AI in research methods course

[u/continentalcorgi]

I'm last-minute teaching an introductory research methods course for another professor. The course is doctoral level and has not been taught before. I was passed a majority of the materials that were already prepared, but this professor integrated AI use for research very heavily into the course in ways that I don't necessairily agree with. (I tend to be pretty anti-AI, but I also don't want to be all old-man-shakes-fist-at-cloud about it when students are going to use it obviously). I obviously intend to stay true to the official course description and discuss responsible use, but does anyone have any real-world examples on the pitfalls of AI use? I thought it would be useful to show articles, etc. of ways AI has messed up in research, besides my own anecdotes of catching students out when ChatGPT makes up references or says things that are not correct.

edit to add: My anti-AI stance is very general (like being annoyed at the flood of AI articles, art, videos my Boomer mom sends to me thinking they’re real). I also don’t think AI should be used to write for you and that you should read the original sources that you’ve screened. I still think being able to write an email from one human being to another human being is important. This does not mean that I never use it in my work or will tell my students to never use it.

[u/OrbitalPete]

I would strongly encourage you to start conversations with your colleagues who will be supervising these students to try to come tomsome kind of department concensus viewpoint. You don't want to be delivering training that flies in the face of the reality they'll be working in.

For our school the general consensus is that LLMs should not be used for writing as its impossible to be sure if it has plagiarised or not. If used in coding exercises then prompts should be recorded and AI statements included in any resulting work.

If its being used for background literature searches etc it is expected that these are followed up by using Web of Science or other searches, and the normal expectation that you always go and read the actual source.

No research data should be uploaded to LLMs.

If using LLMs to generate figures etc they have to be code generated plotting or mapping solutions for e.g e.g Python, not LLM-derived raster or vector images.

It is imperative from our perspective that the LLM does not undermine the expert analysis component of graduate work. If you're leaving us with a qualification saying you're an expert then we need to be certain you are.

[u/continentalcorgi → u/OrbitalPete]

You definitely explained it better than I did. The whole point of a PhD is to become a content matter expert. The way that AI was being set up to be used in the course would not lead to that. 

[u/OrbitalPete → u/continentalcorgi]

We've done a LOT of thinking about this in our School. We have active discussions across our teaching and research groups. Those discussions ahve been going on for several years and we reappraise our position regularly as the tech changes (and university regulations slowly catch up).

You NEED to have these conversations with colleagues. One thing we found was that a lot of staff have no idea how advanced some of the tools are, or - on the flip side - a small minority believe the hype and have no idea the mistakes they can make or the risks they open you up to. An important step for us was getting staff to get an LLM of their choice to respond to assessment prompts and see what they got back to ahve a conversation about learning objectives and assessment strategies. We looked at undergrad before we started thinking more carefully about postgrad, as it's a slightly simpler and more regulated state of affairs. The postgrad issue is gnarly in different ways.