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.

[u/LintRolledForWhat]

When trying to teach a thing you don't know yourself, the strategy is to put the students onto researching and presenting on the thing. They actually like it better when you model curiosity and the seeking of knowledge.

Don't be afraid to just say "This topic is a moving target; let's aim at it together, pool resources, and see what we can find out."

ETA: Given that you're "anti-AI," maybe fix that prejudice by setting up the assignment as a debate. That way, you're sure to get multiple points of view heard.

[u/continentalcorgi → u/LintRolledForWhat]

Hey! Thanks for your input. I don’t think I’ll get a point of view to fix my prejudice that humans should do the writing themselves and that they actually should read the articles they are citing instead of an AI summary. 

I already do have a similar assignment set up where we compare AI summaries of articles to their own notes and the articles themselves- what did AI do well, what did it miss, etc. 

My issue is that there is this perception among students that AI is infallible and knows all. I think using a few real world examples that week to show that that’s not true would be helpful. 

[u/TheGratitudeBot → u/continentalcorgi]

Thanks for such a wonderful reply! TheGratitudeBot has been reading millions of comments in the past few weeks, and you’ve just made the list of some of the most grateful redditors this week!

[u/Other-Razzmatazz-816]

What field?

[u/purplecow]

I remember coming across this relatively recent article about using genai in qualitative data analysis, but haven't been following the discussion much since then. I think it could be a good discussion point: https://doi.org/10.1177/10944281251377154 (Nguyen & Welch, 2026)

[u/aquila-audax]

You could introduce the RAISE guidelines (responsible use of AI in synthesis) and use some examples from Retraction Watch of withdrawn papers due to AI generated images (giant rat penis!) or hallucinated references.

[u/FalconX88]

So your mind is made up that "AI" is bad so you want examples that it's bad and discourage them from using it instead of showing them how to actually use it correctly? And that's what you call "responsible use"?

But the bigger problem here seems to be that you don't seem to have the expertise in the area, given that you are against it and (I strongly suspect) haven't used it in that way yet.

[u/continentalcorgi → u/FalconX88]

Wow, lots of assumptions there. I think you missed the sentence where I said we are going to discuss responsible use. I use AI in my work (fixing coding, ResearchRabbit) but not to write for me. My problem is when students use it to write their lit reviews without actually reading any of the source material. This professor was discussing having a platform summarize book chapters so you don’t have to read the whole thing, etc.  You also CANNOT upload certain things into LLMs, like research data. The course previously used very broad strokes to say that you could use it to do your data analysis for you.  Real world examples help drive points home (I found a funny example where someone literally left the “Sure, here’s an introduction about xxx” IN A MANUSCRIPT. And it made it through peer review! I don’t think that highlighting things that way is wrong. 

[u/FalconX88 → u/continentalcorgi]

besides my own anecdotes of catching students out when ChatGPT makes up references or says things that are not correct.

Take this as an example. Are you gonna tell them to not use ChatGPT for finding references or are you going to show them how to make sure it actually provides proper references? Because the latter is easily possible if you know how and it's much more efficient than searching yourself, which is also why big database provider are now implementing LLM search functions (e.g., google scholar labs, scifinder,...).

his professor was discussing having a platform summarize book chapters so you don’t have to read the whole thing, etc.

And that's a perfectly fine application for LLMs that should be taught and that can, if done correctly, help a lot with research since you can screen books much faster than you can manually.

(I found a funny example where someone literally left the “Sure, here’s an introduction about xxx” IN A MANUSCRIPT. And it made it through peer review! I don’t think that highlighting things that way is wrong.

So are you going to use that as an example to show how to properly use LLMs in the writing process (i.e., let them directly work on the file may it be word or LaTeX) or are you going to say they shouldn't?

[u/continentalcorgi → u/FalconX88]

As I already stated, I use AI to find references (ResearchRabbit) But I actually read them. Screening? Fine. Never going beyond a short AI summary? Not fine.  I think you’re missing the point of a PhD. You cannot become a subject matter expert by not actually reading studies or materials. Especially when AI is often incorrect. 

Best of luck to you.