A ‘buffet’ model of faculty development to support evolving use of AI

AI is not a single fixed tool but rather a rapidly evolving set of technologies that are changing university teaching and learning. Different models are opening new possibilities while also raising important educational, ethical and practical questions about the nature of learning and assessment. Faculty and students use AI differently across disciplines, courses and learning contexts. Even within the same course, various AI tools are used for different tasks and learning goals. 

A “buffet” approach recognises this and gives staff and students multiple pathways for responsible and meaningful engagement. Instead of relying on rigid, one-size-fits-all policies, the model provides flexible, context-based support that encourages responsible and effective AI use. It provides faculty and students with a high degree of choice in guidance, resources and learning with AI tools, allowing them to select the opportunities that best match their role, discipline, confidence and experience.

Our experience shows that faculty and students gain confidence and competence in using AI over time through repeated engagement, practical work and dialogue with colleagues. So, faculty development should be viewed as an agile, ongoing process, not a one-time event, helping educators update their teaching, assessment and curriculum as AI technologies and educational practices change.

The buffet model of AI skills development

Numerous AI capability development programmes for faculty and students are offered through the Centre for Teaching, Learning and Technology (CTLT). Support includes university-wide workshops on responsible AI, as well as on-demand workshops and consultations tailored to the needs of faculties, individuals, departments and disciplines. The CTLT also facilitates an open community of practice where faculty share experiences engaging with AI tools, challenges and opportunities. Individual mentoring programmes that support course redesign, assessment, research and AI integration are also available upon request. 

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Within the “buffet” model, an instructor can organise guest lectures on AI literacy, responsible AI use and discipline-specific applications through the centre. The university has guidelines on appropriate use of AI in learning for undergraduate students, while graduate students receive guidance on the responsible use of AI in research and thesis writing. The guidelines are demystified through workshops and one-to-one consultation and mentorship. I believe that the AI support buffet model helps faculty and students build confidence and competence in using AI responsibly over time.

The model continues to evolve in response to faculty and student feedback, institutional priorities, advances in AI technologies, changing teaching and research needs, and developments in academic integrity.

With clear guidance, flexible learning opportunities and practical support, it builds institutional capability, and promotes the responsible and ethical use of AI.

Flexible governance to keep pace with AI evolution

As technologies and educational practices change, rigid policies quickly become outdated. Instead, universities should adopt flexible governance, provide clear guidance and invest in ongoing professional learning that reflects a breadth of AI experience. Again, the buffet approach offers a way forward. Regularly reviewing AI tools, use cases and support will help institutions respond responsibly to change while promoting innovation, academic integrity, and high-quality teaching and learning.

For teaching and learning centres, the priority is to empower faculty and students to use AI responsibly rather than police its use. Their role is to build faculty and students’ AI confidence and competence through workshops, mentoring, communities of practice and discipline-specific examples. Capability develops over time, and centres for teaching and learning need to work closely with the provost’s office, deans, academic departments, graduate studies, IT, libraries and student success or academic success centres to ensure AI guidance remains relevant, consistent and responsive. 

The units responsible for providing support and oversight on AI can develop policies, resources and professional learning that support the responsible use of AI in teaching, learning, research, and assessment. IT evaluates and supports approved AI tools, libraries provide guidance on AI literacy, information literacy, copyright and citation, while student success centres help students develop responsible AI practices and effective learning strategies. Graduate studies support the responsible use of AI in research and supervision. Regular collaboration and feedback from faculty and students enable the university to continuously review and update AI guidance as technologies, policies and educational needs evolve.

Implementing a buffet support approach to AI faculty development

• Design AI literacy and competence programmes for flexibility, not one-size-fits-all. 
• Use clear principles, not rigid rules. 
• Move from single enforced policies to coordinated support. 
• Enable context-specific access to AI use across disciplines. 
• Provide mentoring for AI course and assessment redesign where necessary. 
• Share practical, discipline-relevant examples in responsible use of AI.
• Create space for reflection and feedback to faculty and students on challenges and opportunities of using AI for teaching and learning.
• Continuously adapt faculty and student support programmes to evolving AI use.

Ben Kei Daniel is professor of artificial intelligence in education, research methodologies, and educational technologies, and the director of the Centre for Teaching and Learning at the University of Northern British Columbia, Canada.

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