AI, legal tech and legal education expert Sean Harrington discusses the complex challenges of new lawyers now learning powerful systems on the job, often without clear institutional guidance, shared professional norms, or confidence in their own ability to supervise the output.
The majority of law firm employees are using AI with virtually no guidance or guardrails. How does your law firm compare? Do you have an AI policy in place, and have you educated your staff about appropriate AI usage? Nicole Black explains drafting AI governance isn’t as difficult as it might seem, and there’s no better time than now to get started.
Over the past several days Rebecca Fordon has been digging into the Claude for Word add-in, and the headline finding surprised her. On document-intensive legal work — cite-checking, consistency review, Table of Authorities assembly — it seems to need less supervision than either Claude on the web or Claude Code. Four tests bear that out, with limits worth knowing.
Michael Berman addresses benchmarks used for AI legal research platforms in the context of the risk of hallucinations in retrieval-augmented generation (RAG) AI outputs. As Berman states, verification, of course, is not only good advice, but also an ethical mandate.
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.
Jennifer Ellis documents the step-by-step process and prompts she used to create an effective ChatGPT PowerPoint presentation on cybersecurity. This template is applicable to multiple subject matters. Ellis allows highlights relevant sources and tools for attorneys to use to create AI generated PowerPoints.
Knowing the difference between a general AI tool and one trained on specific sources can mean the difference between getting an accurate answer and becoming quickly frustrated with outcomes that either don’t answer the question thoroughly or answer the question in a confused mixture of fact and fiction. While not always clear, the data that lies behind the GenAI tool is just as important to consider as the user interface or the cost. Without trustworthy or relevant underlying information, the resulting AI-generated output will be less helpful or less trusted and result in inefficiencies as lawyers and staff work to fill the gaps in the GenAI’s response. Considering these factors, Kristopher Turner’s article identifies how and why a retrieval augmented generation (RAG) can give focused and highly specific answers related to one area that someone needs to quickly understand.
What is the irreducibly human element in legal education when AI can pass the bar exam, generate effective lectures, and provide personalized learning and academic support? This article by law professor Gregory M. Duhl confronts that question head-on by documenting the planning and design of a comprehensive transformation of a required doctrinal law school course—first-year Contracts— with AI fully embedded throughout the course design. Instead of adding AI exercises to conventional pedagogy or creating a stand-alone AI course, this approach reimagines legal education for the AI era by integrating AI as a learning enhancer rather than a threat to be managed. The transformation serves Mitchell Hamline School of Law’s access-driven mission: AI helps create equity for diverse learners, prepares practice-ready professionals for legal practice transformed by AI, and shifts the institutional narrative from policing technology use to leveraging it pedagogically.
Stephen Embry and Melissa Rogozinski challenge the assumption fueling the explosion of AI use in legal is that it will save gobs of time. These savings will inure to the benefit of lawyers and clients, will lead to fairer methods of billing like alternative fee structures, will get better results, improve access to justice, and lead to ‘world peace’. Well, maybe even the vendors would not go so far as to guarantee the last one. But vendors do seem to be guaranteeing everything but that. And pundits talk as if AI will transform legal from the ground up. Law firms are buying into the hype, investing in expensive systems that do things they barely understand. See also Part I of their article here.
In Part 2 of her series on how Generative AI (GAI) has changed the dynamics of legal research, Tanya Thomas highlights how research used to encompass finding sources, evaluating them, synthesizing insights across multiple authorities, and reaching conclusions based on that synthesis. Now however, it means asking questions and accepting answers. Students have become consumers of information rather than investigators of it. They don’t develop the iterative thinking that characterizes skilled research—trying a search, evaluating results, refining the query, following unexpected leads, discovering connections, recognizing gaps, circling back to fill them. They simply ask and receive.