Tenure. Promotion. AI.
Our university has detailed directions about the process for earning Tenure and Promotion. There are ambiguities, but the general format is typical among research or scholar/teacher institutions where the tenure-track faculty are expected to engage in research. Many non tenure-track faculty also engage in research activities as well.
The separation between the work our students are doing and the work we are doing are substantial, but many are differences of degree rather than kind. The same anxieties you may confront when thinking about the ethics of a student using AI to outline a paper or check their code can also be turned around onto us. If you are unfamiliar with the RTP process here is a quick primer.
We are hired.
We publish some stuff in journals or books, or maybe write grants.
We teach some classes and have some evidence we have done well.
We provide service to the University and community by staffing committees and participating in other tasks like reviewing our peers.
We are evaluated by our peers against the standards we have agreed to and awarded Tenure and Promotion–or perhaps not.
As you can probably foresee, AI intersects with every stage of this journey.
Getting hired means writing a cover letter, formatting a CV, and showcasing your existing work which may or may not have utilized AI.
Publishing and the creative process in general can be aided by AI. There are outlier cases in which articles have been published (usually in bad journals) with the evidence of AI generation still intact. The more common use case is having AI engage in part of the creative process for you. Personally, I have used AI to convert manuscripts from one citation style to another, a task I have previously paid someone else to do.
Teaching around AI or teaching students how to use AI have become popular topics. The issue of expediting grading or using AI to generate materials we use in teaching like slide decks, assignments, or rubrics is more controversial. In a recent workshop my advice to participants was to outsource the parts of course creation you like the least to AI. I hate creating rubrics and I don’t plan on ever creating one from scratch again.
Service work is often dull and formulaic. You review some manuscripts or documents, write a response for yourself or a constituency, and go to some meetings. Assessment reports and even accreditation documents could pretty easily be produced with AI with adequate prompting and raw data. Reviewing files of our peers is exhausting as they (understandably) pour hours and hundreds of pages of documentation into their cases for promotion. It is easy to see AI could enter multiple areas here.
Finally, the production of the tenure and promotion file itself requires a substantial amount of work. Summarizing and synthesizing years of work into a package people outside your discipline can digest is challenging and the kind of task AI has been able to help with for over a year.
The uncomfortable question we have to ask ourselves is the one we need to ask about the work we are requiring students to do: are we valuing the process or the product? If it is the process of research, revision, struggle, devastating insecurity, and finally publication then we need to put guidelines in place around publication and other tasks. If it is the product we value, then how are we measuring the productivity of someone who uses AI to increase their output against someone who refuses to do so on ethical grounds?
At a minimum we need to engage in the conversation. From where I sit, I’d like to see us embrace the use of AI from the professoriate and adjust our standards as needed. This may open the door to higher standards the way José Antonio Bowen talks about raising our standards of writing evaluation in the age of AI and I would honestly be all for that. Nik and I teach at a public institution with enrollment challenges where we are often asked to do more with less. Using AI to create course materials and aid in the research process (while not raising standards) may be the way we open up time for activities like recruitment we have not previously been involved in.
As we turn the lens back on ourselves we have a lot of difficult conversations in our near future. The point we are making here is: It’s better to have those conversations and fumble through together than not. Along the way we might understand the technology, our peers, and even our students a little better.