This is a BALEAP TELSig podcast which I have had on my list to listen to for a while. The blurb: Last year HEPI reported 95% of students were using gen AI, but recent research from Stephen Gow and Sam Illingworth casts doubt on this figure. Today I’m joined by Stephen to talk through some of the key finding of his Leverhume Trust funded study that draws data from over 7,000 participants. What do students really think about gen AI in higher education, and how should this shape the way we treat it in the curriculum? As curriculum and materials development is a not insignificant part of my role, this is very relevant to my interests!

There’s a bit about Duolingo and streaks at the start but I’m waiting for the main content before I start making notes! If you are interested in Duolingo and streaks, listen to the start of the podcast! Though I do like the analogy between Duolingo/streaks and going to the gym every day but only using one piece of equipment.

It’s about what users actually think about the technology underneath the surface. Companies introducing no phone policies at work, offering lockers and pouches, to try and boost productivity. Not like picking up a newspaper back in the day because the sole purpose of the algorithm is to hold your attention. They mention teenagers wishing Tiktok could be uninvented. Technology use can be a compulsion. If you look/walk around in a library, you’ll see how many devices people have. You are in multiple places at once with it. When students used to come to university, the library would be the best information you could get and you’d have to go there to research things. The internet does give us access to far more information but managing that information is incredibly challenging. The prinicples of quality of information don’t change. The issue with GenAI as it’s come along is the “double-edged sword” that it does enable to a certain extent to digest all of the information but you aren’t too sure of the quality of the ingredients of that information. A model e.g. Claude may differ between different conversations/threads. Like the film Mickey 17 (maybe wrong number), he’s given himself up to be cloned for military and scientific experiment purposes. But each clone is slightly different. You could be having a really interesting interaction and then start up a new one which feels slightly different.

We have to keep in mind incentive structures: devices whose purpose is to hold attention and chatbots also are. E.g. if you finish a conversation, it will ask you if you want something else, suggest something you might want. There are models with guardrails on too. Tools will suggest ways to go to a student or researcher working on something. It takes a lot for you to say no. You can then discuss the ethics of doing the thing it suggested, and then it will say actually it will get you into trouble. But it can be useful if used in the right way. Just like with Duolingo. Ideally we want students who want to learn but even if the tools were to stop developing today, they have incredible capability and can do things that will massively affect peoples’ motivation to do things. The flip side is that the human brain is incredible, so how do we have a co-existence of those intelligences.

The elevator pitch of the study: The whole project is the StudentXGenAI project. The dynamic tensions paper is a scoping review, including trialling different AI tools. He approached it like a student would. Within the paper he talks about the findings from 40 papers limited to qualitative studies as the plan was to do interviews subsequently. Reflecting on student and researcher use of these tools. After the scoping review came 20 interviews with students. Then the Leverhulme funded bit was a survey of students in the UK. 7000 responses. They found a window without other surveys and got university buy-in by giving them the data of their student respondents to analyse. While for the project it was aggregated. They targeted students whether or not they use GenAI. 70% were using it, 30% were not using it for anything (studies, work, personal life).

Knowledge and access: do they know how to use it, how did they learn how to use it? Mostly trial and error. University resources are very low down. They are very useful if they use them but mostly they weren’t used.

What do you use it for? Brainstorming, computer coding, etc.

Attitudinal area of questions: Do you trust the outputs? What motivation do you have for using the tool (easier, faster, helps me to overcome blocks were common answers).

There was a petition at the University of Salford pushing back against the integration of GenAI.

There is a real tension between productivity/performance and learning. Instrumental action (Wnting to complete something to get it done) vs more communicative action where you are learning to do something and communicating with people in your society to get things done. If we are too instrumental, we stop communicating. We are in competiton with each other. Competition vs collaboration are another area of tension. Degrees in a massified system become less of a thing you do because you want to do it and more something you do because it is the done thing. A means to an end. So students more likely to approach it instrumentally. If you are stressed and getting into debt and find there is a tool that makes things easier, you will consider it a benefit.

The dataset was deliberately large in order to be able to draw some conclusions. The bulk of the questions were the same as an Australian study, and there were a lot of similarities which was interesting.

In terms of institutional policies, we have see versions of the traffic light system etc, assessment scales, two lane approaches, based on the assumption that everyone is using it so let’s facilitate that. Its efficacy as a learning tool is in question so there is a tension between creating conditions where we regulate it vs encouraging use of it because it’s the future.

Based on the data from the 7000 students, there is room for regulation. You can’t ban it but by having these systems you are saying it should be regulated. There is a false binary between using genAI and not using genAI. While the systems mentioned above, if implemented fully, at first assessements would primarily be red, and then over time there would be shifts into amber and green.

Staff don’t trust students, students don’t trust other students, staff don’t trust staff when it comes to using GenAI. The survey shows that students appreciate clarity and want to do the right thing. The majority of the students are honest and want to do the right thing. In both the Australian and this data, 67% of students will be honest and won’t use if when told not to. We need to hold onto this. The majority of people want to do the right thing. A significant minority will use it when not allowed. 10-15% self-report very instrumental approaches, just going to use it. This matches self-reporting on deliberate plagiarism and contract cheating. So what do we do, how do we find the balance between security of the assessments and trusting the majority of students. Security measures often impact the validity of the assessment. We need to take a step back and instead of having traffic light systems etc, have targeted experimental modules where staff and students work together and we see how staff and students are really using it. Have learning enhancement digital people working with staff and students, to see what really works. Then carry what is learnt to other departments. Increasingly there are wearables so we need to have a dicussion about privacy and data issues.

The students who are honest and recognise that using genAI isn’t the best approach, still have to deal with the instrumental pressure. They can see that other students are using AI and giving a false impression of ability and they don’t want to be at the tail end of the bell curve so they are pushed into using it rather than really learning. They don’t feel like they can afford to do the work in the way it was intended.

Is writing a prompt a skill in the same sense as the things that it is replacing? The Australians went back to pen and paper exams while in the UK we were still in a post-covid mindset of very, very enhanced trust – 6hr window, do the exam online, we trust you. If you look at pre-covid exam regulations and security, all you had was the person in the room and their brain. Post-covid, everything was more unsupervised, tools were not proctored. A worrying lack of attention to assessment security. What impact might that have on motivation?

Now it’s 2026, GenAI became a thing in 2022 so there has been time for the kneejerk outright ban, to embrace, and then trying to find a balance. The new normal. The challenge to trust of the digital world. The free era of AI is coming to an end now, as they need to finance, so time for premium. University of Manchester has given co-pilot to all students and staff, lots of universities doing similar. But more common for students to use free tools which have all sorts of data issues. Who owns the information? More collaboration between institutions and negotiating as a block with the GenAI companies is needed but we aren’t there yet. Everyone is trying to reinvent the wheel but it is not joined up enough.

What is the next big question that needs answering? To reflect on the perceived benefit of productivity. If a student can be productive and perform well without learning well, then there is a huge assessment validity issue. How do we solve this, and genuinely assess in the way that we learn?

Further reading

Chung, J., Henderson, M., Slade, C., Liang, Y., Pepperell, N., Corbin, T., Walton, J., Yu, AS., Bearman, M., Buckingham Shum, S., Fawns, T., McCluskey, T., McLean, J., Oberg, G., Seligmann, A., Shibani, A., Bakharia, A., Lim, LA., Matthews, KE. (2026). The use and usefulness of GenAI in higher education: Student experience and perspectives. Computers and Education Open, Available at: doi:  10.1016/j.caeo.2026.100347.

Gow S, Illingworth S (2026), “Dynamic tensions: an AI-assisted critical scoping review of university students’ qualitative experiences of GenAI”. Artificial Intelligence in Education, Vol. 2 No. 1 pp. 67–89, Available at: doi: 10.1108/AIIE-06-2025-0151 

Gow, S. and Illingworth, S. (2026) “It is a temptation to get it to do the work…” – student experiences of GenAI in UK universities. 09 Apr 2026. Advance HE. [Online]. Available at: https://www.advance-he.ac.uk/news-and-views/it-temptation-get-it-do-work-student-experiences-genai-uk-universities [Accessed 24th J 2026].

My thoughts:

It was a good podcast but I think I was hoping for more student voice to come through. More qualitative information from students relating to the three areas mentioned – knowledge and access, what do you use it for and the attitudinal aspect. I think I was also hoping for more concrete suggestions for curriculum and materials design. It may be that I didn’t successfully extract that information from the podcast format. I shall be reading the articles linked to above to glean more. I suppose that is kind of the point: the podcast is never going to be that comprehensive! (Edit: the “Dynamic tensions” one has some interesting stuff in the “Thematic analysis and discussion” part! As does the paper about the Australian study!)

Anyway, I think the bell-curve thing is very interesting – students using it even though they’d rather not because other students are using it and they don’t want to get left behind. That is a very strong argument for designing assessments that reward the skills you are teaching, accept ethical use of AI but don’t reward non-ethical use of AI. We have attempted to do that in our coursework essay assessment via reworking the criteria, with some success but the future will be impacted by evolution of the tools and further evolution of the criteria that is outside our control.

There also seems to be tension between AI-required assessment and the 30% who don’t want to use AI for various reasons (ethical, environmental, want to learn themselves). I suppose a carefully designed assessment could address some of that (e.g. make it so that they do still learn themselves; make it so that it doesn’t require heavy interaction with a tool so that the environmental impact is lower etc.)

It was heartening to hear that students appreciate clarity and the majority will try to follow the rules, as clarity and support is what we are trying to achieve in our curriculum and materials. Though I wonder if that is the case regardless of context. I.e. does the bell curve issue (or the need to succeed) have more influence in a context like ours where the outcome is very high stakes (determines whether or not the student gets to progress to univerity in the first place)!

I think it would be interesting to show students some questions and results (some are illustrated in the Advance-HE article) and discuss where they fit in/why, to get them thinking about it and give them insight into how others use GenAI. Perhaps get them to answer the questions on a google form then show the results side by side with the results from the 7000. All sorts of things you could do with it! Watch this space. 🙂