Universities and AI: where is the Leadership?
There is a report out today report by the UK “Office for Students and Advance HE, a charity promoting university governance and teaching, about the use of AI in universities. It says that the use of AI by students for essays, dissertations and other unsupervised coursework has made it “close to impossible” to assess such work reliably and suggests a return to traditional written exams, including viva-voce examinations, in-person presentations, and other forms of supervised assessment.
I’ll quote this paragraph from the report:
131. To date, innovation and experimentation with AI has generally been led by individual departments and academic staff. Institutions should now demonstrate leadership on AI by adopting a strategic approach that recognises the technology’s complex and wide-ranging implications across teaching, professional services and beyond. This requires visible strategic leadership that empowers staff to make decisions about the technology’s use in the interests of students, alongside driving the meaningful implementation of any AI strategy and ensuring staff are AI-literate.
At Maynooth University, where I work, academic staff have learnt the hard way that leadership of any kind – whether strategic or visible or not – just doesn’t exist when it comes to teaching and learning (even though these should represent one of the core missions of the institution). AI is just one example of an issue concerning which Senior Management appears utterly clueless. This isn’t entirely a bad thing. Most ideas emerging from the top are poor, so it’s better when they don’t have any ideas at all. That leaves it to departments and individual lecturers to decide what to do. This is the situation described in the paragraph quoted above.
Over a year ago I wrote a post about the GenAI issue. Here is an excerpt:
I have been concerned about the integrity of the coursework element of these modules in the light of improvements in Generative AI. Only a couple of years ago GenAI could not solve the sort of problems I set for homework, but now it generally can – especially for EE206. I don’t altogether object to people applying artificial intelligence to solve mathematical problems, but the issue is that it does make mistakes. Moreover, instead of saying “sorry I can’t solve that problem” it will generally present a superficially plausible but incorrect solution. Although students will probably use GenAI for problem-solving, I think it is important that they learn to do such problems themselves, otherwise they won’t know whether the solution coughed up by the algorithm is correct or not. That way lies disaster.
The only way to learn mathematics is by doing it. If students get GenAI to do the mathematics for them, then they won’t learn it. In the past we have given marks for coursework (usually 20% of the module mark) mainly to encourage students to do them. Students who don’t bother to do these exercises generally do badly in the final exam (80%).
For these reasons I am moving the assessment from weekly homework sheets – which could be tackled with AI – to supervised in-class tests for which students can use notes on paper, but not laptops or phones, just like they would in the final examination. I will of course give examples for the students to have a go at themselves, and I will give feedback on their attempts, but they will not contribute to the module score. Another advantage of this approach is that students won’t have to do so much work against deadlines outside of class.
This approach went well last year and I’ve continued it this year. (In fact we had a class test in EE206 today.) Many of my colleagues have also adopted this model. I have, incidentally, shown students how to get various GenAI tools to solve mathematical problems so they can check their own work for formative assessment purposes. They can not use GenAI, however, in any contributory (summative) assessment.
The problem is that not all university departments are committed to academic integrity. Some appear happy to dish out first-class degrees to anyone who can type a prompt into CoPilot. Courses perceived to be easy to pass in such a way will prosper at the expense of those that insist on challenging students. Some overall policy is therefore required. This has been happening for some time already. The solution I propose is simple enough for even a Vice-President to understand. If my University – or any other – wishes to prioritize academic integrity and maintain academic standards then it should make a rule that no unsupervised coursework should contribute to progression through the degree or the final classification.
November 11, 2026 at 7:55 pm
The report’s point about unsupervised coursework is hard to argue with, and the mathematics example here makes it concrete: the only way to learn mathematics is by doing it, so a weekly in-person class test protects both the learning and the assessment. The contrast with departments that appear less committed to academic integrity is also important, because a single university-wide rule will not work if each department applies it differently. One practical addition is for departments to publish, in plain language, which parts of a course may use AI and which must be done unaided, before the term starts — that gives students a clear line and gives staff a consistent basis for marking. Supervised presentations and viva-voce exams, as the report suggests, fit that same idea: they test understanding in the room, rather than a polished file submitted later. Thank you for sharing a working example instead of only the policy debate.