Legal Education: Teaching and Learning Law

A non-legal tool, but the most ubiquitous generative AI tool available. OpenAI's early model ChatGPT-3.5 is still open access.

An open access genAI created by the Conflict Analytics Lab at Queen's. Free to use with an account.

A chatbot allowing users to ask questions about a specific statute.

A chatbot that allows users to ask questions about a specific case.

A selection of open access AI prediction tools on legal issues like worker classification, termination, and layoffs.

Brief Analysis allows you to upload a document and analyze the citations within it, suggesting additional case law for inclusion in the document.

A suite of tax and employment tools which use AI to predict the outcome of a tax or employment law scenario by analyzing previous cases with similar factors. Contact the Library to coordinate access.

This randomized controlled trial studies the effect of AI assistance on human legal analysis. The results have descriptive and normative implications for the future of lawyering. Descriptively, they suggest that AI assistance can significantly improve productivity and satisfaction, and that it can be selectively employed by lawyers in areas where AI is most useful. Because AI tools have an equalizing effect on performance, they may also promote equality in a famously unequal profession. Normatively, our findings suggest that law schools, lawyers, judges, and clients should thoughtfully embrace AI tools and plan for a future in which they will become widespread.

This article, based upon a keynote address by Professor Harry Surden, provides an in-depth overview of the recent advancements in artificial intelligence (AI) since 2022, particularly the rise of highly capable large language models (LLMs) such as OpenAI’s GPT-4, and their implications for the field of law. The talk begins with a historical perspective, tracing the evolution of AI from early symbolic systems to the modern deep learning era, highlighting the breakthroughs that have enabled AI to process and generate human language with unprecedented sophistication.

With the rise of large language models capable of passing law school exams and the Uniform Bar Exam, how should legal educators prepare their students for an age of transformative technological change? As text-generating AI is being integrated in legal research platforms and word processing software, which automate the drafting of legal documents based on human prompts, lawyers are increasingly adopting this technology as a standard tool of legal research and writing. This Article explores the implications of these developments for legal education, focusing on pedagogy, curriculum, and assessment.

This article explores the emergence of ChatGPT and considers the implications for legal education and practice. It will examine how law schools can develop strategies for assessments to make them more challenging for generative AI while educating students on its potential in the workplace. All legal educators are now on a journey to navigate the complexities of open-source AI technology and comprehend its implications.

This essay begins with a description and assessment of the legal tech bro archetype, then problematizes the legal tech bro’s approach to the law by examining the concepts of “the legal singularity” and legal indeterminacy. Next, this essay considers what aspects of lawyering should be automated by exploring the role experience plays in legal research and how it forms the basis of legal creativity. With this understanding in mind, this essay proposes a new normative model of the legal research and writing process that allows lawyers to responsibly augment their law practice with LLM-based technologies. Finally, this essay concludes by discussing the curricular implications of this model.

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.