2026-06-01 スタンフォード大学
＜関連情報＞
- https://news.stanford.edu/stories/2026/06/ai-law-legal-professors-study
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6849678
法学教授は同僚の回答よりもAIによる回答を好む Law Professors Prefer AI Over Peer Answers
Alejandro Salinas,Carly Frieders,Neel Guha,Sibo Ma,Ralph Anzivino,Ian Ayres,Oren Bar-Gill,Omri Ben-Shahar,Stephen E. Friedman,George S. Geis,Sue S. Guan,Christoph Henkel,Stephanie R. Hoffer,Gregory Klass,Larasz Moody,Sarath Sanga,Keith Sharfman,Justin Simard,Rebecca Stone,David A. Wishnick,Julian Nyarko
Social Science Research Network Posted: 2 Jun 2026
Abstract
Large language models (LLMs) are increasingly promoted as educational tutors, yet most evaluations focus on domains with a single ground truth. Many disciplines, however, hinge on judgment: reasoning, weighing ambiguity, and reaching defensible conclusions. Law provides a sharp test. We conducted a blinded evaluation of short-answer tutoring in contracts courses with sixteen U.S. law professors. Participants created 40 representative questions, wrote answers, and judged 2,918 anonymized comparisons between human and LLM responses. Professors rated LLMs far higher than their peers (average win rate = 75.33%), with models performing similarly to the best instructor. LLM responses were also rarely flagged as harmful (3.53% vs 12.06% for professors). Preferences for LLM answers were consistent across evaluators and reflected shared professional standards. Our evaluation can be reliably extended to additional models by employing a separate LLM as a judge, rendering expert agreement an effective, scalable method to evaluate AI tutors in judgment-rich domains.
