Andrew Blair-Stanek (U Maryland Francis King Carey Law) et al. have posted “Is AI’s Law School Exam Performance Plateauing?” on SSRN. Here is the abstract:

Last spring, we had OpenAI’s reasoning model o3 take our final exams, with the reasoning effort parameter set to “high,” and graded its answers on the same curve as our students. o3 got grades ranging from A+ to B. This spring, we repeated the experiment, using OpenAI’s latest reasoning model, GPT-5.5, with the reasoning effort at the new “xhigh” setting. GPT-5.5 got two A+s, three As, two As , and a B+, a good performance but far short of superhuman. Depending on the metric, GPT-5.5 may have actually performed worse than o3 did last year, despite the new “xhigh” setting. These results may fit the broader pattern of frontier AI models’ performance plateauing on other legal benchmarks.

Bj Ard (U Wisconsin Law) and Rebecca Crootof (U Richmond Law) have posted “Technology Law Chapter 6: Upset Equilibria” on SSRN. Here is the abstract:

Based on years of experience teaching the subject, we have produced a first draft of a “Technology Law” coursebook. It teases out fundamental concepts, introduces our methodology for resolving tech-fostered legal uncertainties, and identifies the strengths and weaknesses of different regulatory choices. Through a mixture of readings, exercises, and discussion questions, it helps readers develop facility in:

– Recognizing the recurring techlaw and policy questions and discerning the application, normative, and institutional uncertainties associated with a particular technology;

– Working through the process of resolving legal uncertainties, which includes consciously selecting a regulatory approach, identifying legally salient characteristics and relevant analogies, and weighing the benefits and drawbacks of various regulatory choices (law-by-analogy, creating new law, or reconfiguring legal institutions); and

– Developing familiarity with employing and countering common rhetorical strategies for advancing, opposing, or shaping regulation.

This course is designed to be accessible and useful to all students, regardless of career interests or prior experience with technology. New technologies challenge every area of the law, and the regulatory and rhetorical strategies we’ll explore are transferable across subjects.

This posting includes Chapter Six: Upset Equilibria. Future chapters will be posted bi-monthly.

We welcome feedback at the link included in the document; additional chapters will be updated regularly. If you are interested in teaching from this text, in whole or in part, please let us know, as we would be happy to share our class notes and slides.

Bj Ard (U Wisconsin Law) and Rebecca Crootof (U Richmond Law) have posted “Technology Law Chapter 6: Upset Equilibria” on SSRN. Here is the abstract:

Based on years of experience teaching the subject, we have produced a first draft of a “Technology Law” coursebook. It teases out fundamental concepts, introduces our methodology for resolving tech-fostered legal uncertainties, and identifies the strengths and weaknesses of different regulatory choices. Through a mixture of readings, exercises, and discussion questions, it helps readers develop facility in:

– Recognizing the recurring techlaw and policy questions and discerning the application, normative, and institutional uncertainties associated with a particular technology;

– Working through the process of resolving legal uncertainties, which includes consciously selecting a regulatory approach, identifying legally salient characteristics and relevant analogies, and weighing the benefits and drawbacks of various regulatory choices (law-by-analogy, creating new law, or reconfiguring legal institutions); and

– Developing familiarity with employing and countering common rhetorical strategies for advancing, opposing, or shaping regulation.

This course is designed to be accessible and useful to all students, regardless of career interests or prior experience with technology. New technologies challenge every area of the law, and the regulatory and rhetorical strategies we’ll explore are transferable across subjects.

This posting includes Chapter Six: Upset Equilibria. Future chapters will be posted bi-monthly.

We welcome feedback at the link included in the document; additional chapters will be updated regularly. If you are interested in teaching from this text, in whole or in part, please let us know, as we would be happy to share our class notes and slides.

Bonnie J. Shucha (U Wisconsin Law) has posted “Closing the AI Readiness Gap: A Framework for Law Schools” (32 (2) Perspectives: Teaching Legal Research and Writing (forthcoming 2026)) on SSRN. Here is the abstract:

Gen AI literacy for new associates is expected, not optional, yet most law schools have not caught up, and students are arriving in practice without the foundational gen AI literacy skills to use these tools safely and effectively. Closing that gap need not mean a new curriculum or a major investment; it can be done by coordinating what a school already has, so that every student graduates prepared. This article offers a framework built on three co-equal questions: what students learn, when and where they learn it, and who coordinates the learning.

Bonnie J. Shucha (U Wisconsin Law) has posted “Getting Started with GenAI in Legal Practice” (97 Wis. Law. 29 (2024)) on SSRN. Here is the abstract:

This article offers advice for approaching generative artificial intelligence (GenAI) in legal practice, examines types of GenAI tools and key policy considerations, and provides a step-by-step approach to building competence.

Margaret Raymond (U Wisconsin Law) has posted “Our AI, Ourselves: Illuminating the Human Fears Animating Early Regulatory Responses to the Use of Generative AI in the Practice of Law” (15 St. Mary’s Journal on Legal Malpractice & Ethics 221 (2025)) on SSRN. Here is the abstract:

Generative artificial intelligence is changing the way lawyers work, and with those changes have come questions and concerns about how it should be regulated. Those questions and concerns, particularly on the individual level, are driven by fears about the implications of the use of generative AI. This Article identifies and explores the fears that drive these regulatory responses: fear of exposing judicial fallibility, anxiety over AI replacing human lawyers, and concerns about missing out on AI’s potential benefits. Ultimately, effective regulation of the use of generative AI in legal practice needs to be attentive to the fears and hopes surrounding generative AI in law. Only by understanding the very human anxieties regarding generative AI can the profession craft effective regulatory models that address the integration of AI in legal practice.

Nicholas Bednar (U Minn Law), David R. Cleveland (same), Allan Erbsen (same), and Daniel Schwarcz (same) have posted “Artificial Intelligence and Human Legal Reasoning” on SSRN. Here is the abstract:

Empirical evidence increasingly demonstrates that generative artificial intelligence has the capacity to improve the speed and quality of legal work, yet many lawyers, judges, and clients are reluctant to fully embrace AI. One important reason for hesitation is the concern that AI may undermine the human reasoning and judgment on which competent legal practice depends. This Article provides the first empirical evidence evaluating that concern by testing whether upper level law students who rely on AI at an early stage of a project experience reduced comprehension and impaired legal reasoning on later stages when AI is not an available option.

To evaluate the possibility that AI degrades comprehension and reasoning, we conducted a randomized controlled trial involving approximately one hundred second and third year law students at the University of Minnesota Law School. Participants completed four sequential lawyering tasks: writing a memo synthesizing the law based on a packet of legal materials, answering closed-book multiple choice questions that tested their comprehension of the materials, writing a memo applying the materials to a fact pattern, and revising their second memo. Participants were randomly assigned either to a control group, which could not use AI until the final revision task, or to an AI-exposed group, which used AI during both the initial synthesis task and the final revision task, but not during the intervening comprehension and application tasks.

The results provide a more complex picture of AI’s effects on legal reasoning than critics or enthusiasts often assume. As expected, participants who used AI to help craft synthesis memos produced substantially stronger work and completed that task more quickly. But contrary to our preregistered hypothesis, AI exposure at this initial stage did not diminish downstream comprehension of the underlying legal principles. To the contrary, participants who used AI on the synthesis task outperformed the control group on the later application task even when neither group had access to AI. Yet when all participants used AI to revise their reasoning memos, participants who started with weaker memos improved while participants who started with stronger memos regressed. These findings suggest that AI does not inevitably erode or promote independent legal reasoning, but that its effects depend on when and how law students and junior lawyers use AI. The Article builds on this insight by suggesting best practices for AI use and avenues for further empirical research.

Thamburaj Anthuvan (S.B.Patil Institute Management) et al. have posted “Human-AI Collaboration in Academic Writing: A Narrative Review and the Scholarly HI-AI Loop Framework for Ethical Knowledge Production” on SSRN. Here is the abstract:

This narrative literature review explores the evolving intersection of human and machine collaboration in academic writing, with a focus on literature summarization as a critical site of transformation. Synthesizing findings from 38 peer-reviewed studies published between 2020 and 2025, it examines the emergence of hybrid workflows where machine-generated drafts are refined, contextualized, and ethically validated by human scholars. The review identifies four core themes-tool capabilities, editorial oversight, ethical disclosure, and institutional readiness-that shape current practices and highlight unresolved tensions around authorship, transparency, and scholarly responsibility. Building on this synthesis, the paper introduces the Scholarly HI-AI Loop, a seven-stage framework that reimagines literature review as a co-productive and ethically accountable process. Unlike tool-centric audits, this framework offers a normative roadmap for integrating automation without compromising academic integrity. It positions human scholars not as passive reviewers, but as epistemic anchors who shape meaning, ensure accuracy, and safeguard ethical standards. The review offers actionable guidance for researchers, editors, institutions, and developers seeking to navigate this transition responsibly. By grounding its insights in both empirical patterns and conceptual analysis, the paper contributes to a growing conversation on how academic knowledge production can adapt-without eroding-its foundational values in the age of machine assistance.

Ricardo Fitas (Technical U Darmstadt) et al. have posted “Leveraging AI in Education: Benefits, Responsibilities, and Trends” on SSRN. Here is the abstract:

This chapter presents a review of the role of Artificial Intelligence (AI) in enhancing education outcomes for both students and teachers. This review includes the most recent papers discussing the impact of AI tools, including ChatGPT and other technologies, in the educational landscape. It explores the benefits of AI integration, such as personalized learning and increased efficiency, highlighting how these technologies contribute to the learning experiences of individual student needs and administrative processes to enhance educational delivery. Adaptive learning systems and intelligent tutoring systems are also reviewed. Nevertheless, it is known that important responsibilities and ethical considerations intrinsic to the deployment of AI technologies must be included in such an integration. Therefore, a critical analysis of AI’s ethical considerations and potential misuse in education is also carried out in the present chapter. By presenting real-world case studies of successful AI integration, the chapter offers evidence of AI’s potential to positively transform educational outcomes while cautioning against adoption without addressing these ethical considerations. Furthermore, this chapter’s novelty relates to exploring emerging trends and predictions in the fields of AI and education. This study shows that, based on the success cases, it is possible to benefit from the positive impacts of AI while implementing protection against detrimental outcomes for the users. The chapter is significantly relevant, as it provides the stakeholders, users, and policymakers with a deeper understanding of the role of AI in contemporary education as a technology that aligns with educational values and the needs of society.

Gregory M. Duhl (Mitchell Hamline School of Law) has posted “All In: Embedding AI in the Law School Classroom” on SSRN. Here is the abstract:

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 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.

This Article details the roadmap I have followed for AI integration in a course that I am teaching in Spring 2026. It documents the beginning of my experience with throwing out the traditional legal education playbook and rethinking how I approach teaching using AI pedagogy within a profession in flux. Part I establishes the pedagogical rationale grounded in learning science and institutional mission. Part II describes the implementation strategy, including partnerships with instructional designers, faculty innovators, and legal technology companies. Part III details a course-wide series of specific exercises that develop AI literacy alongside doctrinal and skill mastery. Part IV addresses legitimate objections about bar preparation, analytical skills, academic integrity, and scalability beyond transactional courses. The Article concludes with a commitment to transparent empirical research through a pilot study launching in Spring 2026, acknowledging both the promise and the uncertainty of this pedagogical innovation. For legal educators grappling with AI’s rapid transformation of both education and practice, this Article offers a mission-driven, evidence-informed, yet still preliminary template for intentional change—and an invitation to experiment, adapt, and share results.