Advance HE's pre-arrival questionnaire pilot puts AI readiness into transition planning
Updated May 13, 2026
Incoming students are arriving with sharply uneven experience of generative AI, and Advance HE is now using the national pre-arrival questionnaire pilot to push that issue into mainstream transition planning. On 5 May 2026, its News + Views site published What incoming students actually know about AI, drawing on the pilot and arguing that universities should act on early student evidence before induction begins. For teams responsible for student voice in higher education, the practical point is that AI readiness can now be measured before it shows up later as policy confusion, weak assessment guidance, or anxiety in survey comments.
What has changed in the pre-arrival questionnaire pilot
The immediate development is not a new national survey requirement. It is the publication of AI-specific findings from the first national pre-arrival questionnaire pilot, led by the University of East London with Advance HE and Jisc and funded by the Office for Students. In Wave 1, over 5,500 incoming undergraduates across 15 English institutions completed the questionnaire in September 2025 before starting university. Advance HE's 5 May analysis says 60.6 per cent had used generative AI in some form, but 39 per cent had no experience at all. That makes AI readiness a transition issue, not only an assessment issue.
"The findings show clearly that students arrive at university with diverse learning histories and uneven preparedness."
The detail matters. Advance HE says the most common uses were exploring topics of interest, correcting grammar and spelling, summarising information, and having concepts explained. Drafting or rewriting work was far less common, at around 15 to 16 per cent of use cases, although 32.6 per cent used AI for writing feedback. The article also identifies four user groups: light users, learning-focused users, writing-support users, and a small group of power users. ChatGPT dominates the picture, with 97.4 per cent of AI-using students reporting at least some familiarity with it. Adoption also varied by subject, from 77.8 per cent in Mathematical Sciences to 36.8 per cent in Language and Area Studies.
The wider pilot is still active. Jisc says it is a national, standardised pre-arrival survey for undergraduate and postgraduate taught students, designed to support earlier, more targeted intervention across providers in England. Participation guidance for Wave 2, which will run from September to November 2026, says institutions will run their own survey version in Jisc Online Surveys, receive results in real time, and get benchmark analysis from Advance HE. It also makes the governance model explicit: participating institutions are data controllers, while Advance HE and Jisc act as data processors. That moves the pre-arrival questionnaire from a one-off pilot headline to something closer to operational survey infrastructure.
What this means for institutions
The first implication is that AI induction should be evidence-led and segmented. Universities now have a strong reason to separate students who arrive with no AI experience from those already using AI as part of their writing process. Those groups need different support. One needs basic orientation, examples, and confidence; the other needs clearer boundaries, disciplinary guidance, and assessment design that rewards thinking rather than output. That sits naturally alongside Advance HE's earlier sector evidence on student experiences of GenAI, but the pre-arrival questionnaire pilot pushes the issue earlier in the student journey.
The second implication is that pre-arrival data should feed mainstream student experience governance, not sit inside admissions or induction planning alone. Jisc says institutions used the first-wave findings to adapt induction activities, redesign campus tours, strengthen communications about careers and support, and brief learning and teaching committees, recruitment teams, and senior leaders. That is the right direction. Pre-arrival evidence becomes more useful when institutions read it alongside later signals on assessment, feedback, belonging, and support, using the kind of benchmarking and triangulation approach that makes survey evidence more actionable.
The third implication is governance. The pilot supports demographic disaggregation and can be linked to student identifiers, which makes it more useful for access and participation work but also raises the bar for privacy, consent wording, and follow-through. If universities ask about AI confidence before arrival, they should be clear about how that information will be used, who will see it, and what support or guidance may follow. Early student voice only builds trust if the route from collection to action is visible and defensible.
How student feedback analysis connects
Pre-arrival AI data is most valuable when universities can connect it to what students say later about assessment, feedback, and academic support. If a cohort arrives with low AI familiarity, institutions should be able to check whether that later surfaces in comments about unclear rules, fear of getting AI use wrong, or confusion about what counts as legitimate study support. If a smaller cohort arrives already using AI for drafting, teams need to know whether later feedback points to assessment design problems, slow feedback, or weak guidance rather than assuming misconduct is the whole story.
That is where a consistent approach to comment analysis becomes useful. A tool such as Student Voice Analytics can help institutions compare themes across pre-arrival questionnaires, induction check-ins, module evaluations, and annual surveys without treating each exercise as a standalone snapshot. The practical takeaway is simple: if universities are going to ask earlier questions about AI, they should also be ready to track how those early signals change once students are inside the course.
FAQ
Q: What should institutions do now?
A: Review whether you already collect any pre-arrival evidence on AI confidence, digital access, or expectations. If not, build a small, clearly worded set of questions into transition work for new entrants and decide in advance how the findings will shape induction, guidance, and assessment communication. The value comes from using the evidence quickly, not just collecting it.
Q: What is the timeline and scope of the change?
A: Advance HE published the AI-focused analysis on 5 May 2026. It draws on Wave 1 of the national pre-arrival questionnaire pilot, completed in September 2025 by over 5,500 incoming undergraduates across 15 English institutions. Wave 2 is scheduled to run from September to November 2026. This is an England-focused pilot, not a mandatory UK-wide survey.
Q: What is the broader implication for student voice?
A: Student voice on AI should start earlier than the first module evaluation or the first annual survey. Universities are more likely to act well when they understand what students bring with them, where confidence gaps sit, and how those gaps change once teaching, assessment, and support systems are in play.
References
[Advance HE]: "What incoming students actually know about AI" Published: 2026-05-05
[Jisc]: "Understanding students before they arrive: early insights from the pre-arrival questionnaire pilot" Published: 2026-04-17
[Office for Students]: "Equality in Higher Education Innovation Fund" Published: not stated
[Advance HE]: "Information for participating institutions: Pre-arrival Academic Questionnaire (PAQ), National Pilot – Wave 2" Published: not stated
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