University Assessment Needs Verifiable Judgment, Not AI Detection
- HEPI reports that 94% of UK undergraduates used AI for assessed work in 2026.
- The article argues that easily machine-produced outputs may measure proxies for capability rather than capability itself.
- Verifiable judgment includes critical evaluation of evidence, reasoning under uncertainty and accountable writing.
- Suggested assessment methods include oral examinations, iterative projects, supervised problem-solving and criterion-referenced portfolios.
A student submits a polished essay, complete with tidy citations and confident prose. A teacher has to decide what it demonstrates. An institution, meanwhile, has to stand behind the qualification attached to it. Generative AI has not created that awkwardness, but it has made it much harder to ignore.
According to HEPI, 94% of UK undergraduates used AI for assessed work in 2026, up from 88% a year earlier. The striking point in Mauricio G. Villena’s argument is not that AI use is widespread, although it plainly is. It is that an assessment which can be competently completed by a machine may already have been measuring an easily produced proxy for learning, rather than the capability it claims to certify.
That reframing matters. Detection can identify patterns or raise suspicions, but it cannot repair an assessment that asks mainly for a finished product with little sight of the thinking that produced it. Nor should higher education treat every use of AI as a disciplinary problem. That road ends in an arms race between increasingly capable tools and increasingly uncertain policing, which is not much of an educational philosophy.
What strikes us as more useful is HEPI’s emphasis on verifiable judgment: the ability to weigh evidence, reason when there is no neat answer, write precisely enough to take responsibility for an argument, and apply knowledge in unfamiliar circumstances. Those are not anti-technology skills. They are the skills that make technology use worth trusting.
Oral examinations, iterative projects, supervised problem-solving and portfolios built against clear criteria can make room for AI without allowing it to become an invisible substitute for judgement. A student may use a tool to explore an idea or improve a draft, provided the resulting work still makes the student’s reasoning inspectable. That is a more demanding standard than simply asking whether software was present, but it is also more honest.
Villena proposes that such expectations should reach beyond individual course teams, through regulatory and quality-assurance requirements including a minimum share of non-delegable assessment. Our reading is that the institutional question is not whether every task must be performed without assistance. It is whether a university can credibly show what its graduates can actually do when the prompt is unfamiliar and the answer cannot be borrowed from a fluent machine.
The harder question is now unavoidable: where, in each assessment, does the learner’s own judgement become visible?