Boosting AI literacy across your institution

AI is now ubiquitous in the workplace, with 88 per cent of organisations using these tools. To remain competitive in a precarious job market, graduates must develop the kind of “human skills” that machines cannot replicate but, equally importantly, they must also understand how to use AI to complement their own abilities. 

GenAI and other tools now automate much of the work that was once delegated to entry-level roles and are increasingly used as collaborators for more complex professional tasks. The key to quality outputs, as AI experts will affirm, is effective human oversight. And this is where AI literacy, alongside technical and disciplinary knowledge, becomes vital.

AI literacy goes far beyond basic prompt writing. It encompasses an understanding of where AI can most effectively enhance human work, knowledge of the way the many tools function, an awareness of AI’s weaknesses and limitations, and the implications for ownership and ethical constraints of using it. This spotlight explores how universities can best develop AI literacy among students and staff to ensure a responsible approach to AI use now and in the future.

Build AI literacy as the antidote to fear in higher education

Varying attitudes to AI among faculty means uneven adoption across institutions, as early adopters evangelise about its merits while others refuse to engage. This has a knock-on effect for student outcomes. A culture of curiosity, trust and experimentation can help overcome lecturers’ fear and suspicion, leading to improved AI literacy for all, as these resources explain.

Why your AI training programme won’t create an AI-literate university: Five system-level strategies for building AI literacy, with guidance for both early-stage adopters and digitally mature institutions, by Pavana Kiranmai Chepuri from Woxsen University.

AI literacy is the bridge between fear and the graduates we need: We need to meet the digital revolution with curiosity rather than fear, and AI literacy is the way forward. Phil Laufenberg from La Trobe University outlines how.

Why AI adoption in universities is really a question of trust: To encourage AI use that aligns with institutional policies, university governance must prioritise transparency, usability and academic autonomy. A team at the University of Warwick offers guidance.

How to get AI training right for staff

Professional development and peer learning opportunities for university staff, done well, can advance institution-wide AI literacy. These resources offer insight on what to include in short courses for educators and how to centre peer voices to promote learning about AI. 

GenAI practice blossoms through the open exchange of insights: How a structured GenAI professional development series, built around practice, peer voices and multiple entry points, fosters open exchange among colleagues, universities and industry, by Samuel Doherty at the University of Newcastle, Australia.

Why AI literacy must come before policy: When developing rules and guidelines around the uses of AI, the first question to ask is whether university policymakers and the staff responsible for implementing them truly understand how learners can meet the expectations they set, write Kathryn MacCallum and David Parsons from the University of Canterbury | Te Whare Wānanga o Waitaha.

What university educators should know before using AI for teaching

For educators, it can be tempting to experiment with as many AI tools as possible. But a more intentional approach will better support teaching, assessment, course design and admin. These resources explore ways to understand the capabilities of different AI tools, the best ones to use, and when to avoid AI entirely.

How to choose the right AI tools for teaching: Educators are not always aware of the implications of using the latest shiny AI tool. Laura Milne from the University of Chester offers guidance on balancing educational value with institutional priorities.

AI for feedback – what to keep in mind when developing your own tool: Building an AI tool in-house allows institutions to retain control in a landscape increasingly dominated by external solutions. Academics from the University of Warwick explain how to involve students, uphold ethical standards and navigate resistance.

Only a human will do: when to eschew AI in teaching and research: Artificial intelligence tools have streamlined processes and accelerated innovations – efficiencies not lost on higher education. But, at times, we need to prioritise human judgement and involvement, as Qin Zhu from Virginia Tech explains.

An AI toolkit for all aspects of academic life: Harness the power of technology to reshape the tasks that make up your day. Urbi Ghosh from Colorado State University Global lists the best AI tools to use in higher education.

Reduce your teaching admin burden with AI: University teachers can use AI to respond to student enquiries, provide feedback and create engaging learning content. A team at the University of Auckland offer tips.

Help students understand when and how to use AI for maximum benefit

By encouraging students to reflect on their interactions with AI tools and setting clear parameters around appropriate use, educators can promote mindful engagement with AI and deeper learning. Explore strategies in these resources. 

Reaching for the pizza? How to help students make informed choices with GenAI: How intentional are your students in their GenAI use? Guide them to be more mindful using these strategies from Jenny Moffett at RCSI University of Medicine and Health Sciences.

Show students what thoughtful engagement with GenAI looks like: Face it: students are going to use GenAI whether we ban it or not. Let’s support them to use it purposefully and curiously, writes Walaa Awad from Colorado State University Global.

Conversations with bots: teaching students how – and when – to use GenAI for academic writing: A four-step process teaches students how to use GenAI tools to brainstorm ideas, understand and act on feedback, and edit their essays in line with assessment rubrics. Joseph Tinsley and Huimin He from Xi’an Jiaotong-Liverpool University explain how to implement it. 

Turn AI from a magic box into a tool to be interrogated: Instead of “ban it or embrace it”, the AI collaboration toolkit offers a way for educators to reframe AI, with ethical guard rails built in and an emphasis on students’ thinking. Lucy Gill-Simmen and Will Shüler from Royal Holloway, University of London detail how it works.

A scaffolded approach to teaching with GenAI: This four-phase framework, by Rena Beatrice Alcalay from Technical University of Munich, offers educators ways to guide students to use AI tools critically and ethically, fostering agency, bias awareness and deeper engagement in philosophical writing assignments.

Help students manage the risks and limitations of AI tools

AI-generated content, delivered with an authoritative tone, can lull even a discerning user into a false sense of security. What can follow is the acceptance of inaccurate information, biased perspectives and poor-quality work. These resources offer effective ways to teach students about the flaws, risks and limitations of using AI tools and to critique its outputs.

The gap between facts and understanding: helping students confront AI’s failings: Students often accept AI output without questioning it. Designing assignments where the tool fails – visibly and meaningfully – can change that, as Jan Burzlaff from Cornell University explains.

‘If you like, I can…?’ Why GenAI needs to come with a health warning: Warnings about the dependency-forming tricks of GenAI are unlikely to change student behaviour. Educators need to help students recognise engagement loops for themselves, writes Adrian Wallbank from Oxford Brookes University.

How AI is quietly distorting academic enquiry – and what to do about it: If students rely on AI summaries as starting points – or substitutes for enquiry – they risk bypassing the processes higher education is designed to cultivate: comparison, evaluation and critical analysis, argues Cayce Myers from Virginia Tech.

Three ways to develop students’ AI literacy: Is higher education prepared for a future defined by AI, or do we need to do more to align education with technology’s changing landscape? Academics from King’s College London and Edinburgh Napier University outline three ways to get students to engage with it critically.

Inoculating students against AI-generated scientific misinformation: GenAI raises an urgent pedagogical question for universities: how can we train students to evaluate scientific claims critically when the language of scholarship can be so convincingly simulated? Elissar Gerges from Zayed University offers advice.

(Re)learning critical reading in the age of GenAI: Rather than pretending students can – or even should – avoid GenAI to become critical readers, we need to develop their critical reading skills so they can successfully interrogate AI-produced materials. Brendan Carey from the University of Exeter explains how.

Defining and developing students’ critical AI literacy: With the lines between AI use and original work increasingly blurred, academia now needs to teach students how to use the tools critically. Amy Allen and David Hicks from Virginia Tech provide strategies. 

AI prompt design as a future skill

The ability to craft AI prompts that, among other things, clearly dictate the role of an AI tool and the parameters that it must follow can mean the difference between irrelevant or inaccurate results and those that more closely match the brief. Learn frameworks that leave little room for error.

A research-led approach to teaching GenAI prompt design: Strategies to transform student interactions with GenAI from one-line questions to robust prompts that yield reliable results and improve critical thinking skills, from Mogeeb A. A. Mosleh of the University of Science and Technology (Yemen).

Prompt engineering as academic skill: a model for effective ChatGPT interactions: Gathering information from AI requires a new layer of search skills that includes constructing effective prompts and critically navigating and evaluating outputs. Adrian J. Wallbank from Oxford Brookes University explains how.

Discipline-specific AI training that boosts employability

Discipline-specific demands mean that an engineering, literature or medical student will each engage differently with AI. Institution-wide AI literacy programmes may not highlight these nuances. Read on for advice about teaching the sector-specific AI skills students will need in the workplace.

Why AI literacy must be discipline specific: A one-size-fits-all approach to AI training risks leaving students unprepared for the discipline-specific demands of their future careers. Rose Luckin from UCL explores what field-specific AI literacy looks like in practice.

A four-step process to embedding AI literacy in business courses: Business students will need to know how to work with AI tools in their future careers. Prepare them with this four-step process outlined by John Murphy at Adelaide University.

Essential GenAI skills for marketing students: How students can use AI to generate promotional copy, conduct market research and identify biases, with activities to try from a team of academics at the University of Bristol.

What does AI literacy in healthcare look like? Done well, AI literacy helps students remain safe, critical, communicative and accountable in environments where digital systems are becoming more visible, writes Andy Barker from the University of Hull. Learn strategies to embed it in healthcare education.

Three ways to promote critical engagement with GenAI: However much we fear AI’s impact or despise its outputs, when teaching humanities, the best response is to encourage students to engage with it critically. Read guidance from Neville Morley at the University of Exeter.

In AI-enabled healthcare education, critical thinking comes first: As technology shapes the future of healthcare, how can we embed the skills tomorrow’s medical staff will need? Find out how from Dara Cassidy at RCSI University of Medicine and Health Sciences.

The university library’s role in building AI literacy

Library staff expertise can be invaluable for building AI literacy. Read advice on how to upskill library professionals so they can better support staff and students on AI-related matters, along with examples of how libraries can help improve institutional AI knowledge.

Is AI literacy an information skill? To capitalise on GenAI’s strengths, and understand its limitations, students need to develop their research and critical thinking skills in practical, embedded and subject-specific ways. Emily Dott and Terry Charlton from Newcastle University explain how.

Five steps to embed GenAI literacy for university librarians: The library can be the perfect place to promote GenAI literacy, not only for students but for academics, too. These five steps offered by Liliana González from Universidad del Caribe (UNICARIBE) can help.

AI skills for tomorrow’s university librarians: University librarians need to understand how to guide students and faculty in using AI tools ethically and effectively. In the future, they will not only be information experts but AI facilitators. Yinlin Chen from Virginia Tech provides advice on laying the groundwork.

Build information literacy with AI: a teaching librarian approach: Teach students to use AI appropriately for research tasks by showing them the tools’ strengths and limitations and by promoting critical reflection, says Callum Perry from the University of East Anglia.

Bridge AI literacy gaps to level the playing field

Uneven knowledge and experience of and access to AI tools can slow progress towards AI literacy. Learn how to ensure no student is left behind.

Why GenAI helps some students but not others (and what to do about it): GenAI can boost learning on average, according to research – but individual outcomes vary widely. Oguz A. Acar from King’s College London explains how to ensure every student benefits.

How to support students to learn from, with, about and beyond AI: Educators should not be competing with chatbots and large language models. Instead, a continuum can help them guide students from passive learning from AI to synthesising information alongside it. Read advice from Tan Seng Chee of Nanyang Technological University.

How students’ GenAI skills and reflection affect assignment instructions: The ability to use GenAI is akin to time management or other learning skills that students need practice to master. Here, Vincent Spezzo and Ilya Gokhman from Georgia Tech’s Center for 21st Century Universities offer tips to make sure instructions land equally, no matter students’ level of AI experience.

Why AI literacy belongs in the first-year experience: Embedding AI literacy early ensures every student gains essential understanding of systems, ethics and responsible use, closing gaps left by optional or uneven provision. Leocadia I. Zak from Agnes Scott College explains how.

Students told us what GenAI guidance works. Here’s their advice: Students are using GenAI, whether you’ve addressed it or not. Tom Ritchie and Yanyan Li from the University of Warwick offer advice on providing the clarity they’re asking for.

Why clear GenAI guidance matters to neurodivergent students. And how to get it right: Neurodivergent students often experience heightened anxiety when GenAI expectations vary across courses. Jayne Quoiani from the University of Edinburgh explains how to design more consistent guidance.

Beyond prompts: deeper AI engagement for academics

With greater understanding of AI comes greater potential to improve academic workflows, support innovative research and devise pedagogical interventions that respond to students’ changing needs. Read advice for moving beyond basic technical understanding to a more profound awareness of AI’s capabilities, limitations and ethical implications.

Are you a jack of all GenAI? Effective use of GenAI 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. Staff from the Commonwealth Scientific and Industrial Research Organisation offer insight on how.

Vibe coding belongs in your university’s GenAI literacy strategy: Most AI training teaches staff to use tools. A team from Hong Kong Baptist University provide a practical case for teaching them to build – and what they learned from doing so.

‘Your bot of choice is not a filing cabinet’: With GenAI taking a larger role in research and education, Sorin Krammer from the University of Southampton looks at the data management habits academics can no longer ignore.

Ethical AI: safeguards for the research revolution: AI can accelerate discovery in ways humans alone cannot. For Virginia Tech’s Hongliang Xin, the key is pairing AI’s power with ethical safeguards, institutional governance and responsible oversight.

Safeguards against GenAI hallucination in literature reviews: GenAI can speed up literature reviews but it can also produce convincing false references. Academics at Xi’an Jiaotong-Liverpool University share practical strategies for using AI tools while keeping the work anchored in verifiable research.

Thank you to all Campus contributors who shared their expertise in this guide.

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