Artificial Intelligence for Learning the Law: Generative AI for Academic Support in Law Schools and Universities

This paper reports research conducted from December 2022 to July 2024, and in particular experiments conducted from May 20 to July 12, 2024, on the use of generative AI in legal education and academic support. This study was a cross-sectional, latitudinal qualitative evaluation of generative AI systems at a certain point in time and at the level of development of each system at that point in time. Although the topic of this study is learning the law, the results and overall approach to using an AI as a personalized learning tutor can be applied to many graduate and undergraduate programs in universities and other levels of education. This paper reports the qualitative and comparative findings of experiments comparing the performance of public-facing general purpose LLMs—Claude 3.5 Sonnet, Copilot, Gemini 1.5 Pro, and GPT-4o Omni—and a law-specific LLM with a curated legal dataset, Lexis+ AI, and it will reveal which systems performed the best as personalized, self-guided, one-on-one law tutors.

The advancements in tutoring represented by generative AI systems have increased the pace of adoption of AI technologies to the point that GAI tools can play a significant role in academic support in law schools and universities. Generative AI tools can help a student learn and understand material better, more deeply, and notably faster than traditional means of reading, rereading, notetaking, and outlining. GAI tools, particularly Intelligent Tutoring Systems (ITS), adaptive learning platforms, and AI-augmented tutoring solutions, have shown promise in enhancing student engagement, improving learning outcomes, and providing tailored academic support. AI can explain, elaborate on, and summarize course material. It can write and administer formative assessments, and, if desired, it can write self-guided summative evaluations and grade them. AI can translate material into and from foreign languages with a fidelity to context, usage, and nuances of meaning not previously seen in machine learning or neural network translation services. AI also can visualize material using the tools of visual generative AI that literally paint pictures of the subjects and situations in the material that can overcome students’ literacy issues both in the native language of the communication and in the students’ own native languages.