Browse Comments — Clean (de-noised)

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.

Dan KREUGER thanks for reading my post and sharing your perspective, Dan. The "teaching-learning gap" is definitely being amplified right now. You're so right that this trickles down from the top. When funding and rewards favor research over actual teaching quality, professors get squeezed, and that classroom confusion is just the byproduct. If institutions actually started shifting their values to address this, what do you think a realistic first step would look like?

What stood out to me most in this piece is the idea that the real challenge is not AI itself, but institutional ambiguity around what kinds of thinking, struggle, and cognitive engagement we are actually trying to preserve. That feels increasingly important. When institutions focus primarily on outputs without developing shared clarity around process, authorship, reflection, and demonstrated understanding, it becomes much harder to distinguish between genuine learning and the appearance of learning. I especially appreciated the point that some friction is actually the point. In many ways, the struggle to articulate, wrestle with uncertainty, and work through partial understanding is where meaningful learning often happens. Thought-provoking piece.

Spot on. The tool isn't the problem. The silence around it is. Students are already using AI whether we have policies or not. The question is whether we're going to pretend that's not happening or actually create clear guidelines that help everyone. Teachers need that clarity just as much as students do.

Agree on the clear expectations part. From my experience, clarifying rules and deliverables can really make a difference. It's been 4 years since I dont step foot on a classroom, however; and so much has changed since.

I'm not sure I agree. The research on cheating is pretty clear on the fact that many students will engage in outsourcing or offloading even when they know precisely what they should or shouldn't do. I have proven more contract cheating cases than I can count, and there has never been an instance where the student didn't know that asking someone else to do the assessment for them was an issue. You can be as clear as you like in the instructions about AI use - a non-trivial number of students will simply ignore those instructions if it is easy to do so and the risk of any consequences for doing so is negligible.

Disagree. The issue is that "rules" which are completely unenforceable will result in perverse outcomes. Coming up with a policy is applying a bandaid to an amputated leg.

Shaun Lehmann thanks for sharing your perspective. I'm interested, what do you think is the solution then?

Mike Williams exactly! Thanks for reading and sharing your perspective

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.

Of course the tech ology and the companies that created it are the problem! Had they not built it the way they built it and advertised it and deployed it the way they did it would not be a problem. By your calculation, drugs and pushers aren't the problem either?

Tool is as good as the user and its education about it. Totally with you on this Sabrina N.

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.

Srinesh Vallebhaneni exactly. Students are already using it AND they will use it as they start their careers. I think providing this type of guidance only helps to prepare them for the "real world"