This article synthesizes the views of legal scholars examining how generative artificial intelligence (AI) is affecting legal and dispute resolution practice, education, and scholarship. They share a perspective of responsible realism – recognizing both the promise and the perils of AI. It is already reshaping how lawyers, neutrals, educators, students, and scholars work – and its influence will only grow.
The scholars identify clear benefits: broader access, greater efficiency, and new support for professional learning. They also warn of serious risks, including bias, deskilling, and erosion of judgment. Avoiding both hype and panic, they analyze developments, offer realistic strategies, and propose policies to promote responsible use and curb misuse.
This article distills their main insights into a concise and usable framework. It highlights shared themes, contrasting emphases, and practical takeaways for lawyers, neutrals, educators, students, and institutions.
Responsible realism about generative artificial intelligence (AI) is the view that professionals and institutions should use it when it adds genuine value, while scrupulously preserving key values including competence, confidentiality, and independent judgment. So they should neither shun it nor embrace it reflexively.
Discussions about AI often swing between extremes – grand predictions of total transformation or dire warnings that machines will replace humans, including lawyers and educators.
By contrast, some legal scholars adopt a posture of responsible realism. This article highlights and synthesizes the perspectives of such scholars, as reflected in the following articles.
John Bliss, Teaching Law in the Age of Generative AI.
Kevin T. Frazier & Alan Z. Rozenshtein, Large Language Scholarship: Generative AI in the Legal Academy.
Nachman N. Gutowski, Disclosing the Machine: Trends, Policies, and Considerations of Artificial Intelligence Use in Law Review Authorship.
Nachman N. Gutowski & Jeremy W. Hurley, Forging Ahead or Proceeding with Caution; Developing Policy for Generative Artificial Intelligence in Legal Education.
John Lande, When AI Comes to the Table: How Tech Tools Will Change ADR.
John Lande, How I Learned to Stop Worrying and Love the Bot: What I Learned About AI and What You Can Too.
Andrew M. Perlman, The Legal Ethics of Generative AI.
Joseph Regalia, From Briefs to Bytes: How Generative AI is Transforming Legal Writing and Practice.
Many other articles could have been included, but this is intended as a concise summary, not a comprehensive analysis of a burgeoning literature.[1]
The reviewed articles show that AI is already reshaping legal and dispute resolution practice, education, and scholarship – and that its influence is likely to grow. The scholars acknowledge real benefits and confront serious risks. They emphasize that AI will inevitably shape how practitioners, educators, and students work, think, and interact. They call for thoughtful, proactive responses from law schools, legal institutions, and the legal profession as a whole.
These articles are clear-eyed about what AI both can and can’t do. They blend technological fluency, institutional knowledge, pedagogical insight, and ethical perspective. They offer practical guidance for practitioners, educators, students, and scholars to use AI effectively without compromising core values.
AI is evolving rapidly, with new tools and capabilities emerging constantly. As a result, any analysis of AI must be tentative. Today’s AI won’t be the AI of next year – or five years from now – when tools will likely be far more sophisticated. These scholars ground their conclusions in current capabilities and trends, tempered by experience, institutional awareness, and caution. While the details will evolve, the core insights are likely to remain relevant as individuals and institutions adapt.
This article weaves together their insights to identify where AI is likely to have enduring effects. It highlights points of agreement, notes differences in emphasis, and offers practical takeaways.
[1] . There are more than 6000 publications in SSRN’s “Artificial Intelligence – Law, Policy, &
Ethics” eJournal and more than 600 publications in SSRN’s “Artificial Intelligence – Role & Applications in Law” eJournal. See the “resources” section of the “Love the Bot” article for a collection of recent publications.
The scholars examine AI’s impact on legal and dispute resolution practice, education, and scholarship. Together, they portray a legal profession and academy already shaped by AI – and likely to undergo even greater transformation.
Legal and Dispute Resolution Practice. AI tools already appear in court systems, law offices, dispute resolution processes, and training programs, including integrated platforms for managing large caseloads.
Some tools assist lawyers and neutrals, while others help parties understand their options and participate more effectively in dispute resolution. Lawyers use AI tools to perform a range of legal tasks including research, document review, drafting, editing, risk assessment, prediction, negotiation, and dispute resolution. AI tools also assist with training, case management, dispute prevention, preparation, and resolution. They can increase efficiency, reduce costs, and promote informed decision-making.
Current ethical rules already accommodate AI use, as reflected in ABA Formal Opinion 512. Lawyers are not only permitted to use these tools – they may soon be required to do so. When they do, they must take responsibility to comply with applicable ethical rules.
Legal Education. Students already use AI tools for class preparation, research, writing, and simulation work. AI is reshaping how some instructors teach and assess students’ performance. Instructors use it to draft syllabi, powerpoint slides, simulations, assignments, and exams. For example, some design AI-integrated assignments to build students’ skills in judgment, reflection, and critique. Others use it to help grade student work or draft feedback. Some remain uncertain about its role – or fear it could undermine student learning.
Legal Scholarship. Scholars use AI tools to generate ideas, synthesize literature, draft and edit text, and check citations. These tools may boost productivity and broaden access to new contributors. AI is likely to reshape how legal scholarship is produced, evaluated, and valued. It may also increase pressure to publish, encourage superficial work, and lead to overreliance on machine-generated content.
Frazier & Rozenshtein argue that law schools are entering an era of “large language scholarship” in which AI will play a central role. They predict that disclosure requirements won’t work because it will be too hard to identify AI’s varied contributions – and that norms will shift to accept AI-assisted scholarship as part of routine scholarly work.
The scholars articulate shared principles grounded in responsible realism. They recognize inevitable change, emphasize the importance of human judgment, highlight the need for AI literacy, and advocate for clear, updated institutional norms.
They stress that good human decision-making – not technological fixes or avoidance – is key to managing risk. When used carefully, AI tools can enhance learning and performance without replacing human judgment. Instead of banning AI or imposing blanket rules, they recommend strategies to strengthen AI skills, reinforce human oversight, and promote appropriate disclosure.
These themes offer a constructive path to help lawyers, neutrals, educators, students, and scholars use AI to support core professional values.
Accepting the Inevitability of AI’s Impact. The scholars observe that AI is already reshaping legal dispute resolution practice, education, and scholarship. Its use is expanding rapidly across law firms, legal tech companies, courts, law schools, and dispute resolution processes. New tools will be developed and adopted regardless of whether individuals and institutions are ready.
Preserving Human Judgment. Using AI well requires new forms of intellectual discipline attuned to its ethical challenges. AI should augment – not replace – human judgment and responsibility. Humans should not just be “in the loop” but in control. AI can promote efficiency, creativity, and clarity, but humans must ultimately be responsible for decisions about how they use AI outputs. Lawyers and other dispute resolution professionals must exercise sound professional judgment. Educators must guide students in using AI responsibly. Scholars must ensure that their work expresses their own ideas.
Developing AI literacy. Responsible use of AI requires developing AI literacy – the ability to use these tools with skill and good judgment. It includes asking effective questions and evaluating whether outputs are accurate and appropriate. Building AI literacy requires training, practice, perseverance, curiosity, and experimentation.
Recognizing Risks. People should understand what AI can and cannot do correctly. Its use comes with significant risks, including factual inaccuracies, hallucinations, bias, cognitive deskilling, overreliance, and erosion of trust. These risks can threaten professional standards and academic integrity. AI literacy includes recognizing and managing these risks appropriately
Promoting Wise Policies. Institutional policies should recognize how people actually use AI, promote professional values, and avoid creating incentives for concealment or misuse. Policies should encourage responsible experimentation. Rigid or unrealistic rules can undermine trust and learning. Disclosure requirements may be appropriate in some settings but ineffective or problematic in others. Institutions should regularly revisit their policies as they gain experience and as AI tools evolve.
The scholars largely share a general perspective but differ in how they frame certain issues and what they emphasize. These differences reflect the varied settings, roles, and audiences that each scholar addresses. Recognizing these differences helps illuminate the complexity of the choices that legal institutions and professionals must make.
Skill Development. The scholars differ about when and how law students should be taught to use AI. The tension lies between training students to develop basic skills and preparing them to use the tools they will need in practice.
Surveying students and faculty, Bliss found varied views on when and how to incorporate AI into legal education. He discussed how faculty might use AI in different types of courses. Gutowski & Hurley expressed concern that premature reliance could undermine learning, and that students may need time to build foundational skills before using AI. They propose phasing in AI instruction with careful attention to sequencing and context.
Disclosure and Transparency. Gutowski & Hurley describe law school policies that emphasize the ethical and pedagogical value of transparency in disclosing AI use. Frazier & Rozenshtein argue that disclosure requirements for scholars are likely to be ineffective, unenforceable, and conceptually flawed. They contend that hybrid humanAI authorship is already widespread and difficult to detect – and that policing it would be impractical and counterproductive.
Institutional Roles and Responsibilities. Some scholars focus on individual judgment and ethics, while others highlight the need for institutional leadership. Perlman emphasizes that under current ethical rules, lawyers have duties to understand and supervise the use of AI tools. Bliss and Gutowski & Hurley call on law schools to develop clear, adaptive policies to guide both faculty and students. Frazier & Rozenshtein suggest that law schools will need to reconsider hiring, evaluation, and scholarly norms as AI changes how legal knowledge is produced.
Tone and Urgency. All the scholars adopt a serious, thoughtful tone. Some write with more urgency or optimism than others. Lande, Perlman, and Regalia emphasize practical strategies and immediate steps. Bliss and Gutowski & Hurley frame their work as exploratory and cautious, encouraging experimentation and reflection. Frazier & Rozenshtein write with a sense of inevitability, suggesting that AI has already altered the conditions of legal scholarship and that scholars must now adapt.
The scholars offer practical recommendations for how practitioners, educators, students, and institutions can respond constructively to AI’s development and use. They agree on the need for intentional, values-based action suited to specific roles and responsibilities. They recommend careful adaptation rather than wholesale adoption or resistance. They urge institutions and individuals to develop policies, practices, and skills that support informed, ethical AI use.
This section summarizes the scholars’ key recommendations. Although not every scholar addressed each issue, these recommendations reflect a shared approach of responsible realism about AI.
Legal and Dispute Resolution Practice. Practitioners should develop basic AI literacy. This includes understanding the capabilities and limits of current tools, staying current with emerging technologies, and evaluating the quality and reliability of AI-generated outputs. They should supervise AI use in their work, including by their staff, and remain personally responsible for their professional judgments.
Lawyers should never substitute AI output for their professional judgment when advising clients. Lawyers may gain insights about risk from predictive analytics, but they remain responsible for client-specific judgments about case strategy, negotiation, mediation, and legal representation.
To comply with ethical duties of confidentiality, practitioners should evaluate whether AI tools adequately protect client information. They should assess confidentiality risks from AI use and, in some cases, obtain client consent.
Legal Education. Some faculty feel unprepared to teach with or about AI and may resist engagement – especially without training or incentives. Law schools should develop flexible policies, recognizing that courses and faculty vary widely. Law schools should encourage faculty to learn about AI and design assignments that help students develop sound professional judgment. Successful AI use will require guidance, resources, collaboration, professional development – as well as patience and persistence.
Law schools should help students build professional identities that integrate AI thoughtfully without outsourcing the core skills of legal thinking. Faculty should teach that AI is a tool to support professional client service, not a shortcut that avoids careful judgment. The goal is to build competence that students will need in practice, not to bypass the learning process.
Students should be required to use AI to enhance their learning and deepen their understanding of legal materials. This includes experimenting with prompts, comparing AI outputs to traditional research or writing, and reflecting on how to revise and improve AI-assisted work.
Academic integrity policies should be clear, specific, and realistic about how students actually use AI. Law schools and faculty should define appropriate and inappropriate uses, with examples tailored to different types of assignments. Students should be informed of and comply with institutional transparency policies.
Faculty should re-examine their grading and assessment practices to promote meaningful learning, reinforce ethical standards, and reflect evolving expectations in legal education and practice. Faculty should consider using AI to increase formative assessment of student learning and performance.
Students at under-resourced schools, and those without prior exposure to AI tools, may fall behind peers with greater access and skills. Law schools should ensure that all students and faculty have access to AI tools, training, and support – and that policies do not deepen digital divides.
Scholarship. Scholars should treat AI as a tool to assist – not replace – the human work of scholarship. Scholars may use AI to brainstorm ideas, review literature, and generate or edit drafts. They remain responsible for the analysis, argument, and ultimate authorship.
Law schools should review expectations about what constitutes original contribution, and how to define accountability, attribution, and intellectual ownership. They should also review hiring, promotion, and publication metrics to reward thoughtful AI integration while preserving intellectual rigor and human insight.
Institutions and Policymakers. Courts, bar associations, law schools, and employers should develop AI guidelines that reflect evolving practices and professional values. These guidelines should address competence, supervision, confidentiality, transparency, and accountability. Institutions should also support research, dialogue, and pilot projects to explore how AI can improve legal services, reduce costs, and expand access, especially for underserved communities.
The legal profession and academy are at a pivotal point of a major transition. AI is already changing how lawyers and neutrals practice, how students learn, and how scholars produce knowledge. These changes will almost certainly accelerate in the years ahead. This article highlights how scholars analyze the likely future and suggest ways to respond.
The scholars approach these developments with responsible realism. They are neither Pollyannas – cheerleading for a fantastic new world of AI – nor Cassandras foretelling calamity. Instead, they examine the evidence, consider the risks, and identify ways to adapt to evolving conditions. They do not agree on everything. But they agree that institutitions, practitioners, and educators should use AI thoughtfully and ethically.
Their recommendations – to build competence, promote sound judgment, and develop practical policies – are grounded in values that have long guided the legal field. These values remain relevant even as the tools and tasks evolve. Lawyers, educators, students, neutrals, and institutions will face challenges in using AI in the years ahead. Meeting those challenges will require reflection, experimentation, and humility. We should stay open to significant potential benefits – and alert to potential risks. A growing body of scholarship offers guidance grounded in careful analysis and shared purpose. That is what responsible realism looks like.
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