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Three Approaches, One Goal: AI Policy at Berkeley, Texas, and Boston College
Within a few weeks, Berkeley adopted a rule restricting student AI use, the dean of the University of Texas sent his faculty a letter, and Boston College Law named its first director for AI. The three read as a disagreement, but they serve the same goal the ABA already sets for every law school: graduates who can do legal work and exercise judgment, competently and ethically. What divides them is the instrument and the sequence, when and how to build legal judgment and AI fluency into the same lawyer. Berkeley’s ban is the broadest, and because much of it cannot be detected or enforced, it would work better as a statement of values than as a policy.
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Answer Quality Is Not Learning Impact: The Stanford AI-Tutoring Study and Hybrid Legal Education
Sixteen law professors judged nearly 3,000 blind comparisons between AI-generated and human-written answers to contracts questions, and preferred the AI 75 percent of the time. The finding is hard to dismiss and easy to overread. Hybrid JD programs that deliver most coursework asynchronously might see the study as a case for adopting AI tutors as the primary support mechanism for their distance components. The case is strong as far as it goes. The study measured whether professors preferred the AI’s answers, not whether those answers produce the learning outcomes that legal education is supposed to serve.
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Revised Standard 314: Learning-Outcomes Requirements and the August Deadline
The ABA’s revised accreditation standards require law schools to establish measurable learning outcomes for every course, align them to programmatic outcomes, and build formative assessments into the first year, all by the 2026-2027 academic year. Most schools are not staffed for this work. An LLM can help with the drafting. It cannot help with the judgment calls that make the drafting worthwhile.
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