically what LLMs are demonstrating.”&nbsp;But this lesson in human intelligence can be an especially hard sell if people have low confidence around their academic English.Perhaps they have a point. Shouldn’t we, as writing instructors, be more open about English language learners using AI to level the playing field linguistically? Our interview with engineering student Mariana Jimenez suggests otherwise.Mariana doesn’t think her English is perfect. Yet she compares AI output to her middle-school writing, when her goal was to “sound smart”. As she puts it: “In a sense, when you do more complex writing, your writing gets ‘worse’.” Less conventional writing reflects the idiosyncrasies of a person. To her, the “artificially simplified voice” of GenAI lacks real-world knowledge in the subject matter.So “voice” isn’t about asserting the grammatical first person, and “style” isn’t about puffing up word strings. (In fact, these linguistic functions can also be prompted easily.) Situation sensitivity, including things like audience and exigency, is just as important for STEM writing as it is for any other type of writing.Mariana has exceptional awareness of her English language use, writing and learning. Many multilingual students, however, struggle to move beyond the attitude that the strength of their academic writing in English depends most on using “the right words” and structuring sentences acceptably. The pressure to showcase advanced linguistic proficiency often leads multilingual students to favour AI-produced writing over their own, believing that ideas not presented in “perfect” academic English lack value.We need to teach that imperfect but authentic writing is more valuable than sentences that are polished on the surface.The lingo of writing studies might put it this way: from product, to process, to person. But now GenAI appears to do much of the writing process: with minimal human prompting, it can brainstorm, draft, revise and edit. So, new pedagogical insights and practices may emphasise how the writerly process is tied to the person in all their idiosyncrasies.We are discovering that these lessons from engineering and academic English classrooms resonate all the way across campus. The era of formalised rubrics for writing assessment will likely shift due to the emergence of GenAI. At UCI, for instance, our upper-division rubric for writing assessment traditionally has four equally weighted categories: critical thinking and analysis, use of evidence/research, development and structure, and language and style convention. We will probably drop the word “convention” tied to style, then add a fifth category “voice” as the marker of distinct human intelligence.And then beyond campus. In this article, we are making the case most immediately for the importance of the personal, the variable – even the erroneous – when it comes to technical and other sorts of writing pedagogy, which surprises most students and some instructors. But GenAI also highlights how the personal plays a crucial role in expert-level science writing and discourse; Stanford computer science PhD student Weixin Liang and co-authors make the case for the personal idiosyncrasies of peer review in contrast to the homogenising effects of GenAI (their study analyses a fourfold 2024 uptick in adjectives such as “commendable” and “innovative” in a large set of professional conference peer reviews).Finally, we remember one of those massively attended happy-hour instructor meetings where, amid the GenAI panic of 2023, Patrick calmly raised his hand and said he wasn’t worried. Sure, there were plenty of neat things on the horizon, but since he doesn’t teach his engineering students product, or even process as conventionally understood, he didn’t see how GenAI would get in the way.Patrick said he teaches “confidence”, which is the dispositional location for somebody’s voice, agency and style. Its span starts with students before their shared experience in a class, and it lasts long after. Now in the age of AI, we understand like never before the distinctly personal arc of writing, which is the only place confidence will ever appear, even when it comes to STEM.Qian Du is an associate professor of teaching and former associate director of the programme in global languages and cultures; Daniel M. Gross is professor of English and campus writing and communication coordinator; and Patrick Hong is a lecturer in the Henry Samueli School of Engineering and a former executive editor for Road &amp; Track&nbsp;magazine; all are at the University of California, Irvine.&nbsp;If you’d like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Voice, agency and style: what is lost when AI chats back

We need to teach that imperfect but authentic writing is more valuable than sentences that are polished on the surface, argue three US academics. Here, they share surprise findings from STEM and beyond

Voice, agency and style: what goes missing when AI chats back

Since the dawn of generative artificial intelligence, scholars have been divided. Some educators are enthusiastic about AI, have already incorporated it into their teaching and have usually allowed their students to use AI in their work. &nbsp;Several of them have published articles in higher education-related journals, in which they explain to their colleagues how to improve classes by integrating the tool and teaching their students the new art of “AI prompting”.Four objectives to guide artificial intelligence’s impact on higher educationOpen dialogue about AI in HE is the way forwardChatGPT and the rise of AI writers: how should higher education respond?In contrast, other academics are reluctant to use AI and hope that this phenomenon will dissipate quickly. They believe that AI impedes students from developing the core competencies taught in higher education, which are essential for professional success. They tend to prohibit the use of AI, monitor for potential cheating, adapt questions to exclude those that AI can answer or revert to paper-and-pencil exams.From a personal point of view, we are still hesitant to integrate AI into our daily professional lives, but we recognise its impact on higher education. For our students to be prepared for the future workplace, using AI in our teaching practices is going to be necessary, not optional, for embedding the core competencies of our discipline.AI is not a problem&nbsp;– lack of critical thinking isIn our field of management and strategy, AI has become a standard tool. Management consultants use Microsoft Copilot for meeting minutes, financial audits are partially automated using AI and ChatGPT is consulted over interns or associates. As a result, the question is no longer whether AI should be used, but how it can be used to further develop students’ core competencies and enhance their critical thinking abilities.As management and strategy scholars, our objective is to educate students to think critically and become responsible managers. Traditionally, students develop these competencies by writing a master’s dissertation, analysing a company’s strategy and offering recommendations, and writing reflective essays on a company’s strategic move.Instead of avoiding the use of AI to complete these assignments, we encourage professors to integrate it into the assignments. We show that that it frames students as true professionals-in-training and raises our expectations regarding the quality of students’ work.&nbsp;Educating students to think critically with AIFor example, integrating AI into the master’s dissertation process means viewing students as true researchers-in-training, whose objective is to provide meaningful contributions to academic literature, practice and society. Rather than merely skimming drafts and offering limited follow-up meetings with students, professors should engage even more than before with their students’ work by reviewing drafts, discussing with students how their research questions and literature reviews are framed, going through students’ data collection and analysis, and helping them find how their work can offer an original and relevant contributions, on the same way we would do with our PhD students.&nbsp;By raising expectations, students will soon discover the limitations of AI as a tool. Similarly, as Wikipedia and blogs have demonstrated in the past, AI is only able to provide limited assistance when it comes to serious research and cannot provide an original and unique perspective on a specific topic.&nbsp;Students’ evaluations were previously based on elements that were not directly related to their ability to think critically, such as structure, style and writing or proper citation conventions. These are now easily done by AI. Instead, the assessment of a master’s dissertation is now focused entirely on the demonstration of original and critical thinking, through the analysis and criticism of academic literature, for example. This typically involves the completion of a defence, to test a student’s capacity to argue and defend their own intellectual position and not only to provide a mere synthesis of previous works.Another example is the integration of AI for analysing the strategy of a given company and offering strategic recommendations. Similarly, it encourages professors to raise their standards by considering students as true consultants-in-training. It concentrates on the core of the strategic analysis by collecting valuable data and making recommendations that would be of value to CEOs or boards of directors.&nbsp;While AI will help students frame or categorise information on the competitive environment or the company, it typically has little impact on the analysis of a strategy for which it cannot access the relevant data. Yes, students can work faster using AI (and as consultants-in-training, they are expected to deliver fast!), but AI can’t replace students in producing relevant recommendations.&nbsp;Finally, integrating AI for writing reflective essays on a company’s strategic move encourages professors to concentrate on students’ reflective abilities. How does this strategic situation relate to the students on a personal and professional level? What advice would they give to a stakeholder involved in this strategy move?&nbsp;Students may rely on AI input now, but in the past they would have relied on their friends or family members for similar feedback. Furthermore, the use of AI does not prevent students from constructing their own arguments and finding ways to express themselves outside AI jargon.It would be unwise to view AI as a threat to our role as faculty members and educators. Instead, we believe that by raising our standards and envisioning our students as true professionals-in-training, the technology can be harnessed to focus on the skills students will need for the future. By doing so, we enhance our role as educators and improve the learning experience for our students by concentrating on the acquisition of students’ core competencies.Guillaume Carton is associate professor of strategy at EMLyon business school and Julia Parigot is associate professor of strategy at Institut Supérieur de Gestion.If you’d like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

How to integrate AI into strategy and business education

Incorporate AI into your teaching or ignore it and hope it fades from view? Let’s balance these two attitudes by raising our expectations of students

Digital disruption, short lifespan of skills and rising retirement ages mean people who want to thrive professionally must continually upskill and reskill. During a THE webinar, held in partnership with Instructure, Professor Nicola Wilkin, academic director of digital education and equity at the University of Birmingham, and Daniel Hill, managing director of EMEA at Instructure, discussed the challenges and opportunities that AI presents in the evolving labour market.The emergence of generative AI has changed the skills landscape, requiring people to develop new skills and adapt throughout their careers. “We shouldn’t underestimate the challenge that lifelong learning presents when we expect people to upskill significantly in a given area rapidly,” Wilkin said.&nbsp;Everyone involved in teaching and learning must create an ecosystem where lifelong learning is possible, Hill said. While the education sector is responsible for the way information is structured and absorbed by learners, governing bodies must implement support mechanisms to create a seamless link between the labour market and course content. Hill emphasised that education technology providers need to offer compelling tools that remove barriers for institutions, employers and students to interact.Mindful use of generative AI can smooth out these processes. With proper guidelines in place, AI can support educators and learners in brainstorming, identifying unexplored topics and developing skills, Wilkin said.&nbsp;Co-creating the future with a focus on critical thought is vital, Wilkin said. “We need to make sure that what we are doing is both equitable and ethical.” An outright ban on AI tools leads to a “bring-your-own-AI” mentality among students and staff, which increases the risk of data being leaked and unintentionally supplied to different AI engines, she added.&nbsp;With rapid changes in technology and skills demand, the knowledge and confidence to use AI tools and sense check results will be important, Wilkin said. Technology tools can exacerbate the digital skills gap among learners with less experience in using them. That’s why signposting is vital for such students as well as asynchronous and distance learners. The University of Birmingham is developing AI chatbots to recommend digital tools and opportunities for students.&nbsp;Instructure works to ensure that AI has the biggest and most equitable impact on learners and educators who use its tools. The company has created a set of guiding principles on AI to act as Instructure’s internal governance mechanism, Hill said. “It’s about how we can educate [staff and students] to use the tooling and technology and make the hybrid merge of AI and human intelligence better than just AI on its own.”The panel:Daniel Hill, managing director for EMEA, InstructureSreethu Sajeev, branded content deputy editor, THE (chair)Nicola Wilkin, academic director of digital education and equity, University of BirminghamFind out more about Instructure.

Thoughtful application of AI can support students’ professional development and improve equity in education

AI’s impact on lifelong learning and the future of skills

Creating a university artificial intelligence policy is essential for streamlining operations and anticipating future innovations. This policy goes beyond formalising procedures; it educates on AI capabilities and limitations, centres tools around legal compliance and ethical considerations, and adapts to future technologies.&nbsp;The primary purposes of an institutional AI policy is to:&nbsp;unite data governance, security and leadership teams towards a common goal&nbsp;regulate and put in place procedures for data ingestion into AI services&nbsp;audit and ensure safe, ethical and effective AI use in higher education.&nbsp;Your AI policy will not be a boilerplate reflection of what an AI policy should be – it will reflect your institutional culture of integrating disruptive technology. A successful policy won’t do&nbsp;much – most of your processes and procedures (such as software procurement, data sharing and privacy) probably already have owners and a well-defined process. Successful policy implementation will ensure those processes’ effectiveness with emergent disruptors, such as the recent advances of DeepSeek.Five key stages when embedding AI networking toolsIs AI literacy an information skill?The Goldilocks effect: finding ‘just right’ in the AI eraStep-by-step guide to creating a university AI policyHere, the process to develop an AI policy for a higher education institution is broken down into steps – from conception to implementation – taken by pivotal members of your organisation.&nbsp;Identify policy leadersConfirm which offices, titles, departments or informal working groups will lead writing or implementing AI policy within your university. This process should reflect how change happens in your institution, whether through a department, leadership or collaborative cohort.Survey the institution’s internal and external landscape&nbsp;The aim is to position the AI policy at the intersection of the organisation’s current and aspirational states. Start by surveying institutional stakeholders to understand usage and perspectives on AI. These groups include:&nbsp;Students: Students can be early adopters of new technology. Engage with libraries, teaching units and academic innovators to understand student usage. Academic innovators – the people who introduce and implement new ideas, methods or technologies to improve teaching, learning and research – can provide insights, and can be found sprinkled across your academic units.&nbsp;Research units: Source research-angled considerations and use cases from faculty and staff. These units often house subject matter experts who can translate marketing and sales concepts into practical realities.&nbsp;Data governance: Collaborate with IT for insights on cybersecurity, data governance and data-sharing practices. IT departments can direct you to key figures in these areas and help define data usage practices.&nbsp;Legal and ethics: Work with legal departments, ethics bodies and compliance officials to align with federal, state and organisational guidelines. These experts can bridge existing policies with broader legal requirements and point out niche institutional practices.&nbsp;Academic units: Gather insights from diverse disciplines to understand use cases and risks. Academic units may have their own perspectives on leveraging AI, reflecting their specialised ecosystems of trust, exploration and risks.&nbsp;Evaluate the external landscape focusing on three pillars:&nbsp;Authoritative: Examine federal, state and regents’ positions on AI. Identify leading entities and upcoming laws or policies that could impact AI usage.&nbsp;Applications and services: Connect with enterprise leads to understand AI integration in foundational tools. Many essential tools that institutions use are developed or hosted by external enterprises, making it crucial to understand their AI strategies.&nbsp;Peers: Engaging with academic peers and regional universities can provide a wide range of insights, whether they are ahead or behind in AI adoption.&nbsp;Build an AI advisory coalitionForm an advisory coalition among AI adopters, primary stakeholders and leaders to drive the AI policy. Use findings from your survey to form the team. The coalition will be crucial to identify institutional &nbsp;and stakeholder needs.&nbsp;Define what the policy is intended to achieve&nbsp;Your AI coalition will help you define success for an AI policy. Success can align to one of three pillars:&nbsp;Experimentation and pilots: Encourage disruptive growth and exploration. Policies might focus on rapid experimentation to explore the unknown.&nbsp;Centralisation and equity: Build stable, careful steps in evaluation and usage. Policies might aim for equitable access and centralised control to ensure consistent and fair use of AI.&nbsp;Retraction and cautious adaptation: Encourage a deliberate, methodical approach to integrating AI. Some policies might focus on thorough evaluation and risk management before implementation to minimise disruptions.&nbsp;These perspectives may differ across individual use cases. The approach to a research grant might differ from interactions with a learning management system vendor. The goal is to create a policy that balances innovation with safety and ethical considerations, ensuring that AI is used effectively and responsibly across the institution. Engaging your AI advisory coalition can yield powerful guidance on direction.&nbsp;Generating and updating a policy&nbsp;Your AI policy should reflect your institution’s culture and voice, integrating disruptive technology into practices already in place. It won’t replace processes such as software procurement or data sharing but will enhance them, ensuring their effectiveness and pointing to existing policies.A successful AI policy guides internal innovation and usage, directing resources and identifying key contacts for emergent needs. It partners with your institutional culture to define appropriate AI usage, helping your organisation manage today’s AI tools and prepare for future advancements.&nbsp;Eric Sembrat is director of digital learning technologies in the Center for 21st Century Universities at Georgia Tech.&nbsp;If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

A successful university AI policy guides internal innovation and usage, directs resources and identifies key contacts for emergent needs. Here are the steps and considerations for writing guidelines 

How to create a higher education AI policy

We are now in a world where AI is changing how we test and evaluate our students. Generative AI tools have shaken up assessment, bringing exciting possibilities but also challenges. These tools can crunch through mountains of data, give instant feedback, summarise huge chunks of information and churn out graphs that seem eerily credible. What can we do to address the use of this super-intelligent tutor at our students’ fingertips?Our response to the use of GenAI tools and assignments needs to be appropriate and balanced. We should not be going to the extreme and making all assessments proctored or distrust every submission from students. Ideally, we would embrace the use of GenAI tools, while putting in place measures to prevent misuse.How students’ GenAI skills and reflection affect assignment instructionsAI can help fix student evaluationsSpotlight guide: The evolution of authentic assessmentHere are some steps we can take to make our assignments smarter, without letting the technology take over.&nbsp;Try out the GenAI toolsTo review an assignment’s vulnerability against the use of GenAI tools, we need to test it out. Our digital native students are well equipped to use these tools and are adept in finding ways to manipulate various strategies to help them in their studies.&nbsp;As GenAI tools advance in data processing and writing capabilities, it’s crucial to keep up to date with these technologies. By testing GenAI’s abilities to complete our assignments, we can learn how to make the necessary adjustments to the questions posed. As we continually use these tools, we will then also find innovative ways to incorporate them and develop even more sophisticated student tasks.Review items to be assessedGenAI tools excel in writing, grammar correction, idea generation and quick summarisation. Our students also use these tools to rephrase content or conclude paragraphs. For each task that is easily accomplished by the tool, the marks allocated should be adjusted accordingly.&nbsp;This warrants a second glance at the learning outcomes covered by the assignment. In most cases, teaching staff greatly reduce the marks for writing or report structure as these can be easily covered by GenAI tools. The focus of assignments should shift towards encouraging students to solve more complex or content-specific problems that requires deeper thinking and interpretation.Make the assessment authenticTo focus assessments on higher-order thinking, ground the tasks in real-life situations. In authentic assessments, the problem to be solved comes from industry partners and there are numerous variables and perspectives to consider. There is also not one perfect answer to solve everything. Our students are also taught critical thinking and design thinking skills, and are encouraged to look for innovative solutions.To develop such assessments, we look for opportunities with industry partners to share the challenges they face, and then ask students to help in developing solutions to these problems. Industry partners working with our teaching staff are invited to attend seminars and answer questions students might have about the problem.Students can also use GenAI tools to help them come up with solutions, whether it is a solution to an industry problem or an end product such as an advertisement or poster. The students’ solutions are presented to the industry partner, who will then help assess and provide feedback from their perspective. It is the assignments based in real-life specific contexts for which the GenAI tool will have difficulty formulating answers.Embrace the use of the toolsAs we delve into the use of GenAI tools, we should also let our students apply them to aid in addressing complex issues and consider analysing the tools’ efficacy and weaknesses. For example, students might employ a GenAI tool to identify challenges within a specified industry and develop viable solutions. This is then followed by critically assessing the suggestions and justifying their final decisions.&nbsp;In another assignment, students generate a response from a public relations firm to a complaint, evaluating its weaknesses and strengths. This process is a step toward aiding students to integrate these tools into their future professional endeavours.&nbsp;Offer guidance to studentsGiven the evolving nature of GenAI tools, it’s crucial to guide students on their proper use. This includes reminding them of the university’s academic integrity policy and advising against posting confidential information in GenAI tools or using generated content as their own.Teaching staff should outline how GenAI tools are to be appropriately used for tasks such as brainstorming, consolidating points, suggesting ways to organise a structure or refine language, and highlight potential inaccuracies and misuse consequences. This is a necessary step because most of our students aspire to excel and secure a high-quality degree. It’s essential to encourage them to maintain responsibility and integrity in their academic endeavours.We recommend regular check-ins during assignments, requiring meetings with teaching staff to review progress and understanding. During these meetings, students must present their work-in-progress and answer questions, which can also be integrated into the assessment criteria. This ensures that students demonstrate their learning through the assignment.The integration of GenAI tools into assignments challenges us to improve our assessment strategies, and we should continuously educate ourselves about the capabilities and limitations of GenAI tools. As these tools evolve, our assessment practices must also adapt to maintain the integrity of our educational outcomes.As we continue to develop higher-quality and authentic assessments, we can learn to maximise the capabilities of GenAI tools in an environment that fosters engaged and innovative learning.&nbsp;Miriam Wun is senior educational developer at Singapore Institute of Technology.If you’d like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

The evolution of assignments in the age of generative AI

Instead of cracking down on AI use for written assignments, we just need to make our assessment smarter

Students have wasted no time in figuring out how to use generative artificial intelligence tools to help them in their studies. Need an essay outline? Done. Stuck on a tricky concept? Just ask a chatbot. Run out of ideas? Ask a GenAI tool for help. These tools are changing the way students learn, think and, more importantly, complete their assignments.&nbsp;This raises some big questions. Are students using it wisely? Where do we draw the line between “helpful assistant” and “academic shortcut”?&nbsp;Last year, we talked to students across a range of disciplines, on what they thought of using GenAI tools in their studies.Spotlight guide: Bringing GenAI into the university classroomKeep calm and carry on: ChatGPT doesn’t change a thing for academic integritySo, you want to use ChatGPT in the classroom this semester?Some of the ways they were using the tools, we discovered, were pretty useful. But some students felt that they were getting too reliant on technology. We asked them about how they crafted prompts, navigated assignments, tackled research and, yes, occasionally misused these tools. Here, we’ll outline our findings – and identify how educators can make the most of GenAI.Use of GenAI in learningStudents have been using prompts and GenAI tools in various ways to enhance their learning experience. They use it to formulate prompt questions with contextual knowledge, iterating multiple times to refine their queries. For many, GenAI tools are invaluable for summarising long studies or reports and saving time. Yet we heard from a few students who felt their learning was impeded if they used GenAI tools, and consciously tried to steer clear of using them.For many students, GenAI is deployed as a personal tutor on a perpetually opened browser to help answer the various specific questions that come up. It is especially so when they need an answer and cannot find time with the professor. GenAI tools have been instrumental in helping them understand complex topics, removing barriers to their learning. A favourite prompt for students is to use these few words at the start of the prompt: “Explain it to me like I am a 12-year-old…” It helps to simplify and rephrase unclear instructions or tutorial questions.&nbsp;To provide more intentional guidance to students’ learning, institutions can consider developing their own chatbot platforms, linked to reliable large language model providers via application programming interfaces. Faculty can then customise the chatbots with intentional guidance, and input documents to the chatbot’s knowledge base for greater relevance and specificity. Learner inputs and chatbot responses can be logged, then analysed to find out how our students are learning through the questions they ask. It is also an opportunity for faculty to review the learning materials and questions used in their lessons.&nbsp;Another activity we heard about&nbsp;from students&nbsp;is the use of GenAI tools in class as part of in-class discussions. Students can be asked to generate solutions to a specific problem or discover what the concept is about using the GenAI tools. Through the discussions in class and with critiquing the outputs provided by GenAI tools, students are able to learn, as well as be more discerning of the contents generated.Guidance in assignmentsGenAI tools have been invaluable in helping students to write reports, fix programming problems, correct grammar and generate an organised structure in assignments. This is how our students use GenAI tools to enhance their work. Students have mentioned how they use GenAI in generating the flow, outline and ideas for assignments, especially for those where the topic is difficult to grasp. In statistics or IT-related assignments, GenAI tools can help them to generate multiple functions or programming code, which will lead them to finding a working solution.&nbsp;Our students do also acknowledge that AI-generated ideas and content are often generic, that using its content can lead to similar answers among their peers. They find that they need to have even more original ideas to get an A grade, which highlights their depth of knowledge and creativity. They said that if they simply “cut and paste” with some tweaking, they get a minimal passing grade or even a failure. They also realised that they must find, credit and cite original sources.The first step to review our assessment is to imagine answering the question like students. Students will use various GenAI tools and iterate the process until they get a reasonable answer. Only then will we be able to find out the vulnerability of our assessment and how best to amend it.&nbsp;Next, we will need to reduce or completely remove the amount of marks allocated to grammar, structure and organisation. The assignment itself must require higher-order thinking or be context specific, and students are assessed on their original and critical thought. Projects are based on real-life situations, centred around industry partners and the problems they’re facing today. These partners can also provide feedback on the students’ proposed solutions later on.It is also advisable to remind students of repercussions for misuse of these GenAI tools. Most students would like to do well and would not want to jeopardise their degree with academic dishonesty or low-quality work.While most of our students find GenAI tools invaluable as a useful learning assistant, they are also keenly aware of its limitations. They seem to use it wisely enough but we need to discern and refine responses obtained from GenAI ourselves. The best way forward is to acknowledge their usage and provide guidance for strategically integrating GenAI into teaching and learning. With that, we can build on our students’ intrinsic motivation towards mastery and maximise their potential capabilities for leveraging GenAI.Miriam Wun is a senior educational developer and Nah Yong En is an educational technologist, both at Singapore Institute of Technology.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus&nbsp;newsletter.

An insider’s guide to how students use GenAI tools

There’s no doubt students are using GenAI in their assignments – but how? Miriam Wun and Nah Yong En talked to students to find out

Ask an Alum is our AI-led digital networking tool that connects students and alumni to seek advice. Through smart use of AI, Ask an Alum enables students to receive valuable career insights, creates opportunities for networking with our alumni community and facilitates building longer-term connections. Since its launch in October 2022, it has facilitated more than 3,700 connections between students, recent graduates and alumni.With 12,500 students booking more than 6,055 hours of career appointments annually and with a growing alumni community predicted to be at 300,000+ by 2030, we looked at new technology to scale engagement. Educate for Global Impact is at the forefront of our strategy, and Ask an Alum&nbsp;enables students to develop their networks with alumni across regions and sectors they otherwise may not have access to, gaining career advice and the opportunity to develop longer-term connections. With AI, we can support all students in this way, while engaging alumni with personalised volunteering opportunities relevant to their expertise.Being the first institution in Europe to implement this tool has come with challenges, and we’re still learning. So far, we have worked through:ImplementationLSE has robust processes to follow when implementing a brand-new tool. We needed to have stakeholders on board very early on in the process, including senior management and key divisions. This involved a partnership between LSE Philanthropy and Global Engagement (PAGE) and LSE Careers to lead on the proposal, with a vision that AI would transform the student experience, scale engagement with the growing global alumni population, create targeted engagement for prospects and donors and increase operational efficiency across LSE. As a new volunteering opportunity, this feeds into our Shaping the World Campaign volunteering target, and lines up prospects and donors.We use ChatGPT to give feedback on students’ abstractsTransforming marketing lectures with AI and simulationsCollaborating with artificial intelligence? Use your metacognitive skillsEngaging data protection and cyber security teams early on was essential, alongside key advocates who understood how transformational this could be. Working together to advocate for the impact it could have and identifying what the logistics were to help us get there made a real difference.LaunchA new AI tool cannot have impact unless key stakeholders across the institution have bought into it, understand how and why it works, and feel passionate about sharing it with their audiences. We brought together a working group at an early stage prior to launch, with colleagues from PAGE, Careers and Communications to plan how to share the new tool with our specific target audiences.Building a toolkit of resources that departmental colleagues could use in their own materials and meeting with them regularly to collaborate on new ideas has been essential in strengthening institution-wide collaboration and the tool’s impact on our students.&nbsp;EmbeddingWorking closely with our careers team, we have embedded the tool into careers activities – on social media and the website, careers consultant appointments, events and more. This has resulted in over 25 per cent of questions being referred from careers activities. PAGE promotes Ask an Alum&nbsp;across the year, with a focus on embedding within key events, such as “Welcome” to engage new students with the tool, and “Graduation” to encourage recent graduates to use Ask an Alum in their search for next steps.Earlier in 2024, we began embedding the tool into the curriculum, working with the course directors of LSE100, an interdisciplinary programme for all undergraduate students. This encouraged students to engage alumni as part of their early-stage research, asking them for their insights and expertise. Course teachers worked this into lessons and supported the groups to draft their thoughts into questions, helping them with their project work while building a foundation of confidence in engaging with alumni and building their networks from year one.GrowthTo encourage students to use the tool, we realised we needed to build their confidence both in making that first approach, and in how to frame their question. We put together guidance questions based on the topics discussed with our careers team, embedding these within the tool itself and sharing examples at events. Students mostly ask to connect with alumni to hear about their experiences working in an industry or company, about moving to a new city or starting conversations about their interests to build networks.We also started an Ask an Alum video series to showcase alumni sharing their advice. This put a real face to the AI and created rich content and a further volunteering opportunity for graduates around the world. It also shared student voices speaking about the value they got out of connections they made. Sharing impact stories is important, to show the outcome that these initial connections can have – from CV advice to job referrals and longstanding mentoring relationships. The tool not only enables career conversations but fosters a sense of belonging and community.“Getting advice from alumni makes me feel supported and like there’s a whole community out there that wants me to succeed,” said one undergraduate student.Future visionWe are constantly looking strategically at how the tool can improve the student experience. This involves engaging senior stakeholders to better understand need and where we can embed the tool to further support students. For example, we have rolled out use for student societies to connect with alumni for panel events and continue to explore new areas where AI can have positive impact. The tool also prompts feedback, and we have maintained a satisfaction score of 9 out of 10 since launch.AI is moving incredibly fast so we aim to keep on top of the topic and keep the conversation going – to have the freedom to explore how we may transform our programmes as our communities continue to grow.Wallis Spence is alumni engagement projects manager in the Philanthropic and Global Engagement Division at the London School of Economics and Political Science.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus&nbsp;newsletter.

Five key stages when embedding AI networking tools

The Ask an Alum AI tool is helping current students connect with alumni, develop networks and benefit from career advice. Here’s how to embed a digital networking tool 

Since the public release of ChatGPT-3 in November 2022, terms like “generative AI” and “large language models” have become commonplace among educators, students and even policymakers. While artificial intelligence as a concept has existed for over 70 years, it’s only now that its impact is becoming visible to the general public.&nbsp;Many of us have grappled with crafting policies to define appropriate use of AI tools in the classroom, worried about the inappropriate use of AI in assessment tasks and struggled to keep up with the pace of new AI technology and applications.&nbsp;Beyond Chat: how AI teaching assistants are transforming student supportSpotlight guide: Bringing GenAI into the university classroomHere are seven AI tools you should be using for your teaching and researchAs generative AI pioneer Ethan Mollick noted, “The AI genie is out of the bottle”, and there is no going back to the way things were pre-ChatGPT-3. We need to recognise that generative AI is here to stay in higher education. The question is no longer whether AI will influence education but rather how higher education institutions will adapt to this new reality.Building AI literacy competenceWe believe that effective incorporation of AI in higher education starts with building AI literacy competence for both students and educators. This includes but is not limited to understanding, applying and critically evaluating the use of AI in various settings. It does not matter whether you deal with computer programming or teach history to first-year students, you should have a basic understanding about how (Gen)AI works.&nbsp;One of the critical skills to acquire is prompt engineering. It might seem relatively easy to ask ChatGPT to produce a lesson plan, design a sequence of maths problems or generate a multiple-choice quiz. However, the devil is in the details – it is essential to refine your prompts so that the virtual magician pulls out exactly what you hoped for: a white rabbit with long ears or a black dove with grey wings, or anything else your “abracadabra” asked for. If the trick doesn’t quite work, you refine the spell until the perfect result emerges. The magic lies in crafting the right spell.Incorporating AI into assessment tasksUnderstanding AI is step one, but making it work for you in the classroom is where its potential truly unfolds. Educators have had to adjust assessments so that students can really practise their critical thinking skills without breaching the principles of ethical use and academic integrity. Here are four examples of how AI can easily be incorporated into assessments in a way that mitigates the risk of students using AI tools inappropriately, while helping to develop their critical thinking skills:AI versus manual brainstorming:&nbsp;Students could be asked to: (i) manually brainstorm ideas for a particular project or problem, for example, generating ideas for a new entrepreneurial venture; (ii) then use AI to generate ideas on the same topic; and (iii) evaluate the similarities and differences between the two brainstorming activities and their implications. By critically comparing the results, students would not only engage with AI, but also refine their critical thinking and evaluative skills. This exercise could also help to highlight the strengths and limitations of using AI to generate diverse and original ideas.AI-supported literature analysis:&nbsp;In this task, students could be first asked to use AI to summarise and comment on a scholarly article or piece of literature, such as Shakespeare’s Henry V. They would then critically evaluate the AI-generated response, focusing on factors such as accuracy, depth and interpretative quality. Once again, this would enable students to engage with AI tools, while at the same time helping them to develop a nuanced understanding of both the literature and the AI tools.AI as an expert adviser:&nbsp;Students could prompt AI to provide expert advice on a specific topic and then critically assess the quality of the advice – for example, asking AI to provide advice on a specific engineering problem. This activity could help students to identify errors, biases or oversights in AI-generated content and reinforce the importance of human judgement, expertise and ultimate responsibility.AI-generated presentations:&nbsp;In this exercise, students could be asked to use AI to create a presentation on a chosen topic. They would then present their topic, including an evaluation of the quality of the presentation generated by the AI, taking into account factors such as presentation design, accuracy, relevance and consistency. In addition to engaging with AI and helping to develop critical thinking skills, this task could also help to improve communication and presentation skills.Developing ‘next generation’ AI policiesThe last – and yet perhaps the most critical – consideration is developing forward-thinking policies. While understandable, given that ChatGPT was only introduced to the world in 2022, existing AI policies at many universities tend to focus on restrictive guidelines, detailing whether and how students and educators can use AI and in what contexts. However, such policies often fail to capitalise on AI’s capabilities to enhance learning.To address this, higher education institutions need to develop “next generation” (“Gen 2”) AI policies that also emphasise how AI should be used. These policies should aim to:Foster critical engagement: Encourage students to critically evaluate AI outputs rather than passively accept them.Promote ethical usage: Define responsible use of AI, addressing issues such as bias, transparency and academic integrity.Encourage creativity: Use AI tools to inspire and enhance creative problem-solving.Generative AI is here to stay and higher education institutions need to find ways of incorporating it effectively. By developing foundational AI literacy competence, reimagining assessments and adapting institutional policies, universities can turn potential challenges into opportunities.&nbsp;Aida Nuranova is a professional learning and development expert at the Center for Innovation in Learning and Teaching, and Timothy Wawn is an instructor at the Graduate School of Business, both at Nazarbayev University.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

The AI genie is out of the bottle – now what?

Generative AI is here to stay, so let’s build AI literacy, incorporate AI into assessment and craft solid policies for its use

The young tech start-up founders didn’t enjoy traditional lectures, they said, particularly when classrooms were half empty and students seemed disengaged. Instead, they learned much more through starting a tech business. We had been talking about teaching in the age of innovation and AI, but what this conversation at a start-up accelerator bootcamp highlights is a broader trend that affects even the most engaging universities: poor classroom attendance, disengaged students and declining enrolments.A disconnect has appeared between the way students live their digital, networked lives and the way they are expected to learn at university.Over the past 20 years, socio-technical phenomena such as the World Wide Web, smartphones, social media, pandemic-driven remote teaching, virtual reality (VR), extended reality (XR) and generative artificial intelligence (GenAI) have often seemed to pose a threat to traditional notions of teaching and learning.&nbsp;Resource collection: GenAI is here to stay, so what next?We should be using social media more to understand studentsSpotlight guide: How to serve the Covid generationIt is true that these technologies have transformed from whom students learn, with peer-produced videos, podcasts and social media posts often being preferred to unknown and unrelatable experts. They have redefined where students learn, with home, remote and distance learning replacing classrooms and campuses – as widely experienced during the Covid-19 pandemic. They have altered how students learn and are assessed, with traditional practices like reading textbooks, taking lecture notes and writing essays or code no longer the best approach in a world with GenAI (for example, Perplexity, DALL.E-3, Sora v2, GitHub Copilot, Canva, ChatGPT-4o or LumaLabs AI), advanced XR educational resources and the gamification of learning. They have even altered the way in which learning occurs, with social constructivist and cognitivist learning theories being challenged by theories that better account for the technological realities of the age, such as networked learning theory.It is also true that the many barriers to the adoption of technologies for learning – including human, financial, institutional, pedagogical and personal factors – are genuinely challenging to overcome, especially at speed.&nbsp;The gap between life and learning is clear: whoever we are and wherever we live, on average almost half our daily interactions (46 per cent) are with non-humans, including search engines, websites, forums or blogs, educational platforms, and social media, entertainment or gaming services, according to research from the early 2020s. We interact more often with our smartphones (34 per cent) than we do face-to-face (23 per cent). Many students have developed online side hustles to fund their way through university and develop skills better suited for modern work.&nbsp;Together these technologies and behaviours form their personal learning network – used to access, consume and find patterns in information; create and share ideas and experiences; build, maintain and use connections; earn money; and grow reputation. In truth, HE educators have similar networks that they use for comparable academic and career purposes. Students and educators have more in common than is often recognised, and acknowledging these similarities can foster stronger collaboration, mutual understanding and more effective learning environments.In contrast, even during the Covid-19 online teaching period, teaching practice did not reflect this networked reality, found research with UK HE educators. More than half of educators reported that they never used blogs (51 per cent); open educational resources (OERs) or massive open online courses (Moocs) (59 per cent); or social media (62 per cent). The vast majority of UK HE educators have never used XR for teaching and learning (90 per cent) and make less than daily use of smartphones for learning purposes (64 per cent). No wonder students feel increasingly disconnected from a largely “unnetworked” university experience.&nbsp;Today, XR applications enable advanced AI-driven simulations to teach soft skills, provide gamified immersive language learning, and support medical, mathematical and musical learning – among other uses. XR has been shown to increase student engagement and support experiential learning. Meanwhile, GenAI has been used by educators to rapidly create content that enables personalised, dynamic and remote learning. In the latest developments, the large language models on which GenAI systems are based are becoming more open, customisable and capable of being run locally rather than on large servers – giving students and educators ever more powerful and usable tools. Finally, the latest developments are seeing XR combined with GenAI to enable non-expert, prompt-led, bespoke XR content creation, and classrooms are being emptied of furniture (not students!) to create open spaces for safe XR use.So, the risk of student disconnect is ever-increasing as technology evolves. There is an urgent need to incorporate existing and emerging technology into module design, supporting networked learning and developing usable digital skills.Such module design might steer away from traditional lectures and conventional essays or exams towards network learning and assessment pedagogies, such as:A “blog-comment-reflect” three-weekly learning cycle – where students: explore a specific topic by producing properly researched and referenced multimedia content using text, image, code, video/audio and XR tools (acknowledging GenAI services where used) organised into an engaging and informative blog post (week 1); read, comment on and engage in an online discussion of the posts of at least three other classmates (week 2); and produce a concluding post detailing how their understanding of the topic has changed (or been reinforced) through those digital interactions with peers (week 3)The integration of Moocs and OERs as reference sources to support and guide students’ content creationWeekly voluntary virtual co-working sessions as a substitute for traditional drop-insGamification of student online self-assessment, where students evaluate their work against clear criteria (such as argumentation, research, multimedia content quality and effectiveness, peer engagement or reflection), earning badges or progress points for achievements&nbsp;Real-time educator feedback through collaborative tools on student self-assessment scores, fostering dialogue around performance and growthThe use of GenAI and XR content creation tools to deliver bespoke XR resources through on-campus sessions in open learning spaces filled with students in headsets rather than with empty desks and chairs.We suggest that if educators (demand and) receive the necessary training, equipment and facilities to support networked learning module design and the adoption of emerging technologies, the disconnect between life and learning might be reduced, useful skills developed and engagement with learning reinvigorated.Nicholas S. R. Fair is a web scientist and a senior knowledge engineer in electronics and computer science and&nbsp;Larisa Yarovaya is an associate professor in banking and finance, both at the University of Southampton.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Universities face an urgent need to accelerate change in how they teach – and think about teaching – to reconnect students’ digital lifestyles with the way they learn, write Nic Fair and Larisa Yarovaya 

Empty classrooms and disconnected students in the age of AI

It is important not to overlook core components when creating and delivering webinars. Here are some reminders of the basics to aid instructors in facilitating an online community of practice where learners feel engaged and inspired.Define the purpose of your webinar&nbsp;Before creating a webinar, ensure you define clear objectives and desired outcomes that focus on what students should learn or achieve to ensure relevant and targeted content design. For example, a learning goal might be for students to understand key theories of a discipline, while a broader, corresponding outcome could specify that they will be able to apply these theories in practical real-life scenarios.Set out the expectations of your webinar if these are different from your other sessions, eg, lectures or tutorials. It may also be necessary to clarify the different types of learning delivery. For instance, a tutorial may be linked to navigating the learning platform, addressing assignment queries and answering general questions. In contrast, webinars are strategically crafted to improve engagement with the learning content, exploring the application of theoretical concepts in practical scenarios. These interactive sessions incorporate targeted activities aimed at stimulating students and encouraging them to examine the course materials from diverse viewpoints. By fostering active engagement, webinars facilitate deeper understanding and retention of knowledge.Manage your webinar effectively &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Students need to feel safe and confident to fully participate, so to set them up for success, at the beginning of your webinar, reassure students if they haven’t yet managed to cover all the learning content. Also, remember to explain relevant terminology and acronyms.It is always prudent to reiterate general group rules about respect, confidentiality and psychological safety. Ideally, spend some time at the beginning of your module to co-create these group rules. One fundamental group rule is to ensure that all members treat each other with respect. This involves listening actively when others are speaking, valuing a range of opinions and refraining from interrupting or dismissing contributions.Be mindful of the time you have available and ensure you finish on time to ensure students have time for a break before their next activity. It can be helpful to acknowledge at the beginning of a webinar that at times you may need to move things on for the benefit of the whole group. Be ready to signpost participants to relevant sources of support such as student services and learning enhancement teams or module advisers when necessary. For example, a student may share personal experiences but struggle to shift their focus away from their own perspective, making it difficult for them to hear and engage with others’ viewpoints. In this situation, I gently reiterate the group rules, highlighting the benefits of hearing from everybody and being respectful of different perspectives. I always make a point to follow up with and check on a student’s well-being in these scenarios to ensure they are adequately supported.&nbsp;Spotlight: Enhance student engagementDo you speak Gen Z or are you a noob?The rise and rise of the lecture-tainerPromote interactive learning&nbsp;Interaction keeps participants engaged and connected during webinars. Use polls, quizzes and break-out rooms for discussions, and encourage questions throughout the session, rather than just at the end. Acknowledging participant contributions and thanking individual learners by name also helps. Also, allow time and space for students to anonymously ask questions or make comments at different points during the webinar.&nbsp;Interactive learning experiences are vital for maintaining engagement in a virtual setting. For example, you can use a mini quiz to check understanding of key concepts, or a poll to ask students whether they agree or disagree with an author’s viewpoint. You can also use polls to check in with your students about whether activities have been useful. Additionally, you can ask them to self-assess using self-rating scales to reflect on where they are versus where they want to get to. You can then ask them what they need to do to move to the next step and what they need from you to help them get there. Review feedback carefully. This data can provide valuable insights into what worked well and what could be improved for future sessions.Keep your audience connectedCheck in with everybody when you are ready to move on and acknowledge when things are hard. Real learning takes place when this is the case, so offer reassurance. It is usually helpful to reiterate that there is usually no single or simple answer to the issues we are exploring. It is also good practice to warn students ahead of supplying learning material that could be perceived as challenging or triggering.&nbsp;Bringing real-world experiences into the virtual classroom helps make learning relevant and relatable. Share personal anecdotes or invite guest speakers to provide insights related to the subject matter being taught. This brings it to life and provides a connection between theory and practice. For example, when teaching about leadership, I frequently draw upon examples from my clinical practice.&nbsp;Providing students with opportunities to bridge the gap between theory and practice significantly enhances academic skills. When these opportunities are paired with the chance to develop a community of practice and expand their professional networks, the real magic happens.Lauren Flannery&nbsp;is a lecturer in health sciences at the School of Health Sciences, University of East Anglia.&nbsp;If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

How to create and deliver effective educational webinars

Strategies that incorporate interactive elements and rules setting to help teachers design engaging webinars

Tips for creating and delivering effective educational webinars

Higher education is at a tipping point. Universities face mounting pressure to grow enrolment, cut costs and improve outcomes – all while navigating an increasingly complex financial and political landscape. The stakes are high but so are the opportunities for meaningful change.&nbsp;Bringing together leaders from across the US higher education sector, the University Network Summit 2025 – co-hosted by&nbsp;Noodle, a strategy, services and technology partner serving higher education, and&nbsp;Huron, a global professional services firm – aims to address these challenges head-on, sparking bold ideas and fostering impactful partnerships beyond traditional online programme management (OPM).&nbsp;Set against the dynamic backdrop of Washington, D.C., at Georgetown University’s Capitol Campus, the University Network Summit 2025 comes at a pivotal moment for transformational dialogue. A diverse list of distinguished speakers ranging from academic innovators to edtech entrepreneurs includes Michael L. Lomax, president and CEO of the United Negro College Fund, Van Ton-Quinlivan, CEO of Futuro Health,&nbsp;Rajagopal Echambadi, president of the Illinois Institute of Technology, and Chris Motz, chief partnerships officer at the University of Maryland Global Campus.&nbsp;Building on the success of its inaugural 2024 symposium, titled Exploring the Future of Higher Education Partnerships in the post-OPM Era, the 2025 event will explore how universities can collaborate at scale and work towards sustainable growth.&nbsp;The sessions will focus on four key themes: partnerships, recruitment, the whole student and policy.&nbsp;“The University Network Summit presents a unique opportunity to engage in deep dialogue about the critical issues facing colleges and universities and the transformative power of collaboration to advance their education, research, service and other mission priorities,” says Mark Finlan, education and research practice leader at Huron. “We are excited to bring leaders together to explore how cross-industry partnerships can help to position higher education institutions to thrive in an increasingly complex environment.”Unlike traditional conferences, the summit takes a participant-driven approach. Attendees’ feedback during registration directly shapes the sessions, ensuring every discussion is focused on solving the sector’s most pressing issues. The event will feature engaging workshops, networking opportunities and collaborative exercises to allow delegates to explore solutions together.&nbsp;The summit will enable industry leaders to connect higher education with the business world. At the same time, policymakers will get the chance to see how their policies align with societal and economic needs, and community builders will gain insights that feed into locally relevant and inclusive programmes. John Katzman, founder and CEO of Noodle, says that the summit’s mission is to foster collaboration through such partnerships, ultimately driving positive change in the higher education landscape.&nbsp;​​“Networks are the future of higher education and allow for innovation across institutions,” Katzman says. “The future of higher education is interconnected. By pooling resources, sharing data and forming strategic alliances, universities can cut costs, improve outcomes and adapt to a rapidly changing world.”The 2025 University Network Summit invites educators, industry leaders and policymakers to share innovative strategies and forward-thinking solutions to transform higher education in the US. The call for abstracts and proposals is open until 5 February 2025, giving attendees a chance to shape the agenda and ensure their priorities are front and centre.Register to participate in the University Network Summit 2025.

The summit will bring together higher education leaders in Washington, D.C. from 5 to 7 March, to tackle key issues in the sector head on, with an emphasis on collaboration and shared innovation

The University Network Summit 2025: shaping the future of higher education in the US

“It’s not just giving me answers. It’s helping me think through the problem step by step,” a student said as I watched them using an AI teaching assistant. They showed me how Syntea had guided them through a complex financial concept using the Socratic method. This moment crystallised something fundamental about AI in higher education: when implemented thoughtfully, it’s about not just providing information but enhancing the entire learning journey.The power of purposeful designLet’s start by distinguishing between two approaches to AI implementation in teaching. The first is what we might call the “generic AI” approach – where institutions simply adopt AI tools and hope for the best. The second is “pedagogically integrated AI” – where every interaction is part of a carefully designed learning journey, aligned with curriculum and teaching philosophy.Resource collection: What next for GenAI in higher education?Spotlight guide: Bringing GenAI into the university classroomCampus podcast: the future of XR and immersive learningThe difference is crucial. Generic AI is like having access to a vast library without guidance. Pedagogically integrated AI, on the other hand, is like having a knowledgeable teaching assistant who understands your course materials and learning objectives and, most importantly, how to guide you towards deeper understanding.Designing for deep learningThe most exciting aspect of our implementation has been seeing how thoughtfully designed AI can transform student engagement. Take our “deep dialogue learning” approach. Rather than simply answering questions, Syntea engages students in focused 10-minute dialogues that mirror the Socratic teaching method. Students don’t just receive information; they’re guided through a process of discovery that deepens their understanding.The architecture of supportOur experience has revealed three key elements that we believe make AI truly effective in education:Expert-led design: At LIBF, we ensure that every aspect of our teaching assistant Syntea’s interactions reflects our teaching expertise and curriculum standards. This means students receive consistently high-quality guidance that’s aligned with their learning objectives.Adaptive learning pathways: Our “orchestration” system personalises each student’s journey based on their progress and needs. We start with pre-assessment and adapt difficulty levels as students advance to ensure everyone receives appropriately challenging content.Integrated assessment: Regular knowledge checks and adaptive questioning help students identify areas for improvement before they become problematic. This proactive approach helps maintain steady progress throughout the course.Making it workFor institutions considering similar implementations, here are our key insights:Start with educational goals: Before diving into AI capabilities, clarify your teaching objectives. What aspects of student support could benefit most from 24/7 availability? How can AI complement your teaching methods?Build a strong foundation: Invest time in designing learning pathways and dialogues that reflect your teaching philosophy. This upfront investment pays dividends in student engagement and learning outcomes.Think holistically: Consider how AI fits into your broader learning ecosystem. At our institution, Syntea works alongside our other teaching tools and methods, creating a seamless student support system.Challenges with introducing AI into teachingIntegrating AI into higher education comes with its set of challenges. One of the primary hurdles is staff hesitancy. While many staff members saw the potential of Syntea, there was an initial concern about its role. Could it replace teaching staff? Could it genuinely understand complex student needs? To address this, we focused on open communication, explaining that Syntea is not about replacement but about augmentation – helping staff focus on high-value interactions while AI supports foundational learning.Another challenge was digital accessibility and literacy among students. With varying levels of comfort in using new technology, ensuring equal access was critical. At LIBF, we prioritised providing comprehensive training to ensure all students could engage confidently with Syntea. This involved workshops and digital literacy sessions as part of our onboarding process.What we did to overcome these challengesAs well as training resources for our students, it was important that we also trained staff and embedded AI tools into our processes to ease concerns and build confidence. For staff, this included professional development opportunities focused on understanding and leveraging AI tools in pedagogy. Additionally, Syntea is introduced during our staff induction programme.Our institution also established an AI community to foster collaboration and upskilling. This network supports staff in mastering AI and serves as a forum for sharing best practice and innovations. This approach has been pivotal in creating an environment where AI is seen as a partner in education and day-to-day working, rather than a disruptive force.By addressing these challenges head on, we’ve set a strong foundation for integrating AI as a transformative tool in higher education. This practical advice can serve as a guide for other institutions embarking on their AI journey.Looking forwardThe true potential of AI in education emerges when we move beyond viewing it as just a question-answering tool. Through careful implementation, we’ve seen how it can become an integral part of the learning journey, supporting students in ways that complement traditional teaching methods.Syntea also handles support tasks such as 24/7 questions about course materials or topics, while enabling our tutors to focus on more complex interactions and personalised guidance. It’s not about replacing human teaching but about extending our ability to support students effectively at every stage of their learning journey.Thorsten Fröhlich is a&nbsp; subject matter expert for&nbsp;IT and Technology programmes at LIBF.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus&nbsp;newsletter.

Pedagogically integrated AI is like having knowledgeable support that understands course materials and objectives and, most importantly, can guide students towards deeper learning, writes Thorsten Fröhlich

Beyond Chat: how AI teaching assistants are transforming student support

Since the advent of generative artificial intelligence, critics have raised concerns about the technology’s effect on traditional forms of art and its potential to replace man-made art completely. But AI’s fusion of tradition and cutting-edge technology could provide new ways to conserve art and culture, while also igniting interest in passing down heritage from generation to generation.In order to embrace generative AI as an innovative tool to expand artistic and cultural horizons, researchers from the Hong Kong Polytechnic University (PolyU) have developed a creative painting platform, “Beauty of Cantonese porcelain”, to preserve and educate people about the artistry of this legacy porcelain. The traditional handcrafted decorative porcelain showcases the distinct folk art of the Lingnan region, highlighting its intricate and refined painting techniques that embody the rich heritage and ingenuity of traditional Chinese art and craft culture.GenAI can help literature students think more criticallyApply the principles of critical pedagogy to GenAIFour steps for integrating generative AI in learning and teachingThe research team meticulously collected and processed thousands of images of Cantonese porcelain. These images were then used to build a robust generative model to make this cultural heritage more accessible and understandable.The platform has several key features:Text-to-image synthesisGenerative AI’s expansive text-to-image models are the main technology driving this platform’s ability to guide and generate traditional cultural content like Cantonese porcelain painting techniques.&nbsp;Style consistencyThe models can create images from text descriptions, which is essential for ensuring consistency with the traditional style. This innovative platform highlights several technological features, including few-shots fine-tuning, layered generation and symbol element generation.&nbsp;Few-shots&nbsp;fine-tuning: This technology allows pre-trained models to adapt to specific styles (such as Cantonese porcelain) using limited data, improving the model’s ability to generate images that match text descriptions.Layered generation:&nbsp;This innovation enables the creation of images with different visual elements created on separate layers, making them easier to edit and manipulate without affecting other elements.Symbol element generation: A module designed to identify and segment key symbolic elements from a set of images, ensuring that the language of symbolic painting is integrated into the generative AI process.2D and 3D painting integrationThe research team then integrated the concept of 2D and 3D painting into the platform, creating a user-friendly interface tailored especially for beginners.&nbsp;Interactive learning environmentThrough its intuitive design, this platform assists users in swiftly learning Cantonese porcelain painting techniques. Users are evaluated in real time by a large AI model, which provides them with personalised guidance to enhance their skills as they create artwork. The more intuitive user interface allows artists to interact more naturally with the platform, while professionals can explore the potential of generative AI to enrich cultural and artistic heritage.Localised controllable generationBy merging symbolic painting language with the advanced localised and controllable generation, the platform enables users to creatively express their artistic ideas in meticulous detail. This ensures that the artworks created closely adhere to the traditional Cantonese porcelain style, preserving and promoting its history and culture.Data-driven generative modelsUsing this technology to expand datasets would increase the variety and quantity of training data, allowing the model to generate a broader range of traditional content, by including more cultural products and styles.&nbsp;The “Beauty of Cantonese porcelain” platform demonstrates the transformative potential of generative AI in preserving cultural heritage. By combining advanced technology with a deep understanding of traditional art forms, the platform provides a powerful tool for education, preservation and artistic innovation.&nbsp;This innovative approach demonstrates the power of technology in protecting and promoting cultural treasures, so helping to bridge the gap between tradition and innovation.To strengthen this technology for broader artistic and cultural applications, we can:Expand the datasets: Increase the variety and quantity of training data to include more cultural products and styles, allowing the model to generate a wider range of traditional content.Improve user interaction: Develop a more intuitive user interface that allows artists and designers to interact more naturally with the platform, making it easier to guide the generation process. Create a platform for professionals to explore the potential of generative AI to enhance traditional cultural heritage crafts.Work with cultural institutions: Partner with museums and cultural preservation organisations to validate and improve this approach across a broad range of&nbsp;fields and art forms.Incorporate feedback mechanisms: Further enhance the multimodal large-scale models with a feedback system that provides insights into&nbsp;the generated content. This can then be used for additional training and refinement of the models.Generative AI can enhance human culture by preserving traditional art forms, fostering artistic innovation and improving accessibility to art-making. It aids in educational efforts and attracts new participants to intangible cultural heritage, while providing tools that require less technical skill. However, in order to maintain cultural values and artistic integrity, it’s essential we address ethical use, ensure transparency in AI processes and embed human oversight.Henry Duh is associate dean (global &amp; industry engagement) and professor of the School of Design at the Hong Kong Polytechnic University.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Harness the power of AI to preserve endangered art forms

Researchers breathed new life into Cantonese porcelain painting techniques using AI, equipping a new generation with traditional skills. Here’s how they did it

How can an institution cut through the noise and connect with students who have a world of options at their fingertips?&nbsp;Enter the power of interactive video.&nbsp;Recognising that institutions sell not just academic programmes but life-changing experiences, the University of Tasmania wanted to create a marketing campaign that immersed students in how it would feel to study on the picturesque Australian island. So, they partnered with video advertising technology company VDX.tv and media agency Pivotus to produce a digital experience that would both appeal to those willing to relocate to Tasmania and compete with institutions worldwide in attracting students who can choose to study with any institution from home.&nbsp;A digital gateway to campusThe marketing campaign, which ran from September 2023 to January 2024, sought to drive awareness and encourage applicants for the summer intake by showcasing the university’s courses and facilities in an eye-catching and engaging way. Interactive video ads gave prospective students an inside look at the academic strengths of the university and an introduction to the island, which sits 250km south of the Australian mainland. The ads allowed users to explore course offerings, learn about the application process and get a feel for the local lifestyle.&nbsp;Digital marketing as a route to reaching international studentsSpotlight guide: Marketing and branding that attract attentionSteps to building a winning online presence for your universityUnderstanding the critical role that family members play in the decision-making process, the university targeted the campaign at potential students as well as their parents. Third-party data audience segments were used to create a custom audience blend so that the campaign could accurately target high school students, parents with teenagers, and seekers of higher education.Delivered across multiple platforms – desktop, mobile and tablet – the video ads were strategically placed on websites that resonated with the interests of students and their families, making the messaging feel relevant and timely. This approach not only reached a wider audience but ensured that the right people were seeing the university’s message, increasing the likelihood of conversion from viewers to applicants.&nbsp;Creating a video campaign that surpasses expectationsThe University of Tasmania’s video campaign engaged its target audiences – school leavers,&nbsp;their families, and higher education seekers – effectively.Video completion rate was per cent 26 higher than the industry benchmark.&nbsp;Video completion rate is a metric that measures the percentage of viewers who watch a video from start to finish. It’s a key metric for evaluating the effectiveness of video ads and organic video content.Click-through rate was 150 per cent higher than the benchmark, driving significantly more traffic to the university’s website.Visitors spent 37 per cent more time exploring the university’s website compared with average benchmarks.These metrics highlight the campaign’s ability not only to capture attention but sustain it, helping students and their families explore the university’s offerings in more detail. The university saw these outcomes as more than just numbers; they reflected meaningful engagement from their target audience.How other universities can take actionThe success of this marketing campaign offers valuable lessons for other institutions looking to leverage video advertising to drive institution awareness and applicant consideration:Embrace interactivity: Static content such as simplistic banner ads doesn’t cut it any more. Prospective students want to explore, engage and interact with content. Video formats can offer interactive elements, such as clickable tabs or drop-downs, that provide additional information about courses, admissions and campus life. This keeps potential students engaged for longer and allows them to customise their experience.Target families, not just students: Don’t just design your messaging for students. Parents and guardians play a crucial role in influencing decisions about higher education, so campaigns should speak to their concerns and priorities, such as academic quality, safety and future career prospects.Tailor your content: Context matters. Use data-driven targeting (for example, behavioural, geo, contextual and demographic targeting) to ensure your video ads appear in environments where students and parents are likely to be, whether that’s on social media, streaming platforms or educational websites. Personalised content increases engagement and drives better results. You can work with an advertising agency or ad-tech vendor to ensure that your targeting and personalisation strategy align with your video campaign’s objectives and audiences. Showcase what makes you unique: Every university has something special to offer – whether it’s the campus culture, location or specific programmes. Make sure your campaign focuses on these distinct elements. For the University of Tasmania, the beautiful island environment and the focus of its programmes on the outdoors and nature sets it apart.With a combination of creativity and technology, universities can transform the way they engage with potential students, ensuring that their message resonates long after the ad has ended. The University of Tasmania’s use of interactive video advertising provides a model for success – showing how, with the right strategy, institutions can not only reach their audience but inspire them to take the next step.Courtney Geritz is director of marketing at the University of Tasmania.If you’d like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Marketing the campus experience to future students

When competition to attract students comes from other institutions, not just in your region but around the world, universities need effective marketing tools and a targeted message to stand out 

From island to screen: marketing the campus experience to future students

Several years ago, I wrote an article that, essentially, posed this question: “Are the benefits of artificial intelligence in higher education worth the unintended consequences?” I wasn’t suggesting it would be possible to hold back the next revolutionary force in digital technology. Instead, I described a series of specific shortcomings in AI-driven systems that most leaders of bricks-and-mortar academic institutions needed to be aware of as the next big AI challenge in higher education.&nbsp;That article reflected two facts: that higher education leaders were only beginning to wrestle with AI’s practical implications and emerging challenges, and that most of those leaders viewed AI as a series of discrete problems to be solved, concerns to be addressed. Today, I know that we need to apply a broader lens: viewing AI as both a multifaceted challenge for higher education broadly and a uniquely powerful opportunity to define our individual institutions’ futures.Future-proof software engineering students for an AI-dominated worldOpen dialogue about AI in HE is the way forwardHigher education needs a united approach to AIThat’s why, today, the question I’m asking is: How best can we proactively guide AI’s use in higher education and shape its impact on our students, faculty and institution? The answer to that broad, strategic question lies in pursuing four objectives that, I believe, are relevant for many colleges and universities.&nbsp;Objective 1: Ensuring that across our curriculum we are preparing today’s students to use AI in their careers – in particular, to add human skills value that enables them to succeed in parallel with employers’ expanded use of AI.Objective 2: Employing AI-based capacities to enhance the effectiveness (and therefore value) of the education we deliver to our students, strengthening their capacity for meaningful, lifelong learning.Objective 3: Leverage AI to address specific pedagogical and administrative challenges, ranging from improving learning outcomes to enhancing the effectiveness of recruiting strategies to managing deferred maintenance.&nbsp;Objective 4: Concretely address the already identified pitfalls and shortcomings of using AI in higher education, and develop mechanisms for anticipating and responding to emerging challenges.I’d add a fifth objective for universities that, like Thomas Jefferson University, are academic pillars for multifaceted organisations: we should leverage our growing AI-focused knowledge and technical capabilities in ways that benefit the broader institution. In our case – Jefferson is closely integrated with a large academic healthcare system and a non-profit health insurance plan – this is something we’re doing to advance both clinical care and biomedical research.To achieve those objectives, we must adopt two strategic attitudes that will be essential for universities –individually and collectively – to thrive in the dynamic 21st century.&nbsp;First, a pioneering attitude. We need to view AI as a tool at our disposal – one presenting grand opportunities – not just as a force to be reckoned with and a series of discrete problems to solve.&nbsp;Yes, opportunity involves risk, but the smartest pioneers find ways to mitigate that risk while moving forward. They find collaborators who bring capacities and resources to the table that effectively both spread and reduce the risk.The second is a partnering attitude. Of course we all want our organisations to succeed in perpetuity. Yet it would be a mistake to assume we can succeed independently and in isolation. Mergers are one obvious option, and the merger of Jefferson’s two legacy institutions in 2017 achieved unprecedented success. But there are many more ways for higher education institutions to realise the opportunities and mitigate the risks inherent in AI. We should be exploring formal and informal partnerships across institutions, sectors, industries, localities and nations. We should share more information and ideas, and more clearly acknowledge the problems we’re wrestling with.Adopting these strategic attitudes will enable us to&nbsp;guide AI’s impact and, ultimately, position higher education to be stronger and more resilient in decades to come. In coming months, I’m going to be reaching out to other pioneering- and partnering-minded university leaders, discussing how to work together to shape AI’s role and impact on our institutions.Susan Aldridge is president of Thomas Jefferson University.If you’d like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Four objectives to guide AI's impact on higher education

How can higher education leaders manage both the challenge and the opportunity artificial intelligence presents? Here are four objectives to guide the way

Four objectives to guide artificial intelligence’s impact on higher education

While the concept of lifelong learning may seem like a recent trend, it has been discussed for over a century. A&nbsp;1919 report by the UK’s Adult Education Committee emphasised adult education as a lifelong necessity. However, it wasn’t until the Covid-19 pandemic that the critical need for reskilling and upskilling in the workforce became strikingly evident – and&nbsp;tied to a strong need for digital transformation within universities and further education institutions.&nbsp;&nbsp;Though&nbsp;virtual learning environments (VLEs) have become more prevalent in teaching and learning worldwide, universities face pressure to provide lifelong learning opportunities and serve a broader student population.Modernising for a lifelong learning cultureOnly a few years ago, modernisation in education was limited to video conferencing tools that support distance learning. Today, it means having robust SaaS (software as a service) technology that facilitates continuous learning beyond formal education, including professional development,&nbsp;microcredentials and mobile learning in a secure, flexible and scalable way.To modernise, institutions must integrate innovative tools, platforms and open educational resources so learners can seamlessly access diverse content and enjoy varied learning experiences.&nbsp;Lifelong learning relies on personalisationNew technologies such as generative AI are changing job descriptions and the skills that employers demand. This means traditional career paths are becoming less predictable and individuals need to take control of their learning journeys to remain relevant and competitive in the workforce.&nbsp;Today’s learners are looking for a personalised experience and adaptive learning options from institutions. They have unique goals, backgrounds, learning preferences and multiple responsibilities; furthermore, they are mobile.Having the right technology and tools to offer adaptive learning paths, tailored content recommendations and individualised feedback has become imperative for universities to effectively support the personalised, self-directed learning pathways central to lifelong learning.&nbsp;There is no personalisation without data-driven insightsAI has helped evolve the way educators and administrators can use learner data to track progress, engagement and outcomes.&nbsp;With sophisticated and easy-to-use AI-powered data tools, personalising education is no longer a futuristic idea but a reality that those with a robust SaaS-based VLE could offer to learners at any level.Nowadays, educators don’t need to be experts to generate analytics reports. New conversational AI features embedded in cloud-based VLEs can generate impactful, actionable reports within minutes, identify which students need extra support, evaluate course readiness and understand learning tools interoperability.By leveraging data-informed decisions, institutions can foster growth, increase retention and enhance the learning experience. AI-driven tools play a key role in this process by analysing data, market trends and student interests, enabling institutions to suggest new course offerings and programmes tailored to student demands. This is key to helping lifelong learners meet the needs of a constantly changing job market and for institutions to increase revenue opportunities.&nbsp;The lifelong learner demands flexibilityFlexibility takes on new significance when it comes to supporting lifelong learning.&nbsp;It goes hand in hand with learner mobility and the evolution of a dynamic global workforce, where recognition and certification of skills add another level of complexity to universities’ challenges.The non-traditional learner requires access to various learning models, such as hybrid learning environments, self-paced courses, microcredentials and continuous professional development. Moreover, they need all of that to be available from their mobile phones to access and participate in learning activities on the go, even when the internet is unavailable.&nbsp;To offer this kind of flexibility, universities and further education institutions need robust VLEs that seamlessly allow them to work with other institutions, locally and globally.Regardless of the challenges imposed by new policies on international students, students and universities have significant opportunities thanks to online and hybrid models that support transnational learning, university alliances and institutional cross-collaboration, all facilitated by VLEs.Evolving from self-hosted to cloud-based systemsFor decades, the concept of a free, self-hosted VLE has enabled many institutions to stay afloat. However, the rapid advancements in emerging technologies and the continuous evolution of SaaS-based VLEs are widening the gap between institutions trying to survive and those driving innovation.This digital divide among universities disadvantages students and educators, exacerbating educational inequalities. From limited access to course content during updates or maintenance on self-hosted platforms to a heavy reliance on plug-ins for features that modern SaaS platforms integrate seamlessly, the challenges are clear. Learners deserve – and are demanding – better technologies from their institutions. Addressing these issues demands a cultural shift within universities and a commitment to embracing innovation and fostering a technology-driven, lifelong learning environment.Find out more about Canvas by Instructure.

The adoption of education technology and generative AI have significantly changed what a virtual learning environment should look like

Are universities ready to support technology-driven lifelong learning?

In today’s software engineering landscape, where technology is progressing at an unprecedented pace, educational institutions must focus on preparing students for the challenges they will face in the workplace.&nbsp;My software engineering students will soon be entering a field reshaped by generative AI, cloud computing and a host of other new tools and technologies.&nbsp;These AI tools can help prepare future programmers for the workplaceThe two key steps to promoting responsible use of LLMsA four-step process to embedding AI literacy in business coursesThese advancements promise to make software development more exciting and efficient, but they also pose significant ethical and technical challenges.As a result, my approach to teaching software engineering has evolved to address three key areas:&nbsp;Equipping students to use AI effectively as an assistive technologyInstilling ethical and professional practices to their workFostering direct interaction between students and industry professionalsThe demands of teaching software engineering today extend far beyond simply teaching coding or design patterns. As the role of generative AI continues to expand, students need to understand how it can be used as a powerful tool, while recognising its limitations.&nbsp;Many students are now entering the classroom with an expectation that AI can solve all of their programming problems, or serve as a shortcut to navigating complex coding challenges.&nbsp;However, as I emphasise in my classes, relying too heavily on AI tools without understanding the underlying processes can lead to very real-world problems, particularly in cases where AI produces errors, introduces vulnerabilities or fails to meet up to project specifications.Generative AI is still very much an assistive technology with transformative potential, and it should be used with caution and competence.&nbsp;The critical skills that students need in this area, such as debugging, troubleshooting and understanding system requirements, cannot be outsourced to AI.&nbsp;My teaching focuses on helping students develop these skills independently, while also introducing generative AI as a complement to their work, rather than a replacement. For example, students are encouraged to use AI for tasks like code suggestions or documentation generation, but they are also required to review and refine the AI’s output to ensure accuracy and relevance.Integrating industry into the classroomOne of the most effective ways to prepare students for the realities of software engineering is to bring industry insights directly into the classroom.&nbsp;Most recently, we have partnered with multinational industry professionals to create project-based learning experiences that mirror real-world challenges.&nbsp;Class projects were guided by software architects and IT experts, who mentor the students at every stage, from design through to implementation and testing. This hands-on experience is invaluable, especially for Gen Z students, who are adept with technology but don’t necessarily understand the complexities of the software development lifecycle.By working alongside these industry mentors, students gain practical insights that textbooks cannot provide. They learn to work under certain constraints, receive constructive feedback and witness the direct application of software engineering principles in a professional context.&nbsp;Students were tasked with developing solutions for real-world challenges, such as creating a system to help companies screen CVs and identify profiles to match their needs, while others worked on providing insights into electricity consumption based on energy bills.&nbsp;In other projects, students focused on improving the university library’s management of book reservations. These projects were guided by industry experts familiar with the technical requirements and available Application Programming Interfaces, enabling students to integrate and use these resources to deliver effective solutions for end users.This experience gave students the necessary technical skills, along with a genuine understanding of how to translate user needs into functional software.The ethical and professional use of AIAnother important aspect of my teaching approach has been to emphasise the ethical and professional use of generative AI.&nbsp;While AI can undoubtedly enhance productivity, it also introduces various risks, like the misuse of code, unexpected security vulnerabilities or even intellectual property issues.&nbsp;Many organisations impose strict regulations on the use of AI due to these concerns and students should be prepared to navigate such regulatory environments.To address these issues, our curriculum includes discussions on the ethical implications of generative AI in software engineering. We explore case studies where AI has been used responsibly, as well as instances of misuse and accompanying problems.&nbsp;As well as learning about the technical aspects of AI, students also explore the importance of transparency, data security and accountability. By discussing these issues in the classroom, I hope to instil a professional standard that students will carry with them into their careers.We make students aware of industry-specific regulations governing AI usage. In fields such as healthcare and finance, for example, the improper use of AI can have serious consequences.&nbsp;By understanding these regulations early on, students are better prepared to make responsible choices and question AI outputs that may otherwise be biased or inaccurate. This balanced approach to AI use is essential in preparing students to work in an industry where ethical considerations are increasingly integral to technical proficiency.The value of working with industry professionalsDirect interaction with industry is a cornerstone of our approach at Al Akhawayn University. For Gen Z students, who may be comfortable with technology but lack experience in professional settings, this exposure has proved to be invaluable.&nbsp;Working with industry mentors allows students to receive feedback that they can leverage as they begin their careers.For industry professionals, the collaboration offers a unique opportunity to shape the next generation of software engineers. Many of the experts we work with in Morocco say that they gain as much from these exchanges as the students do. For them, it’s a connection to emerging academic perspectives, providing a sharp glimpse into how the upcoming workforce thinks about technology.&nbsp;This also enables professionals to directly contribute to and help shape the development of their future employees, who are then better prepared to meet industry expectations.Future-proofing Gen Z engineersAs the influence of AI continues to grow, our responsibility as educators is to equip students with both technical skills and a solid understanding of ethical considerations and industry expectations.&nbsp;The software engineering field will continue to be shaped by generative AI, but it will also always need engineers who can understand and address complex challenges independently. We’re successfully fostering this balance by integrating industry experts and ethical discussions into the curriculum.Houda Chakiri is assistant professor of computer science at Al-Akhawayn University.&nbsp;If you’d like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Future-proof software engineering students for a world of AI

Software engineering is increasingly being shaped by generative AI. Here’s how to prepare your students for their future workplace

Future-proof software engineering students for an AI-dominated world

GenAI is arguably the most disruptive technological advancement we have seen in higher education. The speed of content generation is transformational, fuelling concerns about an existential threat to teaching and learning. Universities have attempted to restrict the use of AI through guidelines and detection software. But the way in which these futile attempts have been so quickly abandoned speaks volumes.So, what choice are we left with? Do we redouble our efforts to rein in this rampaging monster? Or do we reconceptualise the beast as a powerful vehicle that will carry us into a future of vastly expanded possibilities?AI is part of our future, and it is a part of our students’ futures. AI hardware supplier Nvidia’s share price alone shows the mind-bending scale of investment in the technology and infrastructure. It is incumbent upon us to find a way to embrace it for our students’ sakes.&nbsp;In this resource, we outline our advice for implementing an approach that opens AI use up to our students through a strategy of assessing the process rather than outputs.To start with, we recommend identifying learning outcomes for your students that can be achieved in collaboration with AI. For example, “demonstrating the skills required to effectively search for and analyse the existing literature” and “the ability to prepare an extended document”. Undoubtedly, these are tasks AI could complete in seconds. However, we encourage you to not give up – in fact, see that there are learning opportunities aplenty. The rapid generation of content shifts the learning dynamic, positioning students as reviewers in control of output. To do this, students need to develop across multiple areas: their AI literacy, their topic knowledge, and their critical appraisal skills. It is the learning and development in this context that we recommend is captured within the assessment tasks.Resource collection: AI transformers like ChatGPT are here, so what next?Promoting ethical and responsible use of GenAI toolsSpotlight guide: Bringing GenAI into the university classroomOur suggested framework for building AI literacy in a literature review-type assessment is as follows:Step 1 – Build experience: the process of critically appraising AI output is too complex if the field is unknown or the content is unfamiliar. Students generate AI output that reviews something they are already an expert in – their favourite book, film or TV series, for example – that they then critically appraise.Step 2 – Apply and upskill: students (working in groups) generate a literature review on an assigned topic. Students will learn with their peers how to push the AI and develop skills in effective prompting – encourage experimentation and a play-orientated approach.Step 3 – Check attainment:&nbsp;students submit a summative (individual) critical appraisal of the group-generated output. This task requires a deeper understanding of the topic and provides an impetus for students to research and read into the subject. Students will recognise their personal developments in AI literacy and critical appraisal skills.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Step 4 – Optional continuation: students prepare their final literature review (with supporting information). Allow them to decide what works for them: AI use is optional and without restriction. The supporting information document contains all prompts used and a 500-word reflective account of the process employed – it will document their development in AI literacy and give insight into their academic experiences with AI.&nbsp;These assessments lend themselves to the use of a senate scale marking scheme. Careful selection of marking criteria aligned with the desired learning outcomes for each element of the assessment is key to differentiating the expectations for each part of the process. The focus of the critical appraisal scheme is much more on the process. Criteria include:&nbsp;search strategyexclusion and inclusion criteriaquality of included studiessynthesis and creativity.These all speak to the process the student goes through to find and evaluate content. The focus of the final submission can then be much more output-focused, with sections assessing structure, formatting and writing style. &nbsp;We have run an assessment in this way, and it has been a hugely positive experience. Students fed back that they felt they had developed their AI literacy as well as their critical appraisal and scientific writing skills. Many expressed satisfaction that they had begun developing AI use as a future life skill. This came after we experienced a surprising amount of inertia at the start of the term. From the instructor’s perspective, the AI tools gave us access to tasks and methods to develop skills relevant to scientific writing, but in a completely different way. It was a lot of fun as well. And (as anthropologists will tell you) there’s no better way to learn than to play.&nbsp;Acknowledgement – our process was inspired by a workshop from Liz Alvey from the University of Sheffield.Paul McDermott, Leoni Palmer and Rosemary Norton are associate professors at the University of East Anglia.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Assessing the GenAI process, not the output

A framework for building AI literacy in a literature-review-type assessment 

Generative AI has given students new opportunities to engage dynamically with subject material. Tools such as ChatGPT have the potential to not only assist in their understanding but also significantly strengthen learning during classroom activities. Even as the education landscape evolves, fostering critical thinking remains an essential priority for university teachers, enabling students to evaluate, adapt and apply information effectively.&nbsp;I have previously explored ways that ChatGPT can enhance students’ critical thinking, focusing on its use for problem-solving, creative brainstorming and debate preparation. This article focuses on three practical ways to use ChatGPT in the classroom to reinforce student learning, highlighting methods to boost comprehension, retention and engagement in diverse educational settings.1. Practise scenarios with ChatGPTReal-life scenarios often provide the best opportunities for learning, as they encourage students to apply knowledge in a practical context. ChatGPT can simulate interactive, scenario-based exercises tailored to specific disciplines, making this approach particularly effective for classes that aim to strengthen applied skills.Resource collection: AI transformers like ChatGPT are here, so what next?Spotlight guide: AI and assessment in higher educationSpotlight guide: Bringing GenAI into the university classroomHow the exercise works:&nbsp;The teacher presents a scenario relevant to the subject matter – for example, ordering coffee in an Italian restaurant (for a language class) or diagnosing a hypothetical patient based on their symptoms (for a medical class). The teacher can provide a structured prompt for all students to use in their ChatGPT screens. Students interact with the GenAI, which acts as a participant in the given scenario (for example, an Italian-speaking waiter or a virtual patient). Students type their responses or questions into ChatGPT and receive real-time feedback or follow-up prompts, creating a dynamic and engaging learning experience. Teachers can guide the session either by moderating the scenarios and providing additional feedback to ensure that the learning objectives are met, or by asking students after their conversation with ChatGPT to discuss an aspect of their interaction based on their own critical appraisal (“What was your diagnosis for your virtual patient?” for the medical class example).Advantages:&nbsp;Encourages active engagement and participationProvides instant feedback in a simulated real-world settingReinforces theoretical knowledge through practical applicationPromotes creativity and problem-solving&nbsp;Considerations:&nbsp;Requires careful design of the scenarios to align with learning goalsRisk of students becoming overly dependent on ChatGPT for responsesTime constraints in class may limit the scope of scenarios2. Structure Q&amp;A sessions for conceptual understandingInteractive Q&amp;A sessions using ChatGPT can enable students to explore complex topics, receive immediate answers and so deepen conceptual understanding. This method encourages curiosity and critical enquiry in a guided classroom environment.How the exercise works: Students generate questions or use questions from the teacher, related to a topic in class. Using ChatGPT, students input their questions and analyse the AI-generated responses. The teacher sets boundaries for the Q&amp;A session (such as a time limitation) and encourages students to refine and critically evaluate the answers. For instance, in a physics class, students might ask ChatGPT about applications of Newton’s laws in real-world scenarios and the teacher then facilitates a discussion to review and expand on the AI’s responses, ensuring conceptual clarity and addressing inaccuracies.Advantages:&nbsp;Promotes independent enquiry and critical thinkingProvides immediate feedback and diverse perspectives on questionsEncourages active learning and engagement with the materialReduces the teacher’s immediate workload in large classroomsConsiderations:&nbsp;Requires teacher guidance to ensure accurate interpretation of AI-generated responsesPotential for students to misinterpret answers without reviewLimited scope for addressing highly nuanced or context-specific queries3. Concept mapping and knowledge organisationOrganising knowledge visually can help students understand complex relationships between concepts. GenAI tools can assist students in creating content for concept maps, making them a valuable aid for strengthening learning during and after class.How the exercise works: In class, students focus on a sub-topic of what was taught and use ChatGPT to identify key concepts, definitions and relationships. For instance, in a biology class, students might want to explore the components of the immune system and their interactions. They then critically appraise this AI-produced information and transform it into a visual concept map using visual tools such as PowerPoint. The teacher can review the generated content in class and encourage students to evaluate the connections critically. Students can then create a larger, more comprehensive concept map as homework, incorporating the day’s full material or expanding into neighbouring concepts.Advantages:&nbsp;Encourages synthesis and organisation of knowledgeStrengthens understanding of relationships among conceptsProvides a visual aid for memory and recallPromotes active learning and critical evaluationConsiderations:&nbsp;In-class time constraints may limit the scope of mapsRequires students to translate ChatGPT’s text-based responses into visual formatsRisk of students over-relying on AI-generated content without critical assessmentBy incorporating these three strategies, educators can effectively use ChatGPT to strengthen learning during classroom activities. These approaches not only engage students in dynamic and interactive ways but also reinforce key skills such as critical thinking, conceptual understanding and knowledge organisation. As with any technological tool, success lies in balancing AI’s potential with the educator’s expertise, ensuring that it complements traditional teaching methods while enriching the learning experience.Nikolas Dietis is an assistant professor of pharmacology in the Medical School of the University of Cyprus.If you would like advice and insight from academics and university staff delivered directly to your inbox each week,&nbsp;sign up for the Campus&nbsp;newsletter.

Class exercises that use ChatGPT to boost students’ learning

To foster engagement, comprehension and knowledge retention in the classroom, educators should find a balance between leveraging AI tools such as GenAI to strengthen learning while also preserving their own guiding role

Class exercises that use ChatGPT to strengthen students’ learning 

Rewind to March 2020. Within weeks, many of us transformed our engagement with digital tools. We went from being universities with limited online curricular and pedagogical presence to offering many of our classes online; we went from paper-based approaches to scheduling, admissions, or financial management to managing that work digitally; and in many cases, we shifted from print materials to using open education resources.&nbsp;In our rush to adapt, we often settled for “good enough for most people” instead of focusing on the inclusion and equity consequences of the changes we implemented. Sure, we tried to provide laptops for students who could not afford them, but we couldn’t easily control the myriad apps or software that professors would use in their courses (and whether those would be accessible on whatever single device a student might have). We picked online platforms that would quickly get us teaching and learning online, but we didn’t always make sure assistive technology would work correctly or offer easy alternative options. We also couldn’t easily address the digital dead zones many of our students and staff faced because of inadequate digital architecture in our cities, towns, provinces, states and countries.Advice on digital strategy in higher educationResources on equity in higher educationBuilding equitable learning pathways in higher educationThis is the time to embrace digital equity. There are many great institutions that have risen to the call for inclusion in their use of digital technologies. We have a long way to go at Dalhousie University, but we are trying to step up to the challenge. Here are five suggestions to consider if you’re keen to join us in this work.1. Just-in-time learning. Becoming competent in digital literacy is like any other form of learning. Most people need some external input for it to be relevant and to stick. In 2020, when many of our institutions offered training, people took it and they put what they learned to work immediately, leading to longer-term recollection. We need to accept a “ready to wear” approach to digital learning. Adopting new learning technologies and digital tools for work should be mandatory, and upskilling and re-skilling modules for staff and students need to be readily available the moment one of those members of our communities needs to use the technologies for the first time.2. Less is more. The proliferation of tools can be a resource challenge for staff and students. Most universities realistically cannot support multiple learning systems, polling apps, laptop computers and classroom scheduling systems. From 2020 to 2022 it was a victory to have our communities move to online learning and working. But now that we have had some time to regroup, it makes sense to narrow our institutionally-supported programs and hardware. Most students will tell you that one of their greatest digital irritants is that every class relies on different digital tools, some of which they cannot access on whatever device they have purchased. IT staff will tell you that they cannot effectively support and troubleshoot for a community that works on 75 different types of hardware or software platforms. We should prioritise choices that facilitate accessibility.&nbsp;3. Wi-fi and broadband. It’s expensive. But ensuring that our communities can get effective wi-fi in the places where they work and study matters. And we need to be advocates with our governments to ensure that broadband enables people to access learning and opportunities that come with education and collaboration, no matter where they live. It is essential to advocate for broadband as a driver of economic growth and to encourage partnerships with communications companies to ensure full network coverage.4. Curricular reform. Digital literacy is a core competency, yet we have been slow to integrate it into most of our programmes. That literacy includes not just technical skills, but critical thinking about digital tools (including artificial intelligence), ethical uses of technology and information literacy to ensure our community knows how to recognise and research credible information.5. Open sources. Most controversially, perhaps, universities should be developing an open-source strategy. Many of us are publicly-funded universities, but the research we produce or the materials we rely on in our teaching are expensive to access. That cost creates a bottleneck to dissemination and transformation, as well as to learning and accessibility. There are myriad ways to take concrete steps. For instance, small grants can enable faculty to co-develop open educational resources with students, creating immediate impact and providing valuable research employment. Or, working through existing higher education industry groups in libraries, information technology and teaching and learning can spread materials and lead to drastic improvements in this area. Look for the pioneers who want to lead in this space – others will follow!Universities are places where we provide knowledge, wisdom, connections and leadership to address global challenges and create new possibilities. Technology and digital access are essential for fulfilling this mandate. When every student and staff member has equitable access to digital tools, resources, relevant materials and skills that they need for success, regardless of their socio-economic or geographic status or their physical ability, we will fully fulfil our roles as inclusive institutions.&nbsp;Kim Brooks is president and vice-chancellor, Jody Couch is the chief information officer, and Ben Tait is the executive director for the Centre for Learning and Teaching at Dalhousie University.&nbsp;If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

It’s time to embrace digital equity

Ensuring digital equity at your institution requires prioritising accessibility and championing digital literacy, among other initiatives

Plagiarism as a concept is relatively recent, and its definitions have evolved over time. It is widely known that Shakespeare, for example, took whole passages from earlier works and included them in his own writing. However, we do not generally accuse him of academic misconduct.&nbsp;AI tools are here to stay and will only become more sophisticated. It is futile to try to ban their use to protect academic integrity. We already accept the submission of some AI work, but now find it increasingly difficult to define what is acceptable and what is not. The concept of “post-plagiarism” first emerged in Sarah Eaton’s book Plagiarism in Higher Education and offers a way to move beyond defining plagiarism, focusing instead on ethical learning. As educators, we must therefore learn how to teach and assess in a world where descriptions of academic conduct have changed.Eaton’s tenets of post-plagiarism&nbsp;Hybrid human and AI writing will become normal: there is little, if any, writing being created now that does not have some input from AI. Every time I begin to type, an AI tool is there to “help” me, whether in the form of auto-correct, spelling and grammar check, or any other tool. Written content being created now is likely generated, at least in part, using AI tools.Human creativity is enhanced: AI can be a stimulus to expand our thinking further. There are issues here, though. AI is “trained” using the creativity of others and its content often lacks attribution. Creative artists are concerned that their work is being hijacked, or that the tools can produce output that renders many creative roles obsolete. There is also a very real fear that the convenience of AI may make us lazy.&nbsp;Language barriers disappear: AI is the new Babel fish, a fictional species in Douglas Adams’ The Hitchhiker’s Guide to the Galaxy that is inserted into the ear and translates any language into the first language of the wearer. I delivered a session to international students recently as part of their induction week. One table had a tablet that was translating everything I said, as I said it. Last year those students would likely not have understood me, requiring repetition and delaying delivery; this time they were immediately up to speed with what I said and the tasks at hand.&nbsp;But who is checking the accuracy of the translations? With any translation there is potentially inherent bias, whether intentional or not. Much has been written about bias in AI, and we could be at risk of further entrenching it by relying on these tools.Humans can relinquish control but not responsibility: when we allow AI to produce content for us, we relinquish control and the ideas cannot be called our own. However, when that piece of work is disseminated, in whatever way, we are still responsible for its production. We, as human “authors”, must be responsible for what is published in our name.&nbsp;Historical definitions of plagiarism no longer apply: as discussed above, plagiarism was a concept for its time, but that time is past. We need to develop a new way to assess our students’ work. The use of AI tools can be seen on a continuum, from “no AI input” through to “entirely AI generated”. While as a community we try to establish where ethical use sits on that continuum, the world moves on, and AI is everywhere.&nbsp;Attribution remains important: in the post-plagiarism era, we need to support students to both develop and demonstrate their academic integrity. Attribution is a key element of that process. GenAI is another source, and as such must be properly attributed. A declaration form can help with this.Collection: AI transformers like ChatGPT are here, so what next?Promoting ethical and responsible use of GenAI toolsOpen dialogue about AI in HE is the way forwardSpotlight guide: Bringing GenAI into the university classroomImplications for assessmentStudents will not simply wait for us to come up with an “acceptable” way to integrate AI into their work. Just as some of our colleagues, and many in other industries, are doing, they will already be using AI tools.&nbsp;When we attempt to “police” AI use, and punish students for its detected use, we are punishing the students we are committed to supporting; the underrepresented students, those with less disposable income to subscribe to the best tools and those whose language skills do not allow them to edit the AI output to make it less recognisable. A punitive strategy is not the way forward.&nbsp;&nbsp;How can we assess in a post-plagiarism era?David Carless suggests that there are three purposes of assessment:To support student learningBy working with students as partners to develop assessment rubrics and to self- and peer-assess, we are supporting learning in both the topic and assessment process.Use AI-generated responses as a learning tool. Academics can input their assessment task to the GenAI tool during a session and then share the output with students for critique.&nbsp;Assess the process, not the product. When we come to accept that hybrid work will be the norm, it becomes important to find a way to assess a student’s thought process: the steps they have taken to direct the tool they have used.To judge the quality of student learningDevelop assessment that supports academic integrity. This can be done by:Requiring drafts of work with reflections throughout the learning process.Using portfolios where students demonstrate their learning journeys.Performance-based assessments such as presentations, hand-crafted posters, artwork, musical performances, prototype building, etc.To satisfy the demands of accountabilityWe need to be able to demonstrate that the assessment we deploy meets the intended learning outcomes of our module or programme, and that we have incorporated measures to ensure academic rigour.&nbsp;Karen Kenny is a senior educator developer at the University of Exeter.&nbsp;If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Rethinking academic integrity in a ‘post-plagiarism era'

What is the future of plagiarism as a concept in the AI age and what are the implications for academic integrity? This resource seeks to answer these questions, among others

We have to rethink academic integrity in a ‘post-plagiarism era’ 

Humanities graduates are expected to demonstrate critical thinking skills when navigating the world of work. However, as generative artificial intelligence (GenAI ) use in higher education continues to rise, so do concerns that tools such as ChatGPT are diminishing students’ critical thinking abilities. This resource aims to debunk this idea.&nbsp;From fear to integrationAt the beginning of last semester, I overheard a conversation between two students, one of whom said, “I don’t want to read the entire novel. I’ll just use ChatGPT for the analysis.” This hit me hard as an educator who is passionate about literature. The idea that students were choosing convenience over critical engagement was disappointing and prompted me to ask the head of the department to impose harsher penalties for GenAI use.Many educators believe tools like ChatGPT offer students a way to cheat, and as such, support either a complete ban or more stringent regulation. Universities are enhancing the use of&nbsp;detection tools and implementing severe penalties to serve as a deterrent.Advice on AI and assessment in higher educationSpotlight guide: Bringing GenAI into the university classroomCan academics tell the difference between AI-generated and human-authored content?It’s reasonable to worry that generative GenAI will impede students’ intellectual growth, particularly when it comes to critical thinking. However, if we are consumed by this fear, we will be blind to its potential. ChatGPT is here to stay. Its functionality will most likely improve, making it even more appealing to students in the months and years to come. That is why we need to shift our focus to look at GenAI as a tool to enhance student engagement, stimulate curiosity and invite students to conduct deeper inquiries.&nbsp;From shortcuts to critical engagement&nbsp;A few weeks after hearing my student’s comment, during a tutorial for my English literary and cultural criticism course, I asked others to generate a prompt using ChatGPT in reference to Jane Austin’s Pride and Prejudice. One prompt they used was: “How are gender and power dynamics represented in the novel?”&nbsp;When ChatGPT produced a few interpretations, students were initially impressed. They then engaged in critical dialogue about the AI's responses and, as we continued to discuss them as a class, started to question their depth. Following this, I asked them to think about how we could dig deeper.This prompted students to discuss how to achieve a more comprehensive analysis, bearing in mind the fact that ChatGPT’s response had not sufficiently addressed the complexity of the protagonist’s internal conflict caused by the clash between society’s expectations of her and her own desires. During that class, we demonstrated GenAI’s power as a discussion starter rather than a shortcut.Another way to help students use GenAI to improve their critical thinking is to guide students to assess the reliability and biases of AI-generated responses, a skill that is especially useful in an era of (mis)information overload. To facilitate this, I prompted ChatGPT to provide an analysis of female independence, one of the novel’s main themes. The prompt I used was: “Discuss how Pride and Prejudice appears to champion female independence but ultimately reinforces traditional gender roles and societal expectations when Elizabeth decides to marry Mr Darcy to provide her family with financial security.” I then asked students to write a rebuttal based on the response.&nbsp;Another exercise involves asking ChatGPT to generate two sides to an argument. For instance, ask ChatGPT: “Provide two contrasting arguments: one that highlights how Elizabeth in Pride and Prejudice asserts her independence, and another that emphasises how she remains reliant on others due to the constraints of societal expectations.” After that, students must assess both arguments and select one side. They then write an essay outlining their points of agreement and disagreement.&nbsp;Guiding students away from using ChatGPT as a shortcut can create a sense of agency and empower them to engage with technology in a way that allows them to take ownership of their learning. It can also help them recognise that contributing original thought and creativity is more important than merely saving time. Classroom activities should aim to assist students in assessing how they can deepen their learning using GenAI. This way, it stops being a tool that sparks concern, and instead becomes a powerful learning partner, ultimately making us and our students better critical thinkers.&nbsp;Shuri Mariasih Gietty Tambunan is a lecturer in the English studies programme at the University of Indonesia’s faculty of humanities.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

GenAI can help literature students think more critically

Is ChatGPT destroying critical thinking, or is it allowing us to reconsider how we teach it? This resource explores some ways to empower literature students to use it to deepen their understanding

Guidance to help universities benefit from digital technology, harnessing AI, edtech and other digital tools to improve teaching, research and more

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