Dan Rodriguez (Northwestern Law, where he is the former Dean; and a former president of AALS) has a new essay up at Substack, titled “Law Schools’ Attention Spans Remain Short.” An excerpt follows, and (below the fold) a brief comment from me. He begins:
“NYTimes Elizabeth Olson, who has been writing about law schools for many years, offers some short thoughts about how law schools might become more scrupulously focused on two rather large external threats on their basic business model. Helpfully, she reminds us of the looming student loan caps, caps which simply cannot be aligned with present rates of law school tuition (in the aggregate, and at most private and many public law schools). As well, she notes the uncertain, but potentially disruptive, impact of AI on law firm employment. These are not new insights, and yet these reminders appearing in the paper of record is telling, as we think about New Year resolutions and the way forward.
What remains striking is the seeming lack of serious, constructive attention by law schools on these issues.”
He builds, in part, on Derek Muller’s recent “Legal education news of the week (1/26),” particularly with respect to coverage of the student loan cap.
A comment from me:
“What should law schools do about Generative AI” is as difficult and complex a programmatic and administrative problem as law schools have faced, collectively and individually, in at least the 40+ years since I started law school myself. “AI”‘s impact on the profession as a whole is both wildly speculative (what will the long-term structural effects be, if any?) and simultaneously immediate and material. Talk to essentially any practicing lawyer or judge today, not to mention folks who are running legaltech companies. Talk to current law students, both about how they are using ChatGPT and more as part of their education and also about how they are experiencing it in part-time jobs, internships, and externships. Lots of students are using these technologies, with enthusiasm; lots of students are deeply worried and skeptical, and more.
My anecdotal takeaway is that I am far from alone either in the observation of AI’s relevance or in detecting a broad range of reactions even within the walls of law schools. There is a lot of conversation about AI both within and, occasionally, across law schools. Take account of law schools outside the US, and the conversation gets a bit richer and broader, in my experience.
Are law school conversations about AI “coherent,” “evidence-based,” and “collective”? That is more difficult to detect. Some law schools are striding forward with strategic and programmatic purpose. To take one example, look at Vanderbilt. Here, I’ve built a list of the many so-called “law labs” in law schools around the world, where a lot of AI-themed curricular and experiential activity is happening. Other law schools are essentially paralyzed, by combinations of their innate caution, some legacy technophobia, a lack of resources to invest in new faculty or programs, and the perception that prioritizing technology-themed education would detract from a mission to implement university priorities that aim elsewhere, such as bar exam pass rates. If schools get beyond those barriers, what would relevant “evidence” look like? The project of gathering evidence about AI uses, values, and drawbacks across the university curriculum is only now just getting under way.
Turning the theme around, what would a “coherent,” “evidence-based,” “collective” conversation look like? Who would host it? Where and when and how would it take place? AALS is an obvious candidate, but as Dan Rodriguez notes, so far AALS has not pushed into this space. Maybe the conversation is ongoing and informal, or maybe it carries on in a multi-level way rather than through conferences and law reviews. Technology-forward deans talk to one another. Law lab directors and faculty talk to one another, spanning law school boundaries. Barriers persist. Conversations can get siloed within curricular mandates (“what should Contracts cover?”), accreditation rules (students should be prepared to take the bar exam), customs around assessment methods, and teaching responsibilities for “practical” skills that default to law clinics, or law libraries, or instructional and student support), among other things. But organizational innovation and personal persistence sometimes help to break those barriers down.
Speaking for myself, as someone whose teaching, research, and general interests lean heavily in this direction, I can say – one anecdote only – that I have far more coherent, “evidence-based,” and collective conversations about AI with colleagues in other schools and departments across my university (and with them, I have a lot!) than I ever have within my law school.
Maybe, in other words, looking to law schools themselves to identify and solve relevant AI-ish problems is … the wrong move. Universities and colleges today are being criticized heavily for being too uncritically enthusiastic about AI deployment. But faculty and students elsewhere on campus are often excellent allies in thinking systematically and critically about futures with and without AI.
