This article by Hannah Rosborough, winner of the 2026 Schulich School of Law Teaching Excellence Award, provides an overview of some visual aids for teaching legal research that she has developed over the past few years. Rosborough shares these based on positive student feedback and with the hope that others might find them useful in their own teaching or training.
Over the past three years, researchers have published dozens of studies examining exactly when and why AI fails at legal tasks—and the patterns are becoming clearer. The research is clear: AI hallucinations in legal work are real, measurable, and follow predictable patterns. Rebecca Fordon evaluates the data and research that documents six critical patterns lawyers must understand to make sound, actionable and effective decisions about using AI.
Jennifer Chapman concisely conveys the importance of identifying for patrons that AI’s confidence doesn’t equal competence. Chapman states that as law librarians we are naturally skeptical of certainty. The law teaches us to question everything, and library school teaches us how to verify everything. We, not generative AI, are the trusted human assistants that need to help our patrons effectively use technology tools.
Tanya Thomas, Research and Instructional Technology Librarian, raises the argument that we are training a generation of lawyers who rarely engage with the raw materials of their profession, and are increasingly consuming only the processed, pre-digested, AI-synthesized versions. Students are suffering from what we might call source blindness, the inability to distinguish between fundamentally different types of sources, compounded by source erasure, where sources disappear behind AI-generated summaries.
Deadlines are everything in patent law. A missed deadline can result in abandoned patent applications, loss of rights, and costly malpractice claims. Accordingly, deadline management is one of the most important functions of patent docketing. Traditional docketing systems rely heavily on manual data entry, introducing opportunities for human error. The use of artificial intelligence (AI) agents (“Agents”) offers a practical solution to reduce these risks. Agents can extract deadlines from United States Patent and Trademark Office (USPTO) communications, populate docketing systems, and even provide attorneys with regular updates on upcoming tasks. Agents create a highly reliable docketing system that reduces clerical mistakes and malpractice exposure and may ultimately lower malpractice insurance premiums over time when combined with human oversight. This paper by John Schulte outlines the potential benefits of using AI agents in docketing workflows and proposes an implementation roadmap, including three key safeguards for law firms to consider.
This spring the Michigan State University College of Law and the MSU Center for Law, Technology & Innovation introduced the “LegalRnD AI Studio,” a groundbreaking mini-course series designed to elevate law students’ AI literacy, focusing on practical skills in generative AI. Dennis Kennedy shares how you can replicate this successful model and provide your students with the essential AI literacy they need at your school.
It is difficult to test Large-Language Models (LLMs) without back-end access to run evaluations. So to test the abilities of these products, librarians can use prompt engineering to figure out how to get desired results (controlling statutes, key cases, drafts of a memo, etc.). Some models are more successful than others at achieving specific results. However, as these models update and change, evaluations of their efficacy can change as well. Law Librarians and tech experts par excellence, Rebecca Fordon, Sean Harrington and Christine Park plan to propose a typology of legal research tasks based on existing computer and information science scholarship and draft corresponding questions using the typology, with rubrics others can use to score the tools they use.
Is better case law data fueling a legal research boom? Recently, Rebecca Fordon noticed a surge of new and innovative legal research tools. Fordon wondered what could be fueling this increase, and set off to find out more.
Hallucinations in generative AI are not a new topic. If you watch the news at all (or read the front page of the New York Times), you’ve heard of the two New York attorneys who used ChatGPT to create fake cases entire cases and then submitted them to the court. After that case, which resulted in a media frenzy and (somewhat mild) court sanctions, many attorneys are wary of using generative AI for legal research. But vendors are working to limit hallucinations and increase trust. And some legal tasks are less affected by hallucinations. Law Librarian and attorney Rebecca Fordon guides us to an understanding of how and why hallucinations occur and how we can effectively evaluate new products and identify lower-risk uses.
The emergence of Large Language Models (LLMs) in legal research signifies a transformative shift. This article by Sean Harrington critically evaluates the advent and fine-tuning of Law-Specific LLMs, such as those offered by Casetext, Westlaw, and Lexis. Unlike generalized models, these specialized LLMs draw from databases enriched with authoritative legal resources, ensuring accuracy and relevance. Harrington highlights the importance of advanced prompting techniques and the innovative utilization of embeddings and vector databases, which enable semantic searching, a critical aspect in retrieving nuanced legal information. Furthermore, the article addresses the ‘Black Box Problem’ and explores remedies for transparency. It also discusses the potential of crowdsourcing secondary materials as a means to democratize legal knowledge. In conclusion, this article emphasizes that Law-Specific LLMs, with proper development and ethical considerations, can revolutionize legal research and practice, while calling for active engagement from the legal community in shaping this emerging technology.