Jisc's 'human in the loop' pilot sharpens AI governance for student feedback evidence

Updated Nov 08, 2026

On 18 June 2026, Jisc published What does “human in the loop” actually mean? Consulting on our next pilot idea, proposing a new 2026-27 pilot on meaningful human oversight in AI-enabled workflows. The proposal connects review responsibilities with tools institutions already use, offering a useful question for student voice teams: what does a reviewer need to inspect before accepting an output?

What the "human in the loop" proposal changes

Jisc says earlier pilots made sense when institutions needed access to tools and wanted help deciding whether products were useful. The new question is different: many colleges and universities already hold licences for relevant tools, so the issue has moved from product choice to governed use. Jisc says institutions are now asking not "is this tool any good?" but "how do we use AI well?" The proposed response combines policy, review processes, staff capability and evaluation.

Jisc's proposed model has two phases. Phase A is a structured discovery programme for member colleges and universities, designed to co-produce formal rules, guidance, ethical checklists, review processes, and evaluation approaches. Jisc says that stage should be a genuine exit point, so an institution that stops there still leaves with useful governance materials. Phase B is optional and would test those outputs in live settings, using AI-enabled tools institutions already have access to. The aim is a practical framework for meaningful human oversight that can be refined through use and then published for the wider membership.

The scope is broader than assessment alone, even if assessment and feedback are the clearest examples in the article. Jisc says the pilot is open to all member colleges and universities and is intended for anyone whose work involves AI supporting judgements or content that a human is expected to oversee, including educators, assessment leads, quality and governance teams, student services, researchers, and professional services staff. The June announcement invited consultation responses and proposed a pilot across the 2026-27 academic year, split between a discovery stage and an optional practice stage. The timetable and two phases were presented as proposals for members to discuss.

What this means for institutions

Our practical suggestion is to make a short review record for one existing workflow. Give it three parts: evidence inspected, changes or challenges raised, and the final decision with its owner.

First, universities should stop treating human review as self-evident. A person glancing at AI-generated feedback is not the same as a person being able to understand, challenge, edit, and reject it. For the selected workflow, define who reviews the output, which source material they can inspect and how they record a correction or rejection. That matters for assessment workflows, but it also matters wherever AI-generated or AI-summarised outputs can influence student-facing decisions.

Second, the proposal has a clear implication for student feedback evidence. This is our application of Jisc's wider human-oversight proposal to survey analytics: if universities use AI to summarise module evaluations, service feedback, or survey comments, they will face the same oversight question. Who can inspect the source comments? Can a team trace a summary back to the underlying evidence? What happens if an output looks plausible but flattens disagreement or misses a safeguarding issue? Record a disagreement alongside the summary, then give the responsible person a route to change the conclusion. The analysing-comments pathway develops the questions of source material, categories and review.

Third, institutions should treat this as a near-term governance task rather than a future procurement task. Jisc's proposal is explicitly tool-agnostic and built around tools universities already have. That means Student Experience teams, PVCs, and quality leaders can start now: map where AI is already touching assessment, support, and evidence workflows; define where human oversight sits; and document how outputs will be checked, escalated, and retained. A practical starting point is a student comment analysis governance checklist, because the same disciplines that protect comment analysis also help universities scrutinise wider AI-supported evidence.

How student feedback analysis connects

Jisc’s proposed pilot spans assessment, student services, research and professional services. Our application to comment analysis is to keep the review close to the underlying response. When universities use AI to code, cluster, summarise, or prioritise open-text student comments, they still need to know what source material was in scope, what review step sat between raw data and reported conclusion, and how exceptions were handled. For a survey review, retain the prompt, reporting period and original response with the analysis record. Public NSS briefs have their own published methodology; an institutional project needs its own agreed purpose and reporting definitions.

Bring a disputed interpretation back to the source material before it enters a decision paper. Record who resolved it and why, so the next reviewer can follow the reasoning.

FAQ

Q: What should institutions do now if they are already using AI in student-facing or evidence workflows?

A: Start with an inventory. Identify where AI is already drafting feedback, summarising comments, supporting triage, or shaping decisions. Then document who reviews the output, what evidence they can inspect, when they are expected to challenge it, and how the final decision is recorded. If that process is still informal, use a checklist such as the student comment analysis governance checklist to turn it into a clearer operating model.

Q: What is the timeline and scope of Jisc's proposed pilot?

A: Jisc published the consultation article on 18 June 2026. At publication, Jisc invited consultation responses and expected the pilot to run across the 2026-27 academic year. The proposed scope covered Jisc member colleges and universities, and the intended participants include educators, assessment leads, quality and governance teams, student services, researchers, and professional services staff.

Q: What is the broader implication for student voice?

A: A useful review record shows the source material inspected, the changes a person made and the decision they accepted. Teams can use that sequence when an AI-assisted output contributes to assessment, support or a student feedback discussion.

References

[Jisc / National Centre for AI in Tertiary Education]: "What does “human in the loop” actually mean? Consulting on our next pilot idea" Published: 2026-06-18

[Jisc / National Centre for AI in Tertiary Education]: "Insights from the AI in Marking and Feedback Pilot" Published: 2026-05-20

[Jisc / National Centre for AI in Tertiary Education]: "University of Nottingham’s blind-study evaluation of AI in assessment design" Published: 2026-06-01

Correction, 7 September 2026: the proposal covers assessment, student services, research and professional services, rather than marking and feedback alone.