Artificial Intelligence and Legal Analysis: Implications for Legal Education and the Profession

TL;DR

This study evaluates legal and non-legal large language models' ability to perform legal reasoning tasks, revealing their limitations and implications for legal education and practice.

Contribution

It provides a comparative analysis of legal and non-legal LLMs' reasoning capabilities and discusses their impact on legal education and professional skills.

Findings

LLMs can perform basic IRAC analysis but with limitations

LLMs exhibit false confidence and hallucinations in responses

Legal and non-legal LLMs show similar shortcomings

Abstract

This article reports the results of a study examining the ability of legal and non-legal Large Language Models to perform legal analysis using the Issue-Rule-Application-Conclusion framework. LLMs were tested on legal reasoning tasks involving rule analysis and analogical reasoning. The results show that LLMs can conduct basic IRAC analysis, but are limited by brief responses lacking detail, an inability to commit to answers, false confidence, and hallucinations. The study compares legal and nonlegal LLMs, identifies shortcomings, and explores traits that may hinder their ability to think like a lawyer. It also discusses the implications for legal education and practice, highlighting the need for critical thinking skills in future lawyers and the potential pitfalls of overreliance on artificial intelligence AI resulting in a loss of logic, reasoning, and critical thinking skills.