What Guidance Do We Give Junior Colleagues & Peers on use of AI? – Hedgehog Librarian

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Date: May 4, 2026Author: Abigail Goben

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In my sphere there have started being a number of new conversations about use of GenAI in the use of scholarship and how that will impact assessment for promotion and tenure. We’re having these conversations from a "what advice do we give as a college to each other and to our junior colleagues." It has also come up recently in the emails exchanged for the two external reviews I have been requested to provide this summer. I have explicitly asked for what guidance was provided to the candidates and asserted at least for myself that I won’t put the candidate materials into Co-Pilot, which is something an external reviewer at my institution did last year.

The number of colleagues across disciplines who are speed running and openly delighted at describing how they are offloading tasks that are the core of what we as faculty are privileged to do — creating new knowledge, writing up our findings, sharing our knowledge. There’s always been the "oh and I have to teach" aspects of it for many programs that are training researchers but now there’s also this seeming desire to not do their own research too? It’s unclear what they anticipate their job is to be.

Anyway, an internal colleague asked for some thoughts related to what I was thinking about as we face cases where junior faculty are navigating the weird pressures of "you must AI OR DIE" versus "so you’re offloading your critical thinking to an ethically corrupt and ecologically disastrous auto-complete calculator." Here’s a slightly edited version of what I sent her — with a recognition that this is not a comprehensive document but questions I am noodling on…

We need to define “AI” and stop lumping everything under a single umbrella term. Are we talking about use of spell check and grammar check that catches the wrong their/they’re/there; testing novel machine learning or algorithmic tools that are working across datasets too large to identify correlations; automating some data clean up and standardization; or generating entire literature reviews and inappropriate rat visualizations? There are significant differences in perceived usage varying from “My campus version of Office 365 has spell check and won’t let me turn off autocomplete suggestions” versus "Here is this manuscript, can you tell that I used an extruded text from Co-Pilot for the literature review and I had it "do" the analysis?” The Artificial Intelligence Disclosure (AID) Framework by Karl Weaver may be useful here.

Faculty will need to put in more effort with their manuscripts if they plan to use an LLM.  There is no reason that mentors, peers, peer reviewers, editors, or other colleagues should be asked to put in time and effort reviewing and providing feedback on extruded text manuscripts that you couldn’t be bothered to write, particularly when those manuscripts are half-baked. I’m seeing this with students and it’s creeping into peer review. You are not too busy or important to meaningfully engage in drafting your own manuscript.

The often very obvious use of LLMs to draft literature reviews suggests a lack of curiosity and engagement with the discipline and your colleagues – if you aren’t reading and engaging with your peers, why are you producing materials that you expect them to read? How can you be sure this research actually meets a gap in the literature? How are you critically engaging and creating a story to lead to your question rather than writing slightly shorter sentences than what is in the abstract? It suggests a misunderstanding of the point of a literature review that is not flattering.

Use of LLM tools is likely to reinforce citation biases and has a strong potential to enhance erasure of women, faculty of color, and other minoritized groups in the literature across disciplines. The tools are trained on what is available to them and we know there are significant problems there. The answer cannot be to steal more of our currently copyrighted materials to train a tool that is then sold back to us. The LLM companies are also ingesting retracted papers without capturing the metadata of retraction, which will cause problems where someone aggressively asserts something that has been refuted. Systematic reviews and meta-analyses will get worse and less trust-worthy. This is on top of the disappointing behavior of researchers not bothering to verify that a citation even exists before submitting it to a journal.

The role of tenure and scholarship at our institutions is to create new knowledge. I was given tenure not only on what I had accomplished to date –which absolutely mattered — but because the university believed I would continue generating new knowledge and work that impacted my discipline, the community, and the world. We’re already hearing about how the voice of scholarship is shifting to sound...