Browse Comments — Relevant (AI ∩ value)

Assessment shapes behaviour. Change the assessment, change the behaviour. When grades depend mainly on recall, students optimize for recall. No amount of policy is going to change that. Under the present education system, this is what produces “fake learning”: the outward signs of achievement are present, but the underlying mental model remains thin. It is a mismatch between what schools assess and what they claim to value. If students can pass tests without being able to explain, apply, or challenge ideas, then the system is overvaluing memorization and underweighting comprehension. This is not really students faking learning, the system is causing it. In today's information-rich environments, that problem becomes more serious. Learning now depends not only on knowing information, but on judging sources, testing claims, and separating fact from misinformation. Either education doubles down and becomes increasingly optimized for so called measurable and standardized "learning". Or institutions deliberately protect the parts of learning that resist today's widespread automation: judgement, interpretation, mentorship, attention, character, independent thought.

The distinction between AI that supports learning and AI that removes the friction of learning. That's what most policy conversations miss. One layer I sometimes think about is that unclear expectations aren't just a classroom problem. They reflect something more structural in Higher Education as well. University funding often flows toward research output, not teaching quality - so institutions hire and reward professors accordingly. AI is being dropped into a system where teaching has often been the secondary obligation, and it is amplifying it. Students feel the teaching-learning gap more acutely now, but the gap isn't new. Which makes your closing question even more meaningful: institutions need to decide what they actually value. The AI policy for the classroom is downstream of that decision - not a substitute for it.

Sabrina N. I don't think there is any one solution. I don't think bans and the use of AI detection are the way to go. They are not meaningfully enforceable or fit for purpose, respectively. It's clear at this stage that assessment needs to change. The days of relying on artefacts as stand-ins for learning are probably over (and that has been well overdue for some time, as someone who has been investigating contract cheating for years). If for some reason a university wants to use a report or an essay for the purposes of assessment, they can either, a) use it as a purely formative exercise (remove the value of cheating), b) watch the student write it, or c) make the assessment a face-to-face conversation about the document rather than the document itself. Ultimately, universities need to be spending more time having conversations with students about their learning, and these conversations should be the assessment.

I agree. Most students are not trying to break the rules, they are trying to navigate a system that often hasn't defined them clearly. The question is no longer whether AI should be used in higher education. Students are already using it. The real question is when, how, and for what purpose it should be used. Clear expectations, AI literacy, and assessment redesign are far more valuable than blanket bans. When institutions provide guidance instead of ambiguity, students can focus on learning rather than guessing where the boundaries are. The challenge isn't AI. It's governance, transparency, and intentional design.