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That’s it. Any advice?

The other day, I asked CoPilot something related to my career progression, and the output addressed me by name and knew what type of contract I am currently employed on, which I found quite scary. I know that we aren't supposed to upload student names and other sensitive data to GenAI, so the university seems to have some data privacy concerns there, but at the same time, if the university-provided (and, I am assuming, approved) tool can access my files and emails and browser, I would assume it can access all that data anyway. What happens to this data, what happens to the questions I ask, the input I provide? Can anyone see all of it directly, does it somehow become part of the training data, ...? I am concerned!

A student approached me and asked whether they could connect Claude to Canvas, as they are used to studying with Claude and this would allow them to not change their habits. I don't know what that would look like in detail, but to me it appears that it is going too far?

Hi! I keep hearing that we are supposed to write guidelines for AI use for students, but I have no idea where to start. All colleagues I talk to are super vague and nobody seems to be willing to share what they do with their courses. I don’t even know if they actually have anything written or if they mostly tell their students general things either like “just don’t do it!” or “do whatever, I don’t care”. Is there anyone out there willing to share what they do?

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?

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?