Kirsty Dunnett
Responses
My student wants to connect Claude to Canvas -- what do I say?
I'll quickly highlight another aspect of the significant potential issues with privacy and data integrity surrounding connecting a Large Language Model (LLM) to Canvas. GDPR is built on a dignity concept of privacy that the US lacks, and the major LLMs are US-based [1], so one can argue that an LLM having access to a course Canvas page is counter to the fundamental European value of the dignity of everyone (students, staff, administrators) who are involved in the course. What I think is more important to think about as a teacher is whether students should be allowed to have a single way of working (even if it 'works for them'). Post-degree a student simply cannot expect to work within their preferred system (even if freelance or in research). There are always systems that one simply has to adapt to, and to which one would never be allowed to grant a LLM access (e.g., what about doing one's tax return?). Thus a student wanting to only use their preferred approach to studying seems to be betraying that they have a shallow understanding of what it means to learn and study at university: their goal seems to be content mastery and nothing else (and their experience has been that an LLM makes that 'easier'). For example, they are not learning how to be in the world, how to work with different systems, how to navigate formal structures. Quite frankly, it's disturbing that these aspects, that I wouldn't even consider part of the 'hidden curriculum' [2], because these are skills for life, not just about succeeding at university, may need to be made part of the formal (explicit) curriculum of university studies. Imagine, every course that is taken predominantly by first year students at the start of their first term has an intended learning outcome 'students will learn to navigate formal university systems (e.g., the learning management system) and appreciate that organisations use a range of specialised systems with specific purposes'? That feels more than a little patronising to me, but maybe that's what generative 'AI' is going to force academia into: catering to the lowest common denominator of possible understanding while teachers have to assume sufficient individual intelligence for university studies. (I cannot help but think this would also force teachers into a horribly uncomfortable state of hypocrisy, and make it much harder to teach with a 'growth mindset'.) [1] Whitman, J. Q. (2004). The two western cultures of privacy: Dignity versus liberty. The Yale Law Journal, 113(6), 1151–1221. https://doi.org/ 10.2307/4135723 [2] Snyder, B. R. (1971). The Hidden Curriculum. MIT Press. --- P.S.: An argument might be that the student must obtain informed consent from everyone who is a member of the course Canvas page. Because access to the personal data is not 'legitimate interest'; a LLM does not have any legitimate interest in the personal data of students taking a course; it it could be argued, then there would be no reason for course pages to require a log in. One missing consent form, even of a student who decided to not take the course, prevents the whole. The teacher who has their doubts may also refuse to give their consent, and even advise (with reason) students against giving theirs. Moreover, consent can be retracted at any time, and the retraction of a single person's consent should be followed immediately by the removal of the LLM's access (arguably, managing the consent should be the teacher or course administrator's responsibility, though the student should make the request unsupported). And this must be repeated with every single course.
Should a student use GenAI to polish their text?
Let's think back a little, before 'automatic language 'correction' or 'improvement'' became an everyday possibility. For years, I've not understood why people seem to not use spell checkers. True, this doesn't prevent grammar errors or homophone confusions (e.g., two, to, too; there, they're, their; break, brake), but it seems so simple, and changing language isn't difficult either (at least once one knows how). I've never used grammar checkers in English, but as I write more in Swedish, I realise that I would benefit from having some sort of grammar checker there (because of the programs I use, it's not something that's easily available). But there's a problem: if a sentence, paragraph or document has mixed tenses, how will a computer know which tense is actually relevant for any given verb? (It's entirely possible that the relevant tense is not even present in the original sentence!) In short: students should always use a spellcheckers. Grammar checkers should also be broadly encouraged (but, in students' first language(s), not considered 'compulsory' in the way of spellcheckers). But these are 'conventional' tools. What about those that propose--or simply make--changes to the language that are in some way 'stylistic'? I strongly dislike Word's language checker that, for example, I encounter in shared documents. For most professional purposes I write in my first language (English), and I am sensitive to nuances and the emphasis that comes from phrasings. I disagree with the majority of Word's language checker's 'suggestions'. To take three examples from a manuscript I am working on: "school teachers" is NOT one word, "appears to combine" should NOT be replaced with "combines" (we are making a judgement), "Perhaps the" should NOT be replaced with "The" (we are suggesting something). To follow these suggestions is to lose preciseness and, in the latter two cases, pervert the meaning of the sentences. I do not know to what extent these suggestions are LLM driven. However, if I click 'ignore' in one editing session, I am met with the same suggestion the next time I go back--which is very irritating and distracting, and also adds considerable pressure to comply just to get rid of the nagging. I sometimes think that students often feel that they 'must' use particular tools, even all the time. Therefore, from a teacher perspective, I would suggest making it explicit to students that while it's no bad thing to see what grammar (and language) suggestions their favourite word processing tool makes, this can be left until late in the writing process, and the bulk of the work done without distraction: turning off all grammar and language suggestions while writing is allowed! (This may actually help students who struggle with 'writers' block' since they are not being reminded to agonise about language while drafting, I don't know.) Now, what about using generative AI for 'polishing', where a text is uploaded and 'improved'? Here I start to doubt usefulness. As Rachel wrote, paragraphs and sections can start to lose their connectedness (I've seen this several times in clearly genAI 'peer'-reviews; it's one of the clearest markers). I have not tried putting in a coherent paragraph (or section) and then asking an LLM to 'polish', but it could be a useful exercise for both teachers and students. A very important point made by Mirjam is that, as in the conventional situation, suggestions should be marked--or changes explicitly tracked. Thus a rewritten sentence should look as it would in a word processer with tracked changes on. I do not know whether LLM programmes (can) do this. There is also the problem of (lost) nuance described above. And the potential perversion of meaning and coherence, especially if done at the paragraph or higher level. When it comes to argumentation, I have experience of peer reviews that are very nicely written 'thing commented on, reasoning, suggestion for authors', 'summarised' in as many words, to an almost dictatorial 'instruction to authors, justification'. It's inhuman and unpleasant to read, and I am pretty sure the 'summarising' was by generative AI. I do not know how universal generative AI's argument 'inversion' is, but it doesn't seem to be how to convince anyone. Logical and convincing arguments are made by leading from premises to conclusions through reasoning, not by post-hoc justification of a preposition. Let me make one final comment on the fact that LLMs are 'next word predictors' and the critical importance of nuance. There is a HUGE difference between 'I really don't want to do something' and 'I don't really want to do something.' (The difference between the two statements is the order of the two words 'don't' and 'really'.) The former is a very strong preference: continuing to do the thing will be under duress (or close to), and the experience may be somewhere in the realm of unpleasant, highly distressing, panic-inducing, or traumatic. The latter statement is much milder, and continuing to do the thing will be OK; perhaps it will not be the most pleasant or enjoyable thing one could do, but nothing more than a transitory discomfort; a bit of relief when it's completed might be expected, but there would never have been any lasting harm. I do not know which is the most frequent ordering in English, but whichever it is, it is presumably the one an LLM will tend to give. And this means an LLM may either exaggerate mild discomfort, or completely invalidate potential trauma. These are the warnings and dangers I see with using LLMs for 'polishing'. It's one thing to upload a single sentence 'I'm not happy with this sentence, can you suggest alternatives', but quite another to request 'polish within style x'. One point I have not discussed, but is of course important, is what students learn by having to think about and make decisions about details themselves or don't learn by passing over the painful work of going through with a fine-toothed comb to a next-word predictor. I would generally advocate to remind students that they can make choices about what sort of human they are, and that it is (arguably increasingly) important that they exert their personal human judgement wherever possible.
How good is generative AI at maths?
I share your scepticism that a 'next word predictor' (Carbone, 2025) is not likely to be trustworthy when it comes to maths. By their very nature, large language models are not 'capable' to logical reasoning (the increasingly common and powerful 'reasoning models' are still statistical), but how to bring this to students' attention? For students doing advanced maths, for example, project work, Carbone's (2025) pre-print may be an interesting and highly relevant read for students as well as teachers and supervisors. It describes how mathematics is incorporated into generative AI (most of generative AI's apparent 'abilities' with maths are feathers unashamedly borrowed by outsourcing many mathematical tasks to specialised deterministic programs and Python libraries), and how generative AI can be used productively for mathematics research (I get the impression this is Carbone's experience). According to Carbone (2025), the main gains of using generative AI in advanced (research) mathematics are for exploring the feasibility of apparently intractable problems, identifying relevant methods from other areas that are needed to address said problems, and for writing the code for non-AI programs to implement (or to be outsourced thereunto). Features such as the finite 'memory' of a generative AI session and various settings that can be adjusted are worth knowing about. Practical guidance on prompting, and warnings that one must prompt in small steps, check every line of the output, and that the entire 'problem' needs to completed within a single memory window can be particularly relevant to emphasise. The bottom line is that the time required to do a complex calculation with generative AI is likely to be no less than that required to do the work without generative AI. If one then adds in that any success requires resource greedy 'reasoning' models, and multiple prompts, that true, logical reasoning is completely absent, that the training data includes retracted articles, typographical errors, multiple conventions (i or j may be used for SQRT(-1), among other variations, and we have no real idea what the training data is or any quality control, there seems to be a strong case for students to do the work themselves, with pen and paper, no matter how hard it is, rather than risk wasting a week or more on work that they then have to redo with pen and paper with full knowledge that they are only progressing by logical steps. I suspect that LLMs' lack of transparency and traceability of methods that is counter to any sort of scientific practice highlight several pre-exisiting weakness in how maths is often presented or talked about -- or rather not talked about. Being told 'it is obvious [from this equation] that the result is [something not obvious if one cannot identify and do the relevant calculation in 10s]' was bad enough before; now LLMs do that without even allowing one to assume that the derivation has actually been done (for examples of good practice when it comes to not presenting all the steps of a derivation, see Landau and Lifshitz, 1960/1976). So much for why advanced mathematics is probably not (yet, maybe never) worth doing any other way than by hand. What about less advanced mathematics? Up to the introductory undergraduate level (differential and integral calculus, linear algebra, and various classical physics topics) Generative AI models can perform as well as students, but with a narrower distribution around 70%, the typical grade boundary for a first class degree in the UK (Walker et al., 2025). However, this is with material that is typically pretty easy to find on the internet and is therefore likely to be present in LLMs training data (Carbone, 2025). Despite the small number of research publications, reviews abound, which is useful for gaining an overview of the potential impact of generative AI programs in mathematics education, though no where near conclusive. For example, Walkington (2025) reports that the impact of students using generative AI in learning maths is very dependent on the study, and for creating adaptive or tailored learning paths (note, these capabilities have existed before in curated apps and programs). The additional 'capability' that comes with LLMs is that problems (practice calculations) can be adapted to versions that are contextually relevant to students, but here it can generate nonsensical problems or create problems with blatantly unreasonable numbers, so the most use here is for teachers developing questions that they then review. However, although generative AI programs can 'guide' students through solving mathematical problems, that students who use generative AI to support their maths learning can have markedly less confidence in their own abilities (cited in Walkington, 2025) is deeply worrisome. Maths is cognitively demanding and has a reputation for difficulty, yet generative AI may prevent the full development of those abilities in many students (which is quite ironic since all computing, including LLMs, is built on mathematics). The impacts of students potentially becoming reliant on LLMs for maths (learning) may have significant impacts on advanced mathematics and mathematics research (although, as Carbone (2025) writes, there are uses for research progress). While this may be starting to become anecdotally available now, I suspect the full scale and impact will not be apparent for another few years. Maths is the discipline with the second highest 'expectation of brilliance' (only exceeded by philosophy; Leslie et al., 2015), and I think every maths teacher — or acquaintance thereof — should be raising questions about the impact of students using generative AI to support maths learning or solve maths problems on the future state, progress, diversity and inclusivity of mathematics. Resources: Carbone, L. (2025). Advancing mathematics research with generative AI. arXiv preprint arXiv:2511.07420. https://arxiv.org/abs/2511.07420v2 Landau, L. D. and Lifshitz, E. M. (1996 [1960/1976]), Course of Theoretical Physics, Volume 1, Mechanics, 3rd Edition. (Any volume, or the 'Shorter Course' will show how they provide very brief explanations of the steps that are not shown.) Leslie, S.-J., Cimpian, A., Meyer, M. & Freeland, E. (2015). Expectations of brilliance underlie gender distributions across academic disciplines. Science 347, 262-265 https://doi.org/10.1126/science.1261375 Walker, B. J., Kalaydzhieva, N., Lameda, B. N., & Reynolds, R. A. (2025). Evaluating undergraduate mathematics examinations in the era of generative AI: a curriculum-level case study. arXiv preprint arXiv:2509.13359. https://arxiv.org/abs/2509.13359 Walkington, C. (2025). The implications of generative artificial intelligence for mathematics education. School Science and Mathematics, 1–10. https://doi.org/10.1111/ssm.18356
How can I approach a student if I suspect that they have used AI in ways that aren’t allowed (but I don’t have proof)?
My experience is with suspecting AI generated 'peer reviews'. Here there tend to be stylistic markers (repetition, lack of connection to content, strict conformity to standard form, formulaic phrasing, contradictory 'assessments', non sequiturs, and more) that accumulate to give an inescapable sense of inhumanity when the review is read through carefully sentence by sentence. You cannot make an accusation on the basis of 'gut instinct' or 'impression'. If you want to raise the issue, you have to collect data and present evidence that can be discussed. This means going through the essay very carefully and marking every place that, when taken together, makes you twitch and how it does so, and put together the argument of why this (cumulatively) makes you strongly suspect generative AI, and why this is evidence of a use that is not allowed. The basis of a just system is 'innocent until proven guilty'; the onus is on the accuser to prove guilt, not on the accused to prove their innocence. Prior knowledge of the students' performance in other tasks should never form part of this 'evidence'; this is simply prejudice. A final grade of B can be obtained by a combination of a C on two exams and an A on the third, or an A on two exams and a D on the third. A student's marks on the various assignments and exams for a module can easily vary by over 30%, without generative AI. The point is that 'unusually' good or bad work may have more to do with the topic, preparation or the conditions immediately surrounding the work, than any rule-breaking. Then there's the question of what to do. Some possibilities include: Discuss the analysed essay and the evidence you have that makes you suspect generative AI use beyond what is considered acceptable with the student individually. The fact that you have spent the time going through their work should help mitigate some of the trust issues: you've shown you cared. So then you have two issues to address: what to do with the current assignment, and what can the students do in future, both of which the student should have a say in. Beyond directly addressing the strong suspicion of 'unacceptable' use of generative AI, a meeting might also address: what is the 'penalty' this time? what learning can they take from the meeting? can you help the student develop an 'action plan' for their future use of generative AI? Raise the issue more generally in class without identifying any particular student (and ideally, also drawing examples, especially of generative AI use, from several essays (if necessary 'create' new ones)). This might take the form of a discussion of one or more of: stylistics, of logic, of structure, of the 'shape' of an essay, or other disciplinary relevant topics. Think about what you're teaching students about essay writing (and make changes). Are you teaching — and allowing — them to think broadly, to consider — and discard — multiple perspectives, only bringing in those that substantially expand their essay (in whatever direction), or are they being 'taught', encouraged, or otherwise learning that an essay should hold itself to a single point, never developing tangents, and that 'depth' can be achieved through introducing irrelevant or arbitrary aspects? (This may include unnecessary theorising, see Kitching, 2008.) Russell et al (2026; preprint) compare generative AI and human-written fiction, finding that "AI stories over-explain themes and favor tidy, single-track plots while human stories frame protagonist' choices as more morally ambiguous and have increased temporal complexity" and that "that AI-generated stories cluster in a shared region of narrative space, while human-authored stories exhibit greater diversity." They also discuss differences between different generative AI models, but the overall inhumanity remains. Kitching, G. (2008). The trouble with theory: the educational costs of postmodernism. Pennsylvania state university press. Russell, J., Rajendhram, R., Pham, C. M., Iyyer, M. and Wieting, J. (2026). StoryScope: Investigating idiosyncrasies in AI fiction. ArXiv preprint https://arxiv.org/abs/2604.03136v4
How to avoid getting lazy using these tools and losing skills to AI
This is a big question — or pair of questions — and the answer is correspondingly long. It's in four parts: in the first, I ask which skills; the second are some ideas for making temptation easier to resist; the third about helping your students (pedagogical aspects); and the final a thought on the systemic context. On the skills 'at risk' Who's losing skills? Individuals or humanity as a species? (Or, rather the subset who have access to and make use of gen AI?) The fear of this reduction in capability, in competence, is one I share, and one of the reasons I staunchly advocate to strongly advise students to not use generative AI (at all if they can bear to, but at least never without very careful thought). I could highlight valuable skills and knowledge like being able to write (string a sentence together), knowing what code does or being able to write computer code in a novel way, being able to do maths, especially in general form, or create figures that say what one actually wants to say, but when I came to write this, I realised that there may be an even bigger skill at risk. Perhaps the two most valuable 'skills' that generative AI erodes at the individual level are the ability to think (which Kosmyna et al.'s 2025 pre-print provides a preliminary quantification of), and one's ability to trust oneself (which I haven't seen discussed, and which I suspect may be even more pernicious): one's trust in one's ability to write text that will be understood — despite imperfections, to do calculations, to solve problems; one's trust in one's own judgement and ability to (critically) evaluate one's own work. It 'promises' improvements; reassurance of being 'on the right lines' or have 'reached the correct answer' but what it becomes is a crutch. Using generative AI runs the risk of leaving the user an intellectual cripple and coward, unable (or at least unwilling) to do anything that is not in some way 'approved' by a large language model (with all its biases). I dare to be hopeful though because, in terms of ability to think, three years is a very short period of time in evolutionary terms (Homo sapiens has been around as a species for c. 200,000 years), but for undergraduate students three years is a big chunk of their lives (around 15% or even more for most). It may be useful to think of generative AI use as an addiction. Avoiding addiction Resisting temptation (or quitting, if the use has become habitual) may not be easy, and is basically a matter of self-discipline, but I have a some suggestions for strategies I would consider. Some of these (making access harder) I used when I spent far too much time (hours a day) playing minesweeper — which I now haven't played for months; those about deliberately making the experience unpleasant are based on the fact that I find generative AI easy to resist because I really dislike the experience. Make accessing the generative AI harder (this may also add a bit of inconvenience into other web browsing, but that's the price one pays for independence, and when the habit is broken, you can relax the browser history ones):Do not have any generative AI web pages bookmarked;If you use institutional access, log out and close the page after every single use (so you have to go through the rigmarole of logging in again next time);Change your browser settings so it doesn't save (or at least doesn't suggest) your browsing history when you type into the search bar. Reduce your exposure to generative AI by adjusting browser and search engine settings so AI summaries are never shown. Deliberately make the experience of using a generative AI interface unpleasant or harder work, e.g., by having a standard 'start of 'conversation'' prompt (which may include prompting for 'slop'). Return generative AI firmly to the roll of a tool: Decide exactly what you will and will not use generative AI for (and which model you will use) — stick the list on your computer screen. If you find yourself thinking 'I could put that through generative AI and see what it 'thinks'', or that you might use generative AI for something not on your list (previous point), do something else! (E.g., go for a walk round the building.) Proofread on paper (NB: paper uses a lot of resources) — or work offline (disconnect from the internet) — in the summer, this might well be outside. But be patient with yourself: your goal is to not become reliant on generative AI — or use it very deliberately. Helping students to become independent humans So much for weaning oneself off generative AI (or making the slippery slope harder to reach and maybe even disgustingly gooey). How about helping your students? The above practical suggestions still apply, and there is some hope since it is students who feel entitled, and for whom going to university is seen more as a rite of passage, and the point is to form connections, including, sickeningly, 'finding a wife', who will use generative AI to cheat, while those for whom a university education is still a genuine educational opportunity that will enable them to do more than they would be able to do without it are more likely to simply boycott the tools (as R. Purser reported in 2025). I'm going to draw up three topics that it may be useful to address with students: general uncertainty and enabling students to self-evaluate; normalising, and providing clarity of declaring AI usage (and never question what is declared); making students aware (or reminding them) of the damage they may be wantonly doing to their abilities, including explaining what they will gain by putting in the effort. 1. Student uncertainty, unwillingness to do or write things unless they were confident it was what was expected, and their struggles evaluating their (and peers') work long predates generative AI. But now they have a means to obtain 'reassurance' that they didn't have before. I suspect generative AI may also be revealing issues of tacit knowledge that have previously been left implicit, or otherwise not deemed necessary to mention. And long-standing complaints (e.g., 'students can't read' — which is actually 'don't have reading stamina') can no longer be ignored. The basic problem of developing judgement and the associated ability to trust one's own work is, I suspect one of (in)experience. Which means students need opportunities to develop an understanding of what 'quality' work is and practice evaluating work. In isolated form, you can spend some time highlighting, and having students discuss, in groups, examples of good and bad work, whether texts, graphs etc. as relevant. This can cover both in general (stylistic) (e.g., Zombie Nouns), and technical aspects ('join the dots' graphs do not show trends; they would be better plotted as discrete data points (the dots) or bar charts so the discreteness of the data points is preserved). To go one step further, developing (even with students), making available to students along with the assignment, and marking using descriptive rubrics (no vague 'good quality x' etc.) are likely to help. Andrade (2005) is a very useful introduction. A certain amount of restructuring of assignments and teaching will be needed, but making expectations clear, and placing students in a position to judge those expectations without hoping that the 'average' outputted by generative AI will be what's expected cannot harm. Although such clarity also enables the student determined to not learn to prompt generative AI to a 'better' output, without such clarity, the temptation to refer to, to check against the 'more knowledgeable' generative AI is higher. 2. The following youtube video (https://www.youtube.com/watch?v\=TWi1eYjkQLE, from 15-21 minutes) describes one way of really integrating generative AI use explicitly into teaching, and making its use part of the learning process. For example, when conducting a literature review, students also submit an AI evidence sheet in which they describe and document their generative AI use, from the models used and the prompts they tried, to the use they made of the output. And this is marked too. From what I understand, this is in conditions where generative AI use is compulsory, but the point is that it rewards clarify, and makes the process both visible, and this visibility part of the learning process. I'm not sure how it translates into conditions where students can decide to not use generative AI at all; but the follow up actions, e.g., performing Google or database searchers, and actually reading the articles, can still be documented. But I'm sure that any claim 'no generative AI use' should not be questioned — or at most explored, on the same basis as generative AI use in a viva (minutes 37-41). There is also what I think is an side comment distinguishing machine learning from generative AI, and the use of local models. 3. The downsides of generative AI are well known, for example as summarised by the IMPACT RISK framework. This, and other, more specific risks, e.g., to thinking ability, as Kosmyna et al.'s (2025) research (note, preprint) points to, can be brought up in discussion with students. I would suggest starting off positive (e.g., 'what can generative AI do or be used for?'), before asking them to consider whether it's all that good (e.g., 'what risks are there, both large scale, and to you as individuals from using generative AI?') and allowing students a role in setting the agenda of which specific issues are discussed (though some you will know you definitely want to bring up). You could even task students with finding out what has been said, in both research and popular venues on these topics (e.g., references here). One thing it may be worth noting is that even (one of) the first developer(s) of chatbots was far from convinced that chatbots, and their extension generative AI programmes, were any sort of a good thing — in 1976 (Tarnoff, 2023). Challenging the system practices A last thought: More radically, and at a systemic level, you might wish to bring up the points raised by Martha Kenney and Martha Lincoln of San Francisco State University (in Purser, 2025), and others like them, at any relevant opportunity (e.g., in meetings discussing a university's strategy on or guidelines around generative AI). Martha Kenney: "Normally when we buy a tech license, it's for software that's supposed to do something specific... but ChatGPT doesn't." Martha Lincoln: "Why would our institution buy a license for a free cheating product?" Many universities have uncritically accepted and, quite literally, bought into generative AI platforms, which effectively endorses or approves their use. The money spent on such licences (most of the platforms can be accessed for free, and even licences are no guarantee of availability) could arguably be much better used, for example, to pay salaries or ensure that staff have work spaces that allow them to work — e.g., individual offices (or at least properly secluded work spaces) for those for whom constant (potential) interruptions from nearby and passing colleagues destroy focus and all hopes of getting anything done. Or at least to ensure "coherence between their stated missions, pedagogical practices, and approaches to emerging technologies" (Taylor and LaCroix, 2026) Repeated attention can also be drawn to the various implications for student learning (or lack thereof) highlighted above (and others). It may also be worth pondering the following sentence from Albert Camus' 'The Rebel' (pg 183): "Work in which one can have an interest, creative work, even if it is badly paid, does not degrade life." Sources Andrade, H. G. (2005). Teaching With Rubrics: The Good, the Bad, and the Ugly. College Teaching, 53(1), 27–31. https://doi.org/10.3200/CTCH.53.1.27-31 Camus, A. (1971 [1951]) The Rebel. Penguin. Translated by A. Bower. Kosmyna N., Hauptmann E., Yuan Y. T., Situ J., Liao X. H., Beresnitzky A. V., Braunstein I., & Maes P. 2025, Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv preprint arXiv:2506.08872. https://arxiv.org/abs/2506.08872 Noon, P. and Myers, T. (2026) AI in Academia: Why You Can’t Govern What You Can’t Question Youtube video https://www.youtube.com/watch?v=TWi1eYjkQLE Purser, R. 2025, AI is Destroying the University and Learning Itself. Current Affairs. https://www.currentaffairs.org/news/ai-is-destroying-the-university-and-learning-itself ; accessed, 2026-06-20 Tarnoff, B. (2023) Weizenbaum’s nightmares: how the inventor of the first chatbot turned against AI. The Guardian, 2023-07-25. https://www.theguardian.com/technology/2023/jul/25/joseph-weizenbaum-inventor-eliza-chatbot-turned-against-artificial-intelligence-ai ; accessed 2026-06-20. Taylor, T. B., LaCroix, T. (2026) Purpose before policy: academic integrity, generative AI, and rhetorical stance. Higher Education. https://doi.org/10.1007/s10734-026-01706-1
Can I use AI to analyse students’ free-text responses?
There's not a lot to add to Mirjam's answer in terms of potentially using generative AI to analyse the responses. However, the question then arises: what sort of analysis do you need for your purposes? Clearly something more systematic than impressions after skimming through, but perhaps not something so labour intensive as inductive thematic coding. You will definitely need to read through all the comments yourself, but a deductive approach to coding (leading to a more quantitative overview) may save you time while avoiding the various problems of generative AI. After all, you were in the course too, have your own thoughts about how it went, know from experience some typical student reactions, and also know what may or may not be possible to change. The main thing to be careful with here it to not set up to confirm your own impressions and thoughts about what to change. A starting list for deductive coding might contain items under the following headers: - new thing(s) tried: liked/disliked. - personal (dis)satisfactions shared by students (list out yours and see which students noted enough to comment on). - reassuring comments (e.g., old things liked; general complements). - typical complaints (be specific: e.g., timing, workload too high). - interesting or unclassifiable comments — to come back to. - concrete suggestions — to come back to. I recommend assigning a maximum of 10-12 codes in any given read through (this really speeds things up because you know almost exactly what each code means). You can mark students' responses as completely coded, so you do not need to return to them in a second read through (i.e., if have more codes than it's easy to more or less remember the meaning of at any one time). The only ones to examine in more detail are then those marked as 'interesting, unclassifiable and concrete suggestions. It may also be efficient to break long comments into their constituent parts so it is easier to keep track of where the code assignment comes from (and also reduce needing to read through parts that have already been coded). From a quick internet search, the following clear description of the different approaches to qualitative coding may be useful: https://limbd.org/the-big-3-qualitative-coding-approaches-inductive-deductive-and-abductive/
I am worried about the environmental impact of using AI. How bad is it really?
This year's exercise is done. So the question is what to do about next year's. I followed through some links from a blog post Mirjam included in an answer to another question, and reached IMPACT-RISK which lists (and is an acronym for) 10 major downsides of generative AI. This led me to think whether can you achieve not only your course goals, through keeping the exercise, avoid a repeat of this year's refusals, and address some of the other downsides. (Your exercise probably already addresses several of the technical ones.) I'm going to suggest two potential lines of development; I'll come to the environment one second because it makes more sense after the first. First: Can it become a group exercise, where students work in a small group, perhaps three, to decide exactly how they will prompt AI to get the answer to the question and also to critique the answer? (While it seems most obvious for students to do this while sitting round a table, I see no reason why this couldn't also all be done by typed chat messages.) This will (or should) reduce the number of prompts (and therefore the environmental costs) from the whole class because fewer will be made. It will also provide an experience that is not 'Knockoff', because it is based around normal human interactions. Second: Can you integrate a discussion of the environmental impact into the task, so students are encouraged to think carefully before they start interacting with AI? (https://what-uses-more.com/ gives some rough estimates of the demands of technology use; and suggests that practices such as Zoom calls without video (at least for most), and setting streaming quality to the lowest possible (unless it really does make a difference) should be normalised.) Things to discuss might include: do they want to use the question as posed, or do they want to add more instructions or information? (e.g., how long and what sort of answer do they want?) And what about follow ups — how many prompts will they allow themselves to use to get an answer? They could set a 'ration' on their resource use — or number of prompts used/outputs generated; if they have a series of prompts, this could also include the time spent between starting to interact with the genAI and stopping (this includes the time spent reading and deciding what to do in reaction to the output). I barely use genAI, but one thing I find really annoying is that it's not obvious how to enter: I want this sort of answer/reaction to the following. [Hit return.] [Here is the question/actual instruction]. The 'hit return' stage initiates a[n entirely unnecessary and unwanted] response. It may be worth flagging this and also how to get the desired behaviour, so prompts really can be kept to a minimum. A quite separate extension that has very little to do with your original question could be for different subgroups to try different models and discuss what that can mean, e.g., for decision making about choosing tools that are fit for what one wants to do, access, 'productivity' in the face of data ownership etc. etc..
“What if the critical AI skill of our era is not how to use it, but how to resist it?”
I staunchly advocate for strongly advising students to not use generative AI: they will not exercise the 'little grey cells' that have brought humans to where we are (however equivocal that position is). Perhaps suggest that they read one or two of Agatha Christie's Poirot mysteries (best on paper; in Europe they're still in copyright as life + 70 years). Equally, it's futile to forbid it, but that's the students' decision: what you can do is warn them about their potential lack of development. Beyond the points outlined by Mirjam, I think there are two further ones: how will one do things if access disappears? (Scharfbillig et al. (2026).) And what image do students want to have or develop of their self-respect, integrity (personal, not just academic), knowledge of independent ability, and the simple satisfaction of having done the work themselves? The conclusion to Adam et al.'s (2025) editorial starts with: "Humans have already entered the GenAI rabbit hole and allowed GenAI to shape their preferences, norms, behaviors, organizations, and societies -- long before the arrival in Wonderland." But, as they write, though there is no going back, that doesn't mean that the idiom: 'look before you leap' needs to be ignored - many have leapt already, but not everyone needs to. The way Adam et al. (2025) frame their discussion around Alice's Adventures in Wonderland is particularly fun: I do recommend reading the book — it can be downloaded freely from project Gutenberg. Resources: Adam, M., Bauer, K., Jussupow, E. et al. Generating Tomorrow’s Me: How Collaborating with Generative AI Changes Humans. Bus Inf Syst Eng 67, 583–594 (2025). https://doi.org/10.1007/s12599-025-00961-3 Amnesty International Report (2026) Unlawful by design: Exposing the human rights costs of generative AI https://www.amnesty.org/en/documents/pol40/0996/2026/en/ Scharfbillig, M., Lewandowsky, S., Altay, S., van Alstyne, M., Kozyreva, A., Hertwig, R., Lorenz-Spreen, P., Diresta, R., Valenzuela, S., Egidy, S., Quattrociocchi, W. and Orben, A. (2026). Fractured reality — How democracy can win the global struggle over the information space, Publications Office of the European Union, Luxembourg, https://data.europa.eu/doi/10.2760/9358883, JRC144603
Can my student use AI to redraw a copyrighted image to include in their thesis?
For me, at least, the underlying ethical issue is that the (to a non-expert blatant) abuse of copyright in training generative AI is no excuse to disregard it in one's own practices. (See also the recent Amnesty International report.) Open a reference book or a work of fiction published since 1988 and with an author who has been dead for less than 50 years (I use 50 because that's the US copyright limit; in Europe it's 70 years). On one of the first few pages (usually, occasionally it's at the back), you will find something along the lines of: 'No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior permission in writing from the publishers'. To me (and I am no expert, but the basics of copyright law are fairly straightforward) it seems clear that copyrighted material, whether from books or research articles that are not published under a creative commons licence, should never be uploaded to an AI without permission from the author or publisher. Even for textbooks that allow for some local or fair use it seems unlikely that this also covers uncontrolled digital distribution - i.e. what happens when an upload is incorporated into a generative AI program's training data. But let's look at it another way: what would the student have done in 2021 (before generative AI was well known and returned at least apparently sensible output)? And what would they (have) learn(t) by doing so? It seems to me that there is an (increasing) addiction to 'pretty pictures', and generative AI images look pretty until one notices the flaws (which might be a lack of floors). Has academic work become nothing more than a fashion show, where 'attractive' has replaced any sort of useful, practical, meaningful, or accurate? Perhaps you can ask the student to sketch (by hand) what they envision a generative AI image to contain. Then there are three options: 1. scan in and include the sketch; 2. create a digital version of the sketch without genAI; 3. scan in the sketch and upload to genAI, asking for a 'pretty' version. Note: with the exception of the students' time, the third will almost certainly use considerably more resources than either of the other two options (https://what-uses-more.com/). And, of course, readers can simply be directed to the original image in its proper place of the original paper.
AI detection tools and even ChatGPT confirm that AI wrote my student’s text, but the student denies it!
Let me start by giving your student the benefit of the doubt: how might such 'AI characteristics' have arisen? Words such as 'thus' and 'leverage' are not exactly common in formal and academic writing - which is often far from 'plain English'. A combination of formal phrasing (including passive voice) and 'jargon' may be what students understand to be 'appropriate academic style'. The student may simply be trying to achieve a 'proper academic style' (or 'voice'), perhaps based on (outdated) formal style guidelines or information about the proper connectives in arguments from school (this could well explain 'thus'). Mimicking what one read last is typical for inexperienced writers 'finding their voice' (see, e.g., King, 2010), so it seems reasonable that a student still learning to write 'academically' will mimic one or more of the papers they refer to most. I also know from my own writing that I go through phases where I use a few words or phrases a lot more than I do at other times, so words used unusually often in one text is not necessarily about generative AI use. It takes more than a few 'characteristic words' to be able to suspect 'this was not written by a human'. Indeed, that students can try too hard, and produce worse work as a result is not new: Kitching (2008) reported students trying too hard to be properly academic and making their work worse as a result — in this case it was about including a confused theoretical discussion, instead of presenting projects as straightforward empirical work, but I think the point of trying too hard to mimic what is perceived as 'correct' and ending up with something that doesn't 'ring true' as it were remains. But there are questions you, as a teacher, have to ask yourself. It's clear now that at least some students will use generative AI for written assignments, but there are a number of ways in which they may use it, all with different implications. I saw a presentation (by André Mathe of the University of Bergen) a few months ago where some lecturers in Norway were interviewed about appropriate uses of generative AI. If I remember correctly, it was typically considered entirely acceptable to use genAI for text editing, and even for idea generation, though the lecturers were pretty much in agreement the the first full draft of the assignment should be the students' own work. So for those lecturers, a student drafting an assignment in everyday, informal language, and then uploading to genAI with a prompt along the lines of 'make more academic' would have been acceptable — and an over abundance of words such as 'thus' and 'leverage' (I'm guessing more common in academic texts than anywhere else) could appear. There are also many things to consider when it comes to exactly why you're inclined to raise the issue to what you suspect may be an AI generated text: a) whether it matters, b) why it matters, c) how it matters, and d) whether (and why and how) it should matter. But the damage you have done your student and their trust in you and perhaps even other academics, however accurate or inaccurate your questioning of them may have been, should not be underestimated. After all, you have the power. Kitching, G. (2008). The trouble with theory: the educational costs of postmodernism. Pennsylvania state university press. King, S. (2010). On writing, a memoir of the craft. 10th anniversary edition. Scribner. André Mathe's talk announcement: https://www4.uib.no/en/research/research-groups/teaching-and-learning-in-higher-education-teled-research-group/events/this-is-not-mine-exploring-the-challenges-metaphors-and-affordances-of-generative-ai-in-design-education; a publication with the same title is somewhere in preparation, so hopefully his observations will soon be available to all.