An interesting counterpoint to the massively overexposed HEPI figure, widely misquoted as if there’s a near total majority of regular AI users rather than almost all students having tried it, can be found in the National Pre-arrival Academic Questionnaire (PAQ) pilot:
The National Pre-arrival Academic Questionnaire (PAQ) pilot, led by Dr Michelle Morgan at the University of East London, Advance HE and JISC, and funded by the Office for Students, is one of the most significant data-gathering exercises in recent UK higher education. In Wave 1, completed by over 5,500 incoming undergraduates across 15 English institutions in September 2025, it asked students directly about their generative AI experience before they had even started university. The findings could usefully sit on the desk of everyone responsible for AI strategy, curriculum design, access and participation, and student transition.
https://advance-he.ac.uk/news-and-views/what-incoming-students-actually-know-about-ai/
They found that “three in five incoming undergraduates, 60.6 per cent to be precise, report having used generative AI in some form before arriving at university” which means that 39% of incoming undergraduates have no experience whatsoever. Once you break down the use we’re left with a far less worrying picture than many colleagues are assuming:
Among those who do use it, the picture is notably less alarming than the dominant narrative suggests. The most common uses are learning-oriented: exploring topics of interest (51.6 per cent), correcting grammar and spelling (50.9 per cent), summarising information (43.9 per cent) and having concepts explained (42.9 per cent). These are study support functions. Students are using AI as a tutor, a proofreader and a reference tool, not primarily as a ghostwriter.
Generating drafts or rewriting work, the uses that attract most anxiety from academic integrity teams, account for around 15 to 16 per cent of use cases. Getting feedback on writing sits at 32.6 per cent. More technical uses, such as creating code or generating presentations, remain genuinely uncommon at 4 per cent and 3.8 per cent respectively, which makes sense for students who have not yet encountered the technical workflows where such tools are most embedded.
A cluster analysis of the data identifies four distinct user groups. Nearly half of AI-using students (48 per cent) are light users: they have experimented with one or two applications but AI is not embedded in their habits. A further 31 per cent are learning-focused users who use AI for topic exploration, summarisation and explanation but do not use it to generate or rewrite their own work. Writing support users (18 per cent) are a distinct cohort in which every student uses AI for drafting or rewriting. And at the top, a small group of power users (4 per cent) have already integrated generative AI broadly and deeply into their workflows.
https://advance-he.ac.uk/news-and-views/what-incoming-students-actually-know-about-ai/
I particularly appreciated the suggestion that universities have a responsibility to guide these students away from a complete reliance on ChatGPT, given the dependency this entails for this behaviour within the commercial landscape:
Second, universities could move beyond the ChatGPT monoculture in their own communications and teaching. Students who know only one tool, and know it uncritically, are not AI-literate. They are tool-dependent. Helping them understand a broader landscape, including the limitations and failure modes of the tools they already use, is part of the curriculum, not an addition to it. And it matters for learning outcomes as well as digital literacy: the evidence is becoming clear that students in environments which reward thinking over production engage with AI in fundamentally different ways, using it to interrogate and understand rather than to generate and submit. Building critical AI understanding from the start is part of creating those environments.
https://advance-he.ac.uk/news-and-views/what-incoming-students-actually-know-about-ai/
