Workshopping AI in higher education with students

I’m thinking about contributing to a workshop activity that involves students working on practical issues related to the implementation of AI-based services in higher education. Here are some ideas that I think might be worth exploring.

• Student-led AI policy development: This would sit alongside institutional policies and guidelines but would be context-specific and developed in collaboration with their lecturers.
• Student-led AI prompt development: Students explore and share examples of good / useful prompts with each other. Since there are no ‘correct’ prompts and every context is different, trying to collate a collection of prompts centrally won’t work because not all prompts are relevant for all students. We can provide a basic framework e.g. PAIR, or Goal, Role, and Instruction.
• AI ethics discussion: Small groups are given an ethical dilemma involving AI (e.g. use of facial recognition for attendance records / campus security, biased algorithms that disadvantage non-English first language students, privacy concerns with institutional AI systems). They discuss the pros and cons, stakeholder perspectives, and propose guidelines or solutions.
• Identifying AI bias: Groups are shown examples of biased AI (e.g. in recruitment and admissions, facial analysis, chatbots) and asked to reflect on how it could impact the student experience. Participants propose ways to address it.
• AI for accessibility: Groups learn about AI innovations that promote accessibility and inclusion (e.g. visual assistance apps, speech recognition, translation tools, cultural competence / convergence) and brainstorm new ideas to improve campus accessibility.
• AI personas and journey-mapping: Groups create “personas” of different students and map their journey, identifying how AI could customise and enhance support services to them throughout the student lifecycle.
• AI debate: Groups debate controversial uses of AI in higher education settings (e.g. virtual proctoring, chatbots for mental health support, automated admissions decisions). They assume and argue different perspectives.
• Critical thinking about AI claims: Groups critically assess outlandish or misleading claims about real-world AI applications and propose thoughtful counterpoints on limitations and ethical considerations.