We would love to be able to tell our students exactly what they can and cannot do regarding using AI, and we know that our students also really want clear guidelines so they do not accidentally cheat. But, as Corbin et al. (2025) write, “The more detailed and specific our instructions become about ‘acceptable’ AI use, the more we highlight the gap between what we can specify and what we can verify”. When we try to code everything a student could do in relation to learning into the traffic light of red – forbidden, green – ok, and yellow – it depends, we run into several problems: The underlying assumption — that students will understand what exactly is permitted and what is not — is not valid. The lines between, for example, drafting and editing, are blurry: what if a suggested edit brings out an argument that we hadn’t thought about explicitly before but now we think we should stress it more? Where is the boundary between having a question explained and getting hints for how to approach it? What counts as “refining” an argument: correcting grammar but not alterning meaning, finding errors but not helping towards a solution? Students need to voluntarily follow the rules, even when there are disagreements (or misunderstanding, as above) between teachers and students about what is legitimate use of GenAI for learning or in assessment. Compliance cannot be enforced because there is no way to reliably detect GenAI use (other than leaving in parts of a response like “Sure, here is the response to the question …”, the teacher directly observing a student using GenAI, or the student admitting to it). So while the traffic light analogy seemed appealing at first for how easily it signals to students what’s ok and what is not, traffic lights in real traffic are substantially different. They exist in much larger structures with speed cameras, police patrols, fines, etc, so rule violations can actually be detected and compliance enforced. To deal with the challenge, Corbin et al. (2025) suggest a “two-lane approach”, where lane 1 is controlled, in-person assessment, and lane 2 is open in the sense that there is no attempt to control how students use GenAI. But lane 2 can of course be structured in a way to discourage misuse, like having someone sign off on data produced in a lab so that students have to continue with that data set rather than generating synthetic data; or checkpoints where students discuss their work and then asynchronously build on the ideas discussed with the instructor. Corbin et al. (2025) conclude that “The path forward through this increasingly challenging terrain [of increasing AI capabilities] lies not in more sophisticated rules about AI use, but in fundamentally redesigning how we structure assessments to demonstrate student capability. This will require significant effort and creativity from educators but has the advantage of allowing for genuine solutions to maintaining assessment validity in an AI-enabled world. These must be solutions based not on what we say, but on what we do.” So – how do we design cheat-proof assessment? There are of course attempts like going back to pen-and-paper (but can we detect AI glasses? And is writing by hand under time pressure really an inclusive and accessible way to assess learning?) or oral assessments (what about test anxiety or teacher biases?). But it is a slippery slope where we might slide from teaching into policing. Read more: Corbin, T., Dawson, P., & Liu, D. (2025). Talk is cheap: why structural assessment changes are needed for a time of GenAI. Assessment & Evaluation in Higher Education, 50(7), 1087–1097. https://doi.org/10.1080/02602938.2025.2503964 An earlier version of this post was first published by Mirjam Glessmer on https://mirjamglessmer.com/2026/04/13/currently-reading-corbin-et-al-2025-on-talk-is-cheap-why-structural-assessment-changes-are-needed-for-a-time-of-genai/ and is reposted here with permission as an example for possible content on this blog.
The argument that “AI is here to stay” and therefore we have to use it is not a valid one. The future is always undetermined and we do have influence on how it turns out. And that includes whether AI is staying -- or not. So what if the critical AI skill of our era, as Karen Costa puts it, was not how to use it, but how to resist it? There are five main reasons for AI resistance, according to Şimşek and Yasar (2025): Socio-economic concerns, like AI taking people’s jobs, Ethical issues, since AI systems are opaque and biased, Safety risks, when AI gets to make decisions, Threats to democracy and sovereignty, “including the use of AI for large-scale societal manipulation“, and Environmental impact. All of these are serious and worth exploring in detail. So if we wanted to resist AI, for these reasons or others, fully or just inducing enough friction in the adoption to make sure we don’t move faster than we have the chance to safely assess what we are doing, how would we do that? Drimmer & Nygren (2025) suggest four small acts of friction: Centering students in our teaching. Not AI, not preventing cheat with AI, just students. They write that “[d]efensive maneuvers, like in-class essay writing exclusively, are acts of deprivation. They deprive students of the opportunity to reflect and refine away from the pressures of the classroom clock.” Instead, we should keep writing assignments and not police how they were generated, but rather make them thought-provoking and give thoughtful feedback on the thoughts reported in them. Not optimizing everything. Making space for things that are not on the curriculum and that are not credited: “Reading groups, lightning round presentations, unambitious programs of being in simple, un-CV-able conversations” to focus back on humanity and community, and make space for skepticism. Taking some things offline again: Sharing printouts, using leaflets, not making everything be online all the time (but careful that we do not roll back all efforts on inclusion that have been made thanks to assistive technologies!). Ask questions — in person, in established relationships — about what problem we are really trying to fix by adopting AI, and whether we are really working on the right problem. What do you think? Is resisting AI the real AI skill of the era? If you are resisting or introducing friction, how are you doing it? --- Read more: Drimmer & Nygren (2025). Four Frictions: or, How to Resist AI in Education. https://www.publicbooks.org/four-frictions-or-how-to-resist-ai-in-education/ Şimşek and Yasar (2025). From Rejection to Regulation: Mapping the Landscape of AI Resistance. Available here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5287068 --- An earlier version of this post was first published by Mirjam Glessmer on https://mirjamglessmer.com/2026/03/11/thinking-about-ai-resistance/ and is reposted here with permission as an example for possible content on this blog.
My student submitted a text and I am 99% sure that it is written by AI. It uses all those words that are all over AI-generated texts, like “thus”, and “leverage”, and I just feel it in my gut. So I uploaded the student’s text to ChatGPT and asked “did you write this?” and it confirmed that it had written it. I even uploaded it to one of those AI checkers, and it gave me a score of 83% AI generated. I confronted the student, but they deny having used ChatGPT or any other AI at all. What can I do? I don’t want to escalate this if I am not 100% sure.