Our January HE AI community meetup followed a lean-coffee format, with participants proposing and voting on topics they most wanted to explore. The discussion focused on how institutions are navigating artificial intelligence (AI) use in assessment, staff and student training, and emerging questions around feedback and AI literacy.
Several themes stood out, reflecting ongoing sector uncertainty as institutions balance ethical concerns, practical constraints and growing expectations about AI literacy.
Required AI use within assessment design
The top-voted discussion explored how institutions are mandating the use of AI by students in assessment. Participants shared examples of integrating AI into specific assessment tasks, often designed to develop students’ critical AI literacy.
A recurring theme was the importance of choice and transparency. Participants distinguished between opting out of using AI tools and opting out of AI literacy. While some supported alternative routes for students with ethical or privacy objections, several contributors emphasised that students may still need to demonstrate understanding of AI’s role, risks and limitations — including the ability to articulate why they have chosen not to use particular tools. Others noted that full opt-out may become harder as AI becomes increasingly embedded in everyday digital systems — and that growing workplace expectations could increase pressure for baseline AI literacy.
Compulsory AI literacy training for staff and students
Another popular topic focused on whether AI literacy training should be compulsory, and if so, for whom. Participants shared a range of institutional approaches, from mandatory baseline training for new staff to strongly recommended short courses covering data protection, bias, hallucinations and appropriate use.
There was broad agreement that AI literacy is not the same as requiring staff to use AI. Instead, contributors emphasised the importance of ensuring staff understand when not to use AI, alongside the risks and limitations of common tools. Several colleagues observed that students are often more willing to engage than staff, but that students also take cues from staff confidence and clarity.
Participants noted the risk of overwhelming staff with new requirements, particularly when AI training is added alongside existing mandatory courses. This raised questions about how AI literacy can be introduced in ways that feel manageable and purposeful, rather than just another compliance exercise.
Prompt training and staff confidence
Prompt training emerged as a practical focus, especially for colleagues who feel unsure about how to start using conversational AI tools. Participants shared examples of workshops that demystify prompting, emphasising context-setting, iteration and critical evaluation, rather than technical techniques often called “prompt engineering”.
Several contributors noted that even experienced users often underestimate how much difference structure and context can make to outputs. Others highlighted the value of prompt training in helping staff understand the limitations of AI tools, including where they produce plausible but incorrect responses. The discussion also touched on sustainability, with some suggesting that effective prompting may reduce unnecessary repeated use of AI tools.
Student attitudes to AI-supported feedback
The final discussion explored student perceptions of AI use in marking and feedback. Participants shared emerging evidence from institutional surveys and pilots, alongside reflections from smaller focus groups. While some students expressed concerns about value for money and access to academic expertise, others saw AI-supported feedback as more consistent and potentially less biased.
Colleagues stressed the importance of human oversight and transparency, noting that students’ work remains their intellectual property and that feedback processes must remain accountable. Several participants highlighted the need for clearer sector-wide insight into student attitudes, with interest in shared survey tools or frameworks to support comparability across institutions.
Closing thoughts
January’s discussion highlighted the different ways institutions are responding as AI becomes more embedded. Conversations around assessment, training, prompting and feedback reflected ongoing questions about choice, proportionality and AI adoption.
The next HE AI Community meetup will take place at 3.30 pm on Tuesday, 17th February. We look forward to continuing the conversation.
Links shared during the call
Jisc Student perceptions of AI 2025 report
- Jisc AI literacy training blog
- MIT Media Lab – Your Brain on ChatGPT
- Evaluating Trust in AI, Human, and Co-produced Feedback Among Undergraduate Students (Zhang et al., 2025)
- Jisc AI in marking and feedback pilot – good practice case studies (Kirklees College) blog
- Jisc AI in marking and feedback pilot – good practice case studies (London South Bank University) blog
- Digitally Enhanced Education webinar series – University of Kent
- Higher education sleepwalking into an AGI-sized hole? – Simon Brookes (LinkedIn)
- My AI experience – looking for conversations – David Callaghan (LinkedIn)
Find out more by visiting our Artificial Intelligence page to explore publications and resources, learn more about our communities and sign up for our AI Literacy training.
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Get in touch with the team directly at AI@jisc.ac.uk