Our graduate students are training for professional and academic careers, but our curriculum was built for a world before AI.
This isn’t an indictment; curricula move slowly by design. They encode what a field has agreed on, and agreement takes time. AI adoption is uneven, along what Ethan Mollick calls the jagged edge: strong in some tasks, unreliable in others, and on an improvement trajectory that makes next year (nearly) unforeseeable. A program that rewrote itself around last year’s capabilities would be revising again this year. Caution is a reasonable response.
The cases for using AI in academic work are tricky. Authorship is contested; Data protection rules were written before anyone pasted an interview transcript into a chat window. Faculty attitudes range from enthusiasm to anxiety, fear, and even hatred, and behind these emotions there are real arguments. I don’t think anyone is wrong to be uneasy.
I do think we are getting the cost of waiting for clarity wrong.
Our graduates are entering fields where AI literacy is already assumed. They will submit (and referee) to journals with AI disclosure policies. They will sit on IRB panels. They will be asked by a district or a dean or a search committee what their position is, and “I wasn’t taught that” is not a position. Sending them out without practical skill and a defensible ethical stance is not neutrality. It is a decision, and the cost lands on them.
On our podcast recently, a graduate student walked through how he arrived at his own working relationship with these tools. Not refusal, not delegation. He described testing where the tool helped his thinking and where it replaced thinking he needed to do himself, then settling into a level he could defend. It is about finding the Goldilocks level.
That process is what I want to teach. He got there on his own, which is fine for some but useless as a program design. Some students will figure it out. Others will land on one extreme or the other and stay there, and neither extreme serves them. Guiding students through that calibration is what post-secondary education is for. We do it with statistics, with critical theory, with writing. There is no reason to treat AI differently.
So rather than complaining in my writing (an exercise I enjoy), I am adding something alongside it. Starting this term, I am offering a free weekly work session for graduate students. Not a lecture series. Work time, with structure. Students bring their own projects, and each session anchors on a cluster of questions with substantial time to try things and see what breaks.
My initial plan covers nine clusters:
Authorship and agency. COPE standards, journal policies, what disclosure actually requires, and where the field has not settled.
Privacy and data protection. What you can put in a model, what you cannot, and how IRB commitments constrain your workflow.
AI in the literature review process. Where it accelerates search and synthesis, and where it steers you wrong.
Building a researcher identity with your AI. Setting up context so the tool works from your frameworks and your voice rather than a generic average.
Qualitative analysis. Promises and pitfalls, coding assistance, and the risk of losing contact with your data.
Quantitative analysis. Code generation, assumption checking, and verification you still have to do yourself.
Designing research instruments. Item generation, revision, and validity questions that do not go away but may look different.
AI as a teaching assistant. For their teaching, and for their own learning.
Friction and learning. Information processing theory, and why removing effort from a task sometimes removes the learning with it.
That last cluster matters most to me, and I plan to keep returning to it. Ease is not the goal. Some cognitive work has to happen in the student’s head, or it does not happen at all, and knowing which work that is separates skilled use from outsourcing.
I expect the list to change once students tell me what they actually need. That is part of the point. The clusters are a starting structure.
The argument is simple. Our students will practice in a field that has already changed. We can hand them the skill and judgment to work in it, or we can leave them to assemble it alone, after they graduate, with the stakes considerably higher. Those are the two options. There is no version where the question goes away.
P.S. Sarah often says that she hates this timeline. I often agree but until we can join the Time Variance Authority- this is pretty much it.
