Advance HE says AI-era assessment and feedback now needs clearer student evidence
Updated Jul 11, 2026
Advance HE's latest AI-era assessment and feedback signal matters because it shifts the conversation away from academic integrity alone and towards what students can actually learn from, respond to, and trust. On 1 July 2026, Advance HE published Focus on assessment in the AI-era on Day Two of the Teaching and Learning Conference, reporting a keynote and workshop strand that argued universities need to redesign assessment and feedback around learner capability rather than just completed artefacts. For teams responsible for student voice, the practical point is clear: if assessment and feedback models are changing under AI pressure, institutions also need better evidence on how students experience clarity, fairness, and usefulness in practice.
What has changed in AI-era assessment and feedback
The immediate development is not a new regulation or a survey methodology change. It is an explicit sector signal from Advance HE that assessment and feedback now need redesign, not minor policy adjustment. The 1 July conference update says nearly 350 delegates heard Professor Cath Ellis argue that higher education is going through a paradigm shift in assessment, because external artefacts such as essays and exams are no longer a sufficient proxy for student thinking when generative AI can help produce convincing outputs.
"We need to place the learner at the centre of the assessment model"
Advance HE's summary makes the shift more concrete. It says the conference focused on academic assurance, learning design, and alternative assessment formats that test capability more directly. One workshop highlighted AI-enabled learning design. Another showcased viva voce assessment, using short evaluative conversations with students rather than relying only on traditional written work. The direction of travel is towards more authentic, more dialogic, and more judgement-led assessment. For institutions, that raises an immediate question: how will students experience those changes, and how will universities know whether they are clearer or fairer than what came before?
Advance HE's supporting Assessment and feedback case study compendium 2026, published on 17 June 2026, shows that this is not a one-off conference theme. The compendium is an expanded four-volume collection organised around inclusive assessment and feedback design, authentic assessments for an AI-enabled world, engaging students with feedback, and sustainable, workload-aware assessment practice. That matters because Advance HE is not only saying the system needs to change. It is also curating examples of what that change might look like in practice, for a UK higher education audience even though some case material is drawn more widely.
What this means for institutions
The first implication is that universities should review assessment and feedback as a connected student experience issue, not as separate workstreams for academic integrity, curriculum design, and quality assurance. If assessment types are changing, institutions need to check whether briefs, criteria, feedback formats, and escalation routes still make sense to students. A move towards oral, authentic, or staged assessment may be educationally sound, but it can still fail if students do not understand what is being judged, how feedback should be used, or where practice varies between modules.
The second implication is about evidence collection. Institutions should ask more precise questions in module evaluations, local pulse work, and representative discussions when assessment practices are being redesigned. Rather than asking only whether students liked an assessment, teams should test whether students understood the purpose of the task, the boundaries around AI use, the consistency of staff guidance, and the usefulness of the feedback that followed. A simple student comment analysis governance checklist is useful here because it helps teams define which evidence routes are in scope, who reviews them, and how concerns are recorded when practice is shifting quickly.
The third implication is strategic. Advance HE's framing suggests that the real risk is not just misuse of AI, but weak institutional confidence in what assessment is proving and what feedback is for. Student Experience teams, PVCs, and quality leaders should therefore treat student comments as part of the redesign process itself. If students repeatedly describe criteria as opaque, feedback as too generic to act on, or module-level expectations as inconsistent, those are not minor service complaints. They are signals that the new assessment model may not yet be working as intended.
How student feedback analysis connects
This is where open-text analysis becomes especially useful. When assessment rules, formats, and feedback expectations are in flux, closed-question survey scores rarely show enough detail to guide redesign. Comments reveal where students are confused about permitted AI use, where viva or authentic tasks feel more meaningful, where feedback arrives in a usable form, and where module-level practice drifts. A consistent method such as our NSS open-text analysis methodology helps institutions compare those patterns across module evaluations, annual surveys, and local in-term feedback without flattening everything into one broad AI theme.
There is also a methodological point for teams tempted to summarise this material ad hoc with generic tools. If the institutional problem is inconsistency, the analysis workflow should not introduce more of it. That is why it helps to understand the limits of generic LLMs for student comment analysis before using them as evidence for policy or committee decisions. Student Voice Analytics is one practical way to keep that work reproducible across large comment sets, but the larger principle is straightforward: universities need student feedback evidence they can explain, revisit, and act on with confidence.
FAQ
Q: What should institutions do now?
A: Start with a focused review of where AI-related assessment change is already happening. Map the modules or schools using new formats, check whether students are receiving consistent guidance on purpose and permitted AI use, and add a few targeted prompts to existing feedback routes so you can see whether clarity and usefulness are improving. The goal is to gather usable evidence before inconsistency hardens into a wider quality problem.
Q: What is the timeline and scope of this Advance HE change?
A: Advance HE published the conference update on 1 July 2026, and its supporting assessment and feedback case study compendium was published on 17 June 2026. This is a sector practice and enhancement signal rather than a statutory rule. The scope is UK higher education institutions reviewing assessment and feedback design in response to generative AI and related quality concerns.
Q: What is the broader implication for student voice?
A: Student voice is becoming more important precisely because assessment models are changing so quickly. Universities need more than policy statements or redesigned briefs. They need evidence on whether students understand the change, trust the process, and can use the feedback they receive afterwards.
References
[Advance HE]: "Focus on assessment in the AI-era on Day Two of the Teaching and Learning Conference" Published: 2026-07-01
[Advance HE]: "Assessment and feedback case study compendium 2026" Published: 2026-06-17
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