The Great Divide

What it might look like for law faculty to learn alongside the students we are training

When my youngest son saw the title of this post scribbled on the whiteboard on my desk, he got excited. He thought I was writing about the latest album by Noah Kahan, who some of you might have caught on Saturday Night Live a few weeks back. (If not, you should.)

I told him no, this was another post about AI. He shrugged and said, “boring,” in the most sarcastic tone ever.

But I have been thinking about the great divide for a few weeks, ever since Bridgette and I led a boot camp on AI for law faculty. We had a slide deck ready to zoom through so we could get to the fun part, playing with the tools. My biggest fear was that our material would be too basic for this crowd.

We got started and mentioned NotebookLM. A colleague raised his hand. “What is it, and how do I log in?”

We spent the next ten minutes walking the room through how to log in and what NotebookLM can do. We showed them how to generate a podcast from a stack of PDFs. We showed them how it could draft a slide deck. I told them it could verify sources and put citations in Bluebook format. Some were blown away.

NotebookLM has been publicly available since 2023.

The moment was disorienting because a few days earlier, a neighbor of mine, a lawyer who runs the in-house tech team at a big company, had been telling me about the agentic workflows he was building to automate legal tasks. He was talking about chaining models together. He was talking about OpenClaw. He was telling me his company was cutting its reliance on outside law firms because of what AI could now do. He was ten steps ahead of me, and I am someone who is trying hard to keep up.

And now here I was, in a room full of brilliant, accomplished, thoughtful legal scholars, and the first question was how to log in to a tool that has been out for three years.

The Older Divide

The divide I am describing is not new. It is just taking a new form.

Legal academia and legal practice have lived in different worlds for a long time. Practitioners have always rolled their eyes at the abstractions of law professors (insert ivory tower jokes). Professors have always wondered why practitioners do not read more carefully or think more deeply.

It is not that law professors are uninterested in the real world. Many of my colleagues care deeply about legal practice. They write briefs. They consult on cases. They run clinics. The issue is that the academy was never built to reward keeping up with the tools of practice. It was built to reward scholarship, and scholarship rewards a different kind of depth, years spent on a single argument and not weeks spent learning a platform that might be obsolete by the time the article comes out.

So when a new tool emerges, the academy is structurally late.

AI is exposing this pattern in a way earlier shifts never did, because AI is not just a new tool. It is changing what it means to read, to write, to research, and to think. Those are precisely the things law school is supposed to teach.

What Our Students Actually Need

Our students are walking into a profession that is changing under their feet. Some will graduate into firms that have already inserted AI into nearly every workflow. Others will graduate into legal aid offices, public defender offices, and small practices where the question of whether to use these tools will fall to them, often without guidance, without support, and without anyone in the building who knows more than they do.

So what do they need from law faculty right now?

They do not need us to be AI evangelists. They do not need a list of approved tools. They do not need an AI policy. They do not need us to pretend we know more than we do.

But they need us to do the work ourselves. They need us to have sat with these tools long enough to feel their power and their limits. They need us to be able to say, with some authority, here is where this helps you think more clearly, and here is where it lets you off the hook in ways that will hurt you later. They need us to have wrestled with the question of when the friction in legal work is the point and when it is just friction we can finally remove.

We cannot teach that judgment if we have not formed it ourselves. We cannot help students think critically about a technology we have not touched.

A Different Kind of Faculty Development

This moment is asking law schools to reimagine what faculty development means. It has never really included continuous learning about the tools reshaping the field we are training students to enter. There has been no expectation of it, so there has been no infrastructure for it.

That has to change, and the change cannot be cosmetic. It cannot be a one-hour lunch session once a semester. It needs to be sustained, supported, and treated as part of what it means to be a legal educator now.

What might that look like in practice? It starts with curiosity, not certainty. We should be running mini-experiments in our own classrooms, the way Dan Schwartz at the University of Minnesota Law School is doing, studying how AI actually shapes student learning rather than guessing at it.

We should be learning from our students, not just teaching them. A colleague recently told me that the top ten students in her 1L class all said they were using AI to tutor themselves through the material. We should be asking those students what they did, how they did it, and what they learned about their own thinking in the process. They are running the experiments we should be running. And when we get answers, we should be sharing it with the entire student community.

We should also be looking hard at our own workflows and asking where AI belongs in them. In my Child Welfare Appellate Clinic, I am building a tool that will mine our past briefs to surface similar arguments and suggest relevant cases for students working on new appeals. I want to walk through our entire briefing workflow with my students and figure out together where AI helps, where it gets in the way, and where it changes what the work itself should be. That kind of inquiry, done in the open, with students as collaborators, is the faculty development we need.

It also needs to come with a deeper conversation about what we owe our students. Too often, we gravitate to simply banning AI use in different settings. What we need are real conversations about what we are preparing students to do, what habits of mind we are trying to cultivate, and what it means to graduate someone into a profession we ourselves do not fully recognize anymore.

The Bridge

The divide between the legal innovator in my neighborhood and my colleagues on the faculty will not close on its own.

It will close only if the people inside legal education decide that closing it is part of the work. And that decision cannot be made one faculty member at a time. It must be made by institutions, and by the people within them who set what counts and what does not.

Here is what I know. Our students are not going to wait for us to catch up. They are already using these tools. They are already making judgments about when to trust them and when not to, often without any guidance from the people they pay tuition to learn from. Every semester we spend treating AI as someone else’s problem is a semester we have chosen not to teach them something they might need.