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What does it mean when a thesis supervisor says "You can use GenAI to polish the text, but not for anything else". What are the limits of polishing?

I am highly sceptical of generative AI and don't use it myself, but I know there's a general impression among students that it 'knows everything'. The discussions I have seen have focused on text (logical enough for 'large language models'), and images (many of which are disastrous), but I cannot see how LLMs can reliably do (complex) mathematics where there are many steps to a derivation, some of which are nuanced or require particular systematic techniques, and the final result should be as general as possible. How can I convince students that they will (probably) waste more time using generative AI than working through the difficulties themselves? Or do I have to resign myself to allowing an extra week or more for students to learn for themselves that there really isn't an alternative to putting in the effort and doing the derivation oneself with pen and paper, no matter how hard it is?

I've seen a lot of discussion, mostly in social media and teaching fora, about students using generative AI instead of doing the reading and writing assignments that have been central to university learning. But I've seen little discussion of generative AI in physics or other natural sciences. How worried should I be that some of my students will put their effort into learning to use generative AI rather than learning physics? How good are large language models at physics? And how are things changing as the models are improving?

There are great AI tools out there these days that generate podcasts from any document you upload or link to. I am considering doing that for my course, since students aren’t reading textbooks anymore (let alone scientific articles), so I was thinking that if I gave them a podcast, that might get them to engage with the ideas and concepts. What are the pros and cons?

A master student wants to use AI to generate an image in their thesis. The publisher of the original image does not respond to attempts to contact them, and the student really wants to use this image and says they need it. The image is a photograph of some structure they are discussing in the thesis through a microscope. I do not think generating and using an AI image is right thing to do. What should I do?

I just read a student essay and I am pretty sure that it is AI generated, but I don’t have proof. I know that AI detection software does not work, but I feel like I cannot let students get away with stuff like this!

I have noticed that using AI is a slippery slope – both for myself and for my students. What might start out as asking for feedback on a text that you fully intend to implement yourself after examining it critically and only addressing points that you agree with, quickly turns into AI drafting texts, which then might be more or less thoughtfully edited. How can I avoid getting lazy using these tools and losing skills to AI? And how can I help my students with this?

For some reason, this semester a lot of students responded to free-text questions on my course evaluations (usually nobody replies). While I am grateful on the one hand because there is of course a lot of very useful information in there that I will be able to learn a lot from, I am also overwhelmed and cannot possibly spend hours to analyse all of it. Can I upload it to AI and ask for a summary?

I have implemented an exercise in my course where students generate AI responses to a question and then critique it, with the goal of showing them that they cannot trust AI outputs. But when I gave that task two weeks ago, two students refused to do it, quoting environmental concerns. I myself am also worried about the environmental impact of AI, too, but that is just my gut feeling and not based on any data. Can you help?

I used to rely on essays written at home for course grades, but now with AI provided even by the university (and knowing that AI detectors do not work) this does not work any more. How should I redesign my assessment?

AI literacy is harder than we thought. Recent research (Ahmed et al., 2026) from Stanford & Yale shows that LLMs can extract near-verbatim copyrighted texts. Claude 3.7 Sonnet achieved 95.8% recall for some books. Gemini and Grok extracted over 70% of Harry Potter, without jailbreaking. This phenomenon is known as memorization: the encoding of specific training data in a model’s weights such that it can later be extracted in outputs. Why this matters for education. When students ask an LLM to “explain photosynthesis” or “summarize the themes in 1984,” they assume synthesis across many sources. In most cases, that's true. The problem is that they cannot tell when it is not. Research on memorization shows that, under some conditions, LLMs can reproduce long, near-verbatim passages from specific copyrighted texts. There is no signal indicating whether a response is synthesized or recalled. Under these conditions of opaque generation, attribution-based academic integrity frameworks become difficult to apply meaningfully. Consider this scenario. A student genuinely trying to learn uses AI for help. The AI produces an argument that is largely verbatim from a book the student has never seen. The student internalizes it and later writes an assignment in their own words. The work may be original in expression, but its intellectual provenance is unknowable. Who plagiarized? The student didn't know. The AI can't “know”. Intent, visibility, and traceability have collapsed. This is what students need to understand: LLMs may reproduce specific source material rather than synthesize across sources in some cases You cannot reliably tell whether output is memorized or transformed Attribution becomes structurally impossible when sources are hidden AI obscures the learner’s ability to distinguish synthesis from reproduction, challenging traditional academic integrity frameworks. “Teach them to cite AI” can't be the solution. So what helps? There are no foolproof answers, but one response is to make research accountability explicit. That means teaching the slow, often invisible, work of evidence building so students can verify and defend where their ideas come from. In practice, this means introducing traceability at the level of student process: Annotated bibliographies Research logs documenting search strategies and decisions Evidence tables mapping claims to sources Explicit documentation of rejected sources Treating process documentation as seriously as the final product Follow-up oral presentations and peer questioning This is not a complete solution. Determined students can still game process documentation. But it shifts assessment toward intellectual work we can actually verify: source evaluation, evidence quality, the evolution of thinking across drafts, and the ability to defend choices under questioning. Read more: Ahmed, A., Cooper, A. F., Koyejo, S., & Liang, P. (2026). Extracting books from production language models. arXiv preprint arXiv:2601.02671. Reisner, A. (2026, January 9). AI’s memorization crisis. The Atlantic. https://www.theatlantic.com/technology/2026/01/ai-memorization-research/685552/ This post was first published on LinkedIn by Charlotte von Essen in February 2026 and is reposted here with permission as an example for possible content on this blog. https://www.linkedin.com/posts/charlottevonessen_ai-literacy-is-harder-than-we-thought-research-activity-7416120957548871680-gw3o

We often hear AI described as a "socratic tutor" but there are fundamental differences between how AI supports learning and what Socrates actually did (well, at least what we know from Plato’s Dialogues). The Socratic method is not asking generic questions instead of giving answers. It's about a teacher who asks deliberate questions because they know where the conversation is going. They know the intellectual territory. And they have a pedagogical goal. Socrates knew what misconceptions his students held and could anticipate their likely responses. Each question was then strategically designed to guide the student toward a specific realization. It was carefully orchestrated discovery. Plato portrays Socrates' teaching method as a form of intellectual "midwifery," which helped students give birth to their own ideas. AI is not directional in this way. It can't anticipate specific misconceptions or craft a sequence of questions tailored to dismantle them systematically. This matters because effective teaching often requires: Deep understanding of common misconceptions in a domain A clear diagnosis of where a particular student is stuck A strategic sequence of questions designed to bridge that gap The ability to improvise when student responses take an unexpected path (while still knowing how to redirect toward the learning goal) Socratic teaching requires a level of intentionality and strategic foresight that's fundamentally different from pattern-matching responses. This post was first published on LinkedIn by Charlotte von Essen in December 2025 and is reposted here with permission as an example for possible content on this blog: https://www.linkedin.com/feed/update/urn:li:activity:7396435289897844737/