Jan-Fredrik Olsen
Responses
Can we "force" students to use AI?
The description of your colleague actually applies to me quite well. I have reflected on whether it is legitimate to “force” students to use generative AI, and while I do not consider this an easy question that I have 'solved', I'll try to share some of my perspectives. First, I want to consider the pragmatic perspective. That is, what does use of generative AI allow us to do that we otherwise cannot? The pragmatic perspective can be considered from a ‘professional’ and ‘educational’ perspective. Professionally, at least in my own discipline (mathematics), there is no doubt of the relevance of generative AI. Not only is generative AI able to solve research level problems, but it has also been used to (partially) solve one of the most famous and long standing problems in all of mathematics - The Navier-Stokes problem. Educationally, it seems clear to me that generative AI is a double-edged sword. By this I mean that productive use can supercharge learning (e.g., by providing personalised explanations), but it can also undermine the learning process (e.g., by such personalised explanations which risk removing the ‘productive struggle’ necessary for learning). Given that many students are likely to use generative AI as part of their studies (whether we like it or not), and are highly likely to encounter generative AI when they enter the workforce, is it ethical of us, as teachers, to leave them to separate productive from unproductive use on their own? Pushing the ethical dimension further, one could also argue that we need to help students engage critically and responsibly with generative AI because the technology is so controversial. I see this latter point as being aligned with my identity as researcher and teacher at the university level. My job is to systematically and critically approach difficult problems (ideally) of relevance to society, where knowledge is both the tool and goal. While personal beliefs, emotions, and prejudices should not be completely dismissed, they should be constantly challenged through critical and responsible engagement — just as we must be willing to challenge our ‘a priori’ knowledge (and methodology). To me, being a researcher is kind of like being Indiana Jones, with the thrill of exploring the unknown coming at the cost of intellectual, emotional, and physical discomfort. And as teachers, our jobs is to invite students to take part of this process (appropriately scaffolded, of course). But does this I mean that I think we should “force” students to use generative AI? In my class, I try to sidestep this issue by giving the students free choice of which AI to use (and what to use it for). I explicitly state that students are free to choose small AI models, which they can run locally on their own computer (more than 2 million different models are currently available). When the environmental cost is brought down this way, I perceive the issue to be similar to whether we should allow students to refuse other uses of technology in our courses. In this case, I think it comes down to the specific learning goals. If skilled use in the specific technology is a learning goal, then they should not be allowed to refuse. But if the actual use of the technology itself is less important (than, say, manual mastery of related concepts), then refusal may be OK. To conclude, I think that exactly because AI is controversial, we need to engage, not stick our heads in the sand. But we should do this for good reasons. If students push back, enter into a dialogue with them and try to find ways to give them room to manoeuvre. And work to make the point of why AI is being used visible.
How can I teach ethics of AI use?
Why not have students suggest 'dilemmas' that they themselves encounter with respect to AI use? Maybe have them spend 5-10 minutes of a lecture to discuss in pairs, and let them submit such dilemmas (anonymously) using some Padlet or Mentimeter system. Discuss a few of these dilemmas with the class (without aiming at 'solving' them), and then let them work on their 'favourite' dilemma in small groups for 10-15 minutes. After this, have a group discussion, before asking them to submit their reflections using the same system (here Padlet really works well, as one can post 'comments' to the posted dilemmas). Share these with the students and write some type of "teacher's reflection". Be humble and explicit about neither you, nor anyone else, having good answers to these dilemmas, and it the real learning goal is to reflect on these issues. If you do this in a couple of courses, and maybe discuss with colleagues doing the same thing (and even reading what researchers have to say about this -- and people in other fields), then you will maybe develop some type of competency on the matter -- and maybe you'll end up giving expert advice to others on these matters :-)
How can I use GenAI to make my teaching better?
If you are using a Windows based computer, with a full suite of Microsoft based tools, then my impression is that Copilot can help you out quite a bit with emails, files, folders and so on. If not, my impression is that you can, say, use the desktop version of ChatGPT to take control of your computer and/or work in parallel with you (even taking control of your mouse cursor). However, these are not use cases that I feel particularly attracted to (September 2026). I especially avoid writing emails with the help of genAI. Having said that, one use case of generative AI that I find attractive, is to record my thinking in words (using some type of VoiceMemo software with automatic transcription or speaking directly to an LLM) and then ask the LLM to condense this into a short note or memo that I can use later on. However, I never seem to read the (mountain of) memos this results in, so I am doing this less and less. An interesting, and new term that I think will capture some of what will happen going forward, is "the infinite backlog". Basically, it means that no matter how much more efficient you get (by using genAI), you will never run out of things that you do not have the time to do. The take-away is that it might not be worth it to maximise your efficiency using genAI as you will never run out of things that you do not have the time to do. As an educator, I find it more relevant to make sure that I keep an open line of communication with students to stay in touch with their use. Indeed, I see generative AI as a double-edged sword that can either undermine or 'supercharge' the learning of students. For this reason, I see it as more important than ever to focus on the human aspect of teaching (and learning). But that is a matter for another post :-)
Should a student use GenAI to polish their text?
Writing may have several functions, two of which are (1) communication with others and (2) thinking -- which could be understood as communication with oneself. To start with the easy part first, it seems fairly clear to me that your thesis advisor does not want you to short-circuit your use of writing as a way to develop your thinking. So don't do that. The more nuanced part is the function of writing as communication with others. Here, there are multiple sub-functions. Again, let us highlight two of these: (1a) writing according to the 'genre' of your discipline, (1b) writing according to the rules of grammar and syntax of whatever language you are using. Again, it seems fairly clear to me that your advisor does not want you to short-circuit practising writing according to the 'genre' of your discipline. In particular, it is hard to completely decouple this function of writing from the 'thinking' part, since adhering to, say, the genre standards of writing mathematical proof is very much aligned with adopting a 'mathematical' mindset. So this leaves the use of generative AI to ensure that you write according to the rules of grammar and syntax. I assume this is what your advisor has in mind. However, again, it is not entirely obvious that this can always be decoupled from the other functions of writing. Indeed, if you have poor mastery of your discipline (or of anything else), it becomes hard to find elegant formulations. And when you do find those elegant formulations (yourself), this may be exactly what will trigger those "aha" moments that are the sign of new insights. So, the bottom line is that, unfortunately, that there is no easy answer to your question. But I hope this attempt at addressing it helps.
Sometimes it feels as if AI arriving has sucked all the joy out of being a teacher.
It would really help if you were more specific. If you, say, enjoyed teaching because you were the sole authoritative source of information for students, then I can see how generative AI can be a bummer. On the other hand, if you found joy in helping your students use the tools of your discipline to better understand societal or natural phenomena (or something entirely different), then you could view generative AI as a juicy target to attack together with your students. Likely, you found joy in teaching for other reasons than what I suggest above. Basically, my advice is that you reflect on exactly why you used to find your teaching meaningful, and then double down on protecting this. If you see generative AI as a threat to learning within your discipline, then why not make this part of your course? But again, if you want more specific advice, please be more specific.
My student wants to connect Claude to Canvas -- what do I say?
I am fairly close to the “let’s go all in on AI in our teaching” side of the spectrum, yet my immediate thought is that this would be a bad idea. While there is a lot to be gained by allowing students to go “all in” on AI use, this has to be balanced by “AI-free zones” to help students recognise the difference between “unproductive” and “productive” struggle, and more importantly, realise the dangers of the real snake in the grass: “unproductive AI-aided success”. So a question is whether “Canvas” should be one of those AI-free zones and why? But while I am sceptical, I also realise that I have never been a ‘young’ student trying to navigate the Canvas website among all the other noise of university life in a world with social media on all sides, etc. So, I think the best course of action is not to give the student any knee-jerk response (even if we all want to), but instead to engage with the student to figure out what they want to achieve by connecting Claude to Canvas and what the context of this request is. Would it be possible to get more information from the student?
How good is generative AI at maths?
Generative AI is not only good at mathematics, it is good enough to be a valuable help for professional mathematicians. For more on this, see the blog of Timothy Gowers and a recent essay by Terence Tao (links below). Both are highly respected in the mathematical community. However, generative AI is not reliably good. While expert users can use generative AI to prove theorems and solve problems beyond what was possible before generative AI, this makes it a problematic tool for non-expert users. Indeed, since generative AI is probabilistic (it does a little internal lottery for every word it outputs) it will make mistakes every now and then. This is not a big deal for experts, since they will be able to spot (or at least suspect) that something is off. But for novices, they can easily be misled and confused. I am not an expert on the technical reason why generative AI is as good at mathematics as it is, but it is related to the fact that mathematics is a highly structured and logical language -- just like programming languages, which generative AI is even better at. However, a reason generative AI may be a bit worse at mathematics than programming, is that its training data contains many executable programs, while mathematics is often written up in (partially) informal ways (and there is a lot of bad maths online). In recent years, a programming language called LEAN has been developped, and it actually represents mathematical arguments as programs. That is, when you first start using LEAN, you can program axiomatic proofs of simple statements by writing down the axioms you use, line by line, and connecting them with logical statements. You can then store such 'programs' and re-use them when making programs that prove more advanced results. These 'programs' are exactly the propositions, theorems and lemmas of mathematics. When using LEAN, the reliability with which generative AI can do mathematics seems to increase dramatically. But what does this say about teaching mathematics in school? I am actually an optimist. As I said above, the power of generative AI for doing mathematics is (still) linked to whether the AI is collaborating with a human mathematician. So, while I think role and status of mathematics may change in the future, I don't think humans will be taken out of the equation. And the humans will still need to have a solid manual and 'old school' mastery of mathematical concepts, techniques and ideas in order to get the most out of the AI (and vice versa, one could say). From this perspective, I think the more interesting question is: how can we use generative AI to have humans learn mathematics in a better, more robust and interesting way? I am myself teaching introductory calculus at Lund University, allowing free use of generative AI by students (even on homeworks and projects), and getting better results than ever on completely AI-free traditional closed book exams and on one hour theoretical oral exams. I think the trick is that by carefully changing the course structure, and course activites, I have been able to get students to use generative AI to experiment with mathematics, and 'see' much further than what they could before (learning mathematics has always been a bit like walking in a dense fog -- it is very hard to look ahead). In this way, students can become exited about mathematics they do not yet fully understand (just as students can in other subjects - such as in physics and in music), and then make it their business to actually do the hard work necessary to develop the 'manual' mathematical mastery of having the please of understanding and being able to grasp these advanced mathematical concepts and ideas themselves. The exact details of what I do are too long to explain here -- and I hope to publish more about it elsewhere. In the meantime, please contact me for addition details. Below, I provide links to course evaluations of the courses I have taught in this way. They are quite detailed, and at the very end, you can see free-text responses on what students appreciated and what they thought could be improved. Note that the most successful was One Variable Calculus in autumn 2024. The course taught in spring 2025 was a completely new course, which also had some "teething" problems. The evaluation for the same course from spring 2026 will appear soon, and this fall I will be teaching the One Variable course using the same AI-integrated approach. One Variable Calculus - Autumn 2024: https://www.maths.lu.se/fileadmin/maths/Matematik_NF/Kursutvaerderingar/HT2024/MATA31HT24.pdf Introduction to higher analysis - spring 2025: https://www.maths.lu.se/fileadmin/maths/Matematik_NF/Kursutvaerderingar/VT2025/MATB33VT25.pdf Blog of Tim Gowers: https://gowers.wordpress.com/2026/08/12/what-sort-of-maths-are-llms-good-at/ Essay of Terry Tao: https://arxiv.org/pdf/2608.16753