Mirjam Glessmer
Centre for Engineering Education at Lund University
Responses
Should I make AI disclosure mandatory?
To add to what Rachel wrote about adding unnecessary stress about something you cannot enforce anyway: several recent studies have shown that students who follow instructions and disclose AI use risk getting lower grades despite doing exactly what they were told to do (Tan et al., 2025; Luo & Dawson, 2026). Due to this very real risk, many students, especially those that use AI a lot, rarely or never disclose their AI use even when it is required (Vetter et al., 2025). -- Luo, J. (Jess), & Dawson, P. (2026). Exploring value judgements in grading: will teachers mark down student work assisted by GenAI, and should they? Studies in Higher Education, 51(9), 1970–1984. https://doi.org/10.1080/03075079.2025.2552825 Tan, X., Wang, C., & Xu, W. (2025). To disclose or not to disclose: Exploring the risk of being transparent about GenAI use in second language writing. Applied Linguistics, amae092. Vetter, M., Farag, I. M., Jiang, J., Lucia, B., & Silvestro, J. (2025). The Hidden Cost of Disclosure: A Multi-institutional Study on Undergraduate Students’ Generative AI Usage and Faculty Accusations. Available at SSRN 5755762.
My students complain they don't know what's allowed, yet they don't even read my AI guidance
Hi, glad to read that you find the GenAI guidance wizard on CanAI.education useful! And I agree that having a good document is only the first step and the more important work happens later. A good way to "make students read" is usually to make it a bit more interactive, for example by not just linking to a document, but encouraging them to annotate it with questions and comments and reply to each other, or by using the document as a basis for discussion. If students have the chance to not just read a guidance that is being given to them but actually shape the guidance itself, that might make it even more relevant for them to engage. A really nice example of that is given in Hingle & Johri (2026), where they ask students to individually develop elements of a guidance based on iterature that was provided by the teacher, their own research, and importantly, their own experiences. For each policy element, they had to motivate why it was important to include in the policy, the benefits of including it, and concerns that various stakeholders, e.g. future employers or teachers, might have. Then, students discussed their elements in groups, focusing on finding consensus on a comprehensive policy which also explains what exactly is covered, when the policy applies, its effect on different student groups, and necessary success factors. In the end, students then reflected on the process and on how their views had changed. In that study, all of this happened in a course on technology and society and in its own module over 4 weeks, so for most other courses an activity of that extent is not feasible. But even asking students to read your guidance document and reflect on it using the questions as above (plus suggesting changes?) could be a really nice exercise and would not have to take a very long time, and it would practically guarantee that they think about it carefully, especially if you are willing to seriously consider and discuss changes that they suggest! --- Hingle, A., and Johri, A. (2026), “A guided inquiry approach to students co-designing generative AI course policies”. Artificial Intelligence in Education, Vol. 2 No. 4 pp. 91–109, doi: https://doi.org/10.1108/AIIE-05-2025-0123
How do we react to AI wearables?
I think not approaching the student was a good reaction for all the reasons you describe! Maybe, depending on class size and your general relationship with students, it is possible to ask a general question at a later date about if students have experiences with wearable AI devices that they would be willing to share with you. In that case, I would make sure that they understand that you are asking out of curiosity about the technology, not trying to control them, and that it is totally up to them whether they want to tell you or not. It is probably always a good policy to assume that we cannot recognise wearable AI devices. No matter how well-informed and up-to-date we might try to be, they are bound to get smaller and smaller and more and more difficult to recognise. And just because something can technically run AI does not necessarily mean that it is currently running it, too. I read a super interesting article by Corbin et al. (2026) this morning that won't help you with your question but that is well worth a read if you are curious about wearable AI in general! --- Corbin, T., Sharpe, S., & Dawson, P. (2026). On AI glasses and wearable AI in assessment. Assessment & Evaluation in Higher Education, 1–17. https://doi.org/10.1080/02602938.2026.2661367
How can I use AI as a tutor to support my students' learning?
I don't know if I want to convince you (and I don't know if I am convinced myself), but I recently read several studies about AI tutors that made me feel slightly more positive towards the idea. The way that AI tutors were used in those studies was to set them up in ways that minimise hallucinations (which can never be excluded completely, but the GenAI can be trained on the exact materials we want), do not give students complete solutions but rather help them with the next step, and are generally supportive and encouraging of a growth mindset. In those studies, students did learn as much or even more from the AI tutors as they did in in-person active learning control groups, and they appreciated the self-paced nature. Kestin et al. (2025) might be an interesting article to read in that context. The conceptual paper by Brcic & Frljic (2026) gives a nice framework for when and how to use, and importantly when not to use AI on a very small-scale level, definitely worth having a look at that article, especially their Table 1 and 2! What all the studies I read warn against, though, is using unguarded GenAI as tutor, that seems to get used too often as a crutch and thus be counterproductive to learning. And what I think is way under-discussed is how to use AI tutors in ways that don't discriminate students (as in "you are someone who should really work with the AI tutor so you get up to speed before you come to class", or "if you cannot afford a human tutor, use the free AI one!"). One use case that I liked, described by Wang et al. (2024), was using AI not to tutor students directly, but to support human tutors by providing them with suggestions, combining content and good pedagogical practice, for how to react to students. Of course that might also be a slippery slope of the human tutor more and more uncritically relying on the AI suggestions, which might have unintended side effects on both the human tutors and their students, but that was not explored in that study. So in a nutshell: AI tutors might be worth experimenting with! --- Brcic, M., & Frljic, S. (2026). The Effortless Trap: Productive Struggle, AI, and the Illusion of Learning. arXiv preprint arXiv:2606.26181. Kestin, G., Miller, K., Klales, A. et al. (2025) AI tutoring outperforms in-class active learning: an RCT introducing a novel research-based design in an authentic educational setting. Sci Rep 15, 17458. https://doi.org/10.1038/s41598-025-97652-6 Wang, R. E., Ribeiro, A. T., Robinson, C. D., Loeb, S., & Demszky, D. (2024). Tutor copilot: A human-ai approach for scaling real-time expertise. arXiv preprint arXiv:2410.03017.
Should I make AI disclosure mandatory?
Maybe it would be helpful to try to write your own AI disclosure for a work day to develop a feeling for what it means in practice. Today, for example, I used AI to remove the background of an image that I wanted to use on a slide, and to proof-read a text I had written in a foreign language (both good uses of AI in my book). I forgot to add "-ai" to a google search, so I read the start of the AI summary before moving on to the documents I had originally been looking for (usually I avoid AI summaries because I am trying to get to the original source). I released an email from the quarantine folder that it had been placed in based on some AI (there is nothing I can do about the spam filters that the university puts in place, except trying to train them to what kind of messages I consider spam vs want to see). And I looked through my LinkedIn feed "most relevant first", meaning the order in which I saw things was set by some AI (usually I read "most recent first", but sometimes it is nice to get an overview over what is going on on LinkedIn, even knowing that the overview is biased in ways I don't know). If a reflection of this sort turns out to be meaningful to you, maybe it is something that you can start doing and discussing with your students, too. Talking about these things openly and developing a shared understanding of what is and isn't good use of AI is, in my opinion, the much much better approach...
How can we talk about AI in ways that don't imply that it is somehow human?
Yes! I think about that almost daily when I hear kids on the bus mention how they talk to “chattis” (which seems to be a common nickname for ChatGPT around here) about all kinds of topics. But even in professional contexts, we (me definitely very much included) talk about AI as if it was some kind of person sitting in a box. Inie et al. (2026) investigate the type of language people use to write about AI in scientific texts, news articles, and company blog posts. They create a taxonomy with eight categories of anthropomorphization: Cognizer (when the system is portraied as having cognition, for example through descriptions like intelligent, or as doing activities like learning or believing. Products of cognition (for example skills, capabilities, and being biased can only happen if there was cognition first [but reflecting bias is different from being biased!]) Emotion (e.g. struggling, caring) Communication (e.g. answering, following instructions, explaining, suggesting) Agent (helping, facilitating, leveraging) Human role analogy (e.g. tutor, assistant) Names and pronouns (in Sweden they often talk about talking with “chattis”! And the list of pronouns the authors present here don’t include “it”, but maybe it should be included?) Biological metaphors (e.g. seeing, consuming information) Inie et al. (2026) report that anthropomorphization is described in many studies as a deliberate strategy to increase user trust in “probabilistic automation systems” and that “A recent article found that anthropomorphism in the sense of how human-like an “AI-agent” appears is actually a greater predictor of acceptance and adoption of the technology than trust (Gefen, et al., 2025)” (and I did not follow the reference yet). Inie et al. (2026) describe anthropomorphization as risky in four different ways: risk of misplaced trust and over-reliance (i.e. using AI for tasks it isn’t designed for, like ethical or health advice) risk of spillover effects of overestimation (e.g. since AI produces good texts, it can also give health advice) risks related to accountability (whose fault is it if someone follows bad advice from an AI?) risk of disproportionate impact to different populations (are elderly people going to be more easily tricked by AI voices designed to trick them into believing their grandkids need money? Angsty teenagers seeking advice from AI rather than from humans?) So what can we do? Inie et al. (2026) provide some suggestions for de-anthropomorphized language, organized in the categories they presented above: Cognizer & products of cognition: Make it about an algorithm performing operations and people doing the thinking, e.g. “artificial intelligence -> probabilistic automation“, “image recognition -> image labeling“, “speech recognition -> automatic transcription“, “the model shows bias -> the model reflects bias“, “model mistakes -> model errors“, “chatbots are good at … -> chatbots are good for …“ Emotion: Just don’t do it! :-) Communication: e.g. “prompt -> text input“, “answer -> output“ Agent: “Turns of phrase that locate agency with a machine often serve to obfuscate the interests and goals of people. We suggest revising to locate agency with people or choosing less agentive verbs“, e.g. “ChatGPT assisted students -> the students used ChatGPT“ Human role analogy: Algorithms are tools that people use; calling them human role analogies are overclaims “that describe what a developer might wish they could develop — for those who want to replace people in these roles“ Names and pronouns: e.g., “who’s right? -> is the machine output correct?“ Biological metaphors, e.g., “the model consumes data -> data is used in setting model weights“ Is it really worth the effort to try and change how we (and everybody else) talks about AI, especially when there are established terms (like “AI” itself)? Inie et al. (2026) argue that yes, it is, even in scientific articles, since “a better term will make the actual claims of the paper more realistic and clearer“. And I am (as most of the time) thinking about learning and teaching and academic development — I think changing language is super relevant here, too, to mitigate the risks Inie et al. (2026) describe (see above). And after all, teaching is about telling people what they don’t know to ask for yet (that’s my interpretation of Biesta)! Relatedly, a colleague recently pointed me to the post "How the UN’s Scientific Panel Erases Human Responsibility for AI" by Eryk Salvaggio, who warns against another risk, talking about AI as if it was a "force of nature", and writes "If we name the annotators, moderators, developers, and decision-makers the existing definition erases, we center the power of the people who make AI what it is, rather than treating humankind as passive observers of its inevitable arrival. This would shift the question from what AI does to what companies, engineers, communities and governments desire or design technology to do, opening space for democratic participation in setting goals and weighing trade-offs." which I think is a really important point, too. So much to think about! --- Inie, N., Zukerman, P., & Bender, E. M. (2026). De-anthropomorphizing “AI”: From wishful mnemonics to accurate nomenclature. First Monday.
Can we "force" students to use AI?
Provided that you have a good reason for students to engage with GenAI, and depending on which part of engagement with GenAI your student is objecting to (and I am assuming that it is about actually prompting a model and thereby contributing to the environmental costs that are triggered by that) and what the indended learning outcomes are, there might also be work-arounds like working in groups so that the student who does not want to prompt themself is still discussing prompts and outputs with their peers, but someone else is the one to actually "press the trigger" (but if I were the student who does not want to prompt, this would not satisfy me) working with prompts and corresponding outputs that you provide and that the students still analyse critically, or fact-check, or whatever you wanted them to do with it (and in that case they are working with a source you provide like they would with any source you provide) critically discussing a prompt that students write and what kind of answer to expect without actually submitting it to GenAI In the end, I think the best way forward is to discuss their concerns with students and find ways with them to learn what they need to learn while respecting the boundary they articulate.
What do I need to tell my students about what they are (not) allowed to do with GenAI in my course?
P.S.: This isn't so much about what students are allowed to do or not, but I recently came across a couple of really good posts by Jason Gulya on LinkedIn, where he shares text snippets that he has on his syllabus that I think are really interesting and worth considering, e.g. about "the right to ask questions about processes" that both teachers and students have "my commitment to you" where the teacher promises to not use GenAI to communicate with students through feedback or emails etc
How can I teach ethics of AI use?
Hej, great question! Since it sounds like your course is not about ethics or AI directly, one way to address the topic is to not make it an explicit unit (which might seem disconnected from the rest of your course, and therefore possibly not very relevant to your students), but to bring up the topic with smaller comments or considerations over and over again, whereever it seems appropriate, and from whichever angle you are most concerned about. That could mean to mention data center water use in regions that are fighting draughts already without the added stress, or mental health effects that training LLMs has on people doing the training, or how intellectual property is being used without consent. These could be just one or two sentences each instance, combined with the willingness to discuss each a bit more if students have questions. And once you start looking for connections between your subject and AI ethics, there are probably many instances where it is actually not far fetched to address the topic explicitly! If you do want to make AI ethics a bigger topic because it deserves that attention, there are many resources available that you could lean on for inspiration, for example https://teachingaiethics.com/resources/ is a database that you can filter by 25 different subjects and eight ethics topic (bias, copyright, data, emotions, environment, human labour, privacy, truth) and then get case studies and articles to build on The podcast episode https://creators.spotify.com/pod/profile/podnetwork-podcast/episodes/What-If-AI-Ethics-Were-a-Team-Sport--With-Katie-Chiou-and-Lindsay-Doukopoulos-e3oak5t discusses the idea of an "Ethics Bowl", a debate event where teams compete on having throught through ethics topics in the most comprehensive way. They also present more collaborative round-table approaches if competition isn't what you want to do...
Sometimes it feels as if AI arriving has sucked all the joy out of being a teacher.
Hi, that sounds terrible! Without knowing anything about you or your context, first let me tell you that you are not alone. The presence of GenAI is a challenge many teachers struggle with, and it has caused many to question their own role as a teacher now that it seems that knowledge is even more readily available than before and students might feel that they can get personalised instruction from GenAI 24/7. I recently read a study by Paxton et al. (2026) on writing instruction — probably the subject that is affected the most by the presence of GenAI — which I think it is relevant far beyond, where they ask what the presence of GenAI does to teacher emotions. They motivate their study by writing “[t]he emotional well-being of frontline educators has far-reaching consequences, not only for faculty job satisfaction and retention but also for student success (Madigan and Kim, 2021)" (a study in which they find “preliminary evidence that teacher burnout can affect the students they teach"!). Teachers do emotional labour to regulate and care for emotions — their own as well as their students’. Paxton et al. (2026) interviewed eight writing teachers and find teachers emotional responses to GenAI are curiosity, but also anxiety over how writing skills will be valued in the future, and a sense of moral injury (because their jobs as teachers were made so much more difficult, but also because students would now never learn the same skills as before, and also not experience the joy that comes from being a skillful writer, especially in a second language). teachers respond to students’ potential use of GenAI by increased emotional and cognitive labour, and more time working with assessment, especially when GenAI use is suspected. teachers perceive a lack of institutional guidance on how to deal with GenAI and suspected misuse. teachers feel less professional accomplishment compared to “before AI”. From their findings, Paxton et al. (2026) develop recommendations that — of course — include that universities need to provide clearer guidance for how to deal with GenAI and with suspected cheating, but also to “expand the range of supports offered to faculty in the wake of GenAI, specifically addressing the emotional and interpersonal pedagogical effects of the technology. Whereas workshop sessions on integrating GenAI tools into teaching and assignment design proliferate, guidance on addressing issues of trust-building in the [teacher student relationship] would constitute valuable additions” and relatedly, to offer more opportunities to talk about their experiences: “formal support for communities of practice in which instructors can share their experiences with each other for mutual support and acquire guidance from experts". And at least in my context at LU, we are working on all of this! There is maybe not so much you can do about how much support you receive from your university (but sometimes even contacting people whose job it is to support you, for example in educational development units, can change the support you get -- both because you are then in direct contact with them, and also because they can use your request to document the demand and lobby for it higher up, which might ultimately give them more resources to support you and others), but the last recommendation about the communities of practice is something that you can somewhat drive yourself. We recently wrote an article (Glessmer et al., 2026) where we present four grassroot initiatives that teachers had created to talk about teaching in different contexts, and there we share how we interpret design principles for such communties and the success factors and difficulties we identified. Maybe you can initiate a community to talk with colleagues and support each other? Last not least, maybe feeling like teaching is not fun any more is also an opportunity to seek out conversations with students. It is probably not the best approach to tell them that you feel that there is no joy left, but rather to ask them how they feel about the presence of GenAI. Maybe once you create some connections and realise that both you and your students do actually want to talk with, and learn from, actual humans, things will start to feel better? It won't make GenAI go away completely, nor all the challenges that its presence brings to teaching, but it might bring back at least some of the joy in learning and teaching, both for you and your students! -- Glessmer, M. S., Daae, K., Thoni, T., Lundmark, A. M., Dunnett, K., and Herde, H. (2026). Creating grassroots communities of practice to discuss teaching. Oceanography 39(3), https://doi.org/10.5670/oceanog.2026.e300. Madigan, D. J., & Kim, L. E. (2021). Does teacher burnout affect students? A systematic review of its association with academic achievement and student-reported outcomes. International journal of educational research, 105, 101714. Paxton, A., Kang, P., & Yim, A. (2026). The Affective Impact of Generative Artificial Intelligence on Instructors of First-Year Writing Courses. Teaching and Learning Inquiry, 14, 1-17.