Jisc's June HE AI meetup discusses literacy and assessment guidance

Updated Nov 08, 2026

assessment methodsartificial intelligence

On 19 June 2026, Jisc published June HE AI community meetup, a summary of sector discussion on AI literacy, assessment guidance, and student support. For student feedback teams, the discussion offers a practical starting point: ask students to apply guidance to the assessment task in front of them.

What has changed in Jisc's June HE AI community meetup

The June community meetup revisited topics selected through participant voting, including suggestions that had not been discussed at earlier sessions. Members explored judgement, criticality and institutional practice alongside AI literacy.

The article says the most popular topic concerned what AI capabilities staff and students should now be expected to develop. It also highlights how institutions are embedding AI literacy into programmes, careers activity, digital capability work, and student support, rather than treating it as a standalone workshop topic. Jisc says members discussed critical AI literacy, metacognition, and students' ability to judge when AI use is helpful, limited, or inappropriate. In practice, that means AI literacy is being treated as part of the student experience, not only as a staff development issue.

The most practical shift comes in the discussion of assessment guidance. Jisc says members reflected on traffic light models and other ways of communicating expectations around AI use, with a preference for guidance tied to a programme, module or assessment.

That is the key development for student feedback teams. If guidance is becoming more local and task-specific, institutions will need more local and task-specific student evidence too. A generic question about AI policy is unlikely to tell a university whether students understood the rules on a particular module, trusted the guidance, or knew when human support was still available.

What this means for institutions collecting student feedback on AI

Our practical suggestion is to put the exact guidance and assessment task beside the student comment. Ask what the student understood they could do, which instruction they used and where they would seek clarification.

The first implication is survey design. If universities are embedding AI literacy into teaching, support, and assessment, they should review whether local surveys and module evaluations are asking the right questions. Jisc's discussion suggests teams need to separate understanding, trust, usefulness, and assessment-level clarity rather than asking a single broad question about AI. Our earlier discussion of student trust and AI literacy considers related questions.

The second implication is consistency across the institution. One school may rely on traffic light labels, another may use assessment-specific statements, and a third may embed AI guidance inside skills support or module handbooks. Student comments can help a team investigate whether those differences are causing confusion. That is why Jisc's earlier AI in assessment findings on student buy-in and communication still matter here. The issue is no longer only whether an AI-supported approach exists, but whether students can interpret it reliably across courses and contexts.

The third implication is ownership. Jisc's meetup summary cuts across academic practice, digital capability, employability, and student support. That means Student Experience teams, PVCs, and quality professionals should treat AI-related feedback as shared institutional evidence rather than leaving it with one digital or assessment lead. If students say guidance is inconsistent, staff need to know where that concern should go, who reviews it, and how any change will be communicated back. Give conflicting instructions a named owner, then record the wording agreed across the relevant assessment and module documents.

How student feedback analysis connects

This is where open-text feedback becomes more useful. A useful open-text prompt could ask students where guidance was clear or contradictory, whether assessment rules made sense in practice, and where subject-specific examples would help. These are suggested questions, not reported responses from the meetup. Keep understanding the rule, applying it and finding help as separate questions. The digital-learning pathway connects these experiences with a practical review of tools and support.

After a wording change, ask students to use the revised instruction on the same task. Record the question resolved and any explanation still needed. The student comment analysis governance checklist can help assign responsibility for reviewing and reporting those comments.

FAQ

Q: What should institutions do now if they are updating AI guidance for 2026-27?

A: Audit where students currently encounter AI rules, at programme, module, and assessment level, and then check whether your feedback questions match that reality. Add at least one open-text prompt asking where guidance felt clear, unclear, or inconsistent, so teams can tell whether the problem sits in policy wording, local implementation, or student support.

Q: What is the timeline and scope of Jisc's latest update?

A: Jisc published the meetup summary on 19 June 2026. It reflects discussion in a higher education community session rather than a statutory change, so there is no formal implementation deadline. The scope is UK higher education practice, especially institutions reviewing AI literacy, assessment guidance, and student support for 2026-27.

Q: What is the broader implication for student voice?

A: The broader implication is that student voice on AI now needs to become more granular. As guidance moves closer to programme, module, and assessment level, universities need evidence that shows not only whether students approved of AI policy in general, but whether they understood the rules, trusted the judgement behind them, and knew how to act on them in practice.

References

[Jisc / National Centre for AI in Tertiary Education]: "June HE AI community meetup" Published: 2026-06-19