June HE AI community meetup - Artificial intelligence

Our June HE AI Community Meetup marked the final session before the summer break and used a slightly different format in response to a community suggestion. Alongside new discussion topics, we revisited topics that had been proposed throughout the academic year but had not previously received enough votes to make it into the conversation, giving members another opportunity to prioritise them for discussion.

As always, the community selected topics through voting. While the discussion covered a range of issues, a recurring theme emerged throughout the session: as AI becomes increasingly embedded in higher education, attention is shifting from learning how to use AI tools towards developing the judgement, criticality and institutional approaches needed to use them effectively.

What AI capabilities and literacies should students and staff now be expected to develop?

The most popular topic centred on AI literacy and what institutions should now expect from both staff and students. Participants discussed the relationship between AI literacy and broader digital literacy, with several contributors arguing that AI literacy should not be viewed in isolation from wider digital capabilities.

There was discussion around the idea of critical AI literacy. Members suggested that AI literacy is moving beyond learning how to write prompts and towards understanding how AI systems work, recognising their limitations, evaluating outputs critically, and making informed decisions about when and how AI should be used.

Participants also highlighted the importance of metacognition and critical thinking. Rather than focusing solely on whether AI can complete a task, discussion explored how students and staff can make thoughtful decisions about AI use, including when AI may be beneficial, when it may be inappropriate, and how its use can affect learning and professional practice.

How are institutions embedding AI literacy into programmes and support services?

The conversation then turned to how institutions are embedding AI literacy into teaching and student support. Members shared examples of AI literacy being incorporated into employability initiatives, careers activities, digital capability frameworks and institutional skills programmes.

Examples included the use of digital capability tools, AI literacy activities embedded within modules, and approaches that encourage students to engage with AI within disciplinary and professional contexts. Participants generally agreed that AI literacy is most meaningful when connected to subject-specific learning, professional practice and authentic tasks rather than treated as a standalone topic.

Members also discussed the use of digital capability and AI competency frameworks, including the Jisc Discovery Tool and AI question sets, as ways to support conversations about AI literacy among students and staff and identify areas for further development.

Several examples also reflected a broader shift towards developing students’ ability to critically evaluate and apply AI within their chosen disciplines, rather than focusing solely on technical proficiency with AI tools.

What are the cognitive impacts of AI on learning?

The community shared emerging research relating to AI and learning. Participants discussed recent studies examining critical thinking, cognitive effort and the effects of AI-assisted work,. This included discussion of a recently retracted study that had suggested ChatGPT could improve learning and higher-order thinking. Its retraction prompted discussion about the challenges of building a robust evidence base for AI and how institutions should interpret emerging research findings.

Discussion highlighted the challenges of researching rapidly evolving technologies, with participants noting that by the time studies are completed and published, the capabilities of AI systems may have changed significantly. This led to wider reflection on how institutions should interpret emerging evidence while remaining responsive to ongoing developments.

A recurring theme was the relationship between existing knowledge and effective AI use. Several contributors suggested that students with stronger subject knowledge and critical thinking skills may be better placed to evaluate and benefit from AI outputs. The discussion also touched on the potential benefits of AI for accessibility and learner support, alongside concerns about over-reliance on AI where foundational knowledge has not yet been developed.

Are traffic light systems still the best way to communicate expectations around AI use?

Our next topic focused on traffic light systems and other approaches for communicating expectations around AI use in assessment. Participants shared a range of institutional experiences and reflected on both the strengths and limitations of traffic light models.

While some felt that traffic light systems provide a useful starting point for conversations about AI use, others argued that they can oversimplify complex assessment contexts. There was broad support for providing guidance at programme, module or assessment level rather than relying solely on institution-wide classifications.

The discussion also highlighted the close relationship between AI guidance and assessment design. Members discussed AI declarations, transparency around AI use, and the importance of aligning guidance with assessment design and intended learning outcomes. A recurring theme was the need to move beyond detection-focused approaches and towards assessment strategies that help students use AI responsibly, appropriately and transparently.

Looking ahead

The June meetup concluded another active year of community discussions. Revisiting previously suggested topics provided an opportunity to surface issues that had not previously made it into the conversation while still allowing members to propose and vote on new topics.

Across the discussion, participants repeatedly returned to questions of judgement, criticality and responsible use. Whether discussing AI literacy, cognitive impacts or assessment guidance, the conversation reflected a growing recognition that effective AI use depends not only on technical capability but also on the ability to make informed decisions about when, how and why AI should be used.

The session reflected a sector that is moving beyond questions of AI adoption and towards deeper conversations about capability, judgement, assessment and responsible use. We look forward to continuing these conversations with the community in the new academic year. If you’d like to join future discussions, you can join the AI in higher education community meetup list.

Links shared during the call

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