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Integrating Generative AI in Legal Pedagogy: A Case Study

Published online by Cambridge University Press:  16 December 2025

Bakht Munir
Bakht Munir*
Affiliation:
School of Law, The University of Kansas, United States. Email: bakht.munir@ku.edu.

Abstract

The integration of Generative Artificial Intelligence (GAI) is reshaping traditional legal pedagogy by introducing new dimensions to research, drafting, and personalized learning. Despite its transformative potential, the application of GAI in legal education remains promising and is accompanied by such challenges as algorithmic bias, privacy concerns, and the generation of fabricated or inaccurate content—issues that continue to divide scholarly opinion on its adoption. With this in mind, the University of Kansas School of Law offered a one-credit, one-hour course on “AI for Lawyers” during the summer of 2025. Along with reproducing the syllabus of the course and some of the professor’s observations, the paper includes an overview of AI and legal pedagogy and some recommendations going forward for integrating AI into law school curricula.

Keywords

AI toolsGenerative AIlegal educationLLMspedagogyUniversity of Kansas School of Law

Information

Type
Article
Information
International Journal of Legal Information , Volume 53 , Issue 3 , Winter 2025 , pp. 272 - 284
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2025. Published by International Association of Law Libraries

I. Introduction

Legal education has remained a foundation of the legal profession for centuries, cultivating the intellect of aspiring lawyers and providing them with the essential knowledge and skills to navigate the complexity of the legal landscape. Traditionally, legal education has been deeply rooted in the Socratic method, characterized by intensive case law analysis and dynamic classroom dialogue. This enduring pedagogical approach has proven instrumental in cultivating students’ critical thinking and legal reasoning abilities. Though historically effective, the traditional Socratic method in legal education often falls short in bridging theory with practice, leaving law students underprepared for real-world legal work.Footnote 1

The prevalent debates over legal education focus on whether vocational training or academic theory is more viable for potential lawyers, reflecting broader questions about their societal roles. While vocational models prepare law students for practice, academic approaches promote critical thinking and legal reform. Currently, many law schools adopt a hybrid model to produce graduates skilled in both practice and scholarship.

Artificial Intelligence (AI) is reshaping traditional education and the legal profession, influencing how we learn and work. Despite the revolutionary transformation of AI across various industries, the educational sector faces a dilemma—that is, whether there should be a ban on the use of AI in educational institutions. Instead of revamping the traditional Socratic teaching and learning approach, students who leverage AI for their academic activities, such as research and writing assignments, are subject to disciplinary actions. This phenomenon cannot be left unnoticed, necessitating the reform of prevalent legal pedagogy.

With the intersection of AI and law, coupled with the advent of Large Language Models (LLMs) that can pass law school and uniform bar exams, legal educators must prepare to familiarize themselves with modern technological advancements to transform their pedagogies. The legal profession is increasingly being transformed by advancements in GAI, necessitating reliance on contemporary AI tools for imparting legal education. However, this transformation is still in its infancy and is vulnerable to ethical challenges, such as bias, data privacy, and the authenticity of generated responses.Footnote 2 Like other critical fields, such as healthcare and finance, the accuracy of legal information is paramount.Footnote 3 The AI solutions used in legal pedagogy confront ethical issues that affect their performance and reliability.

This paper contributes some practical suggestions on how to improve the performance of these tools and cautions educators when imparting them in legal education. The work is broadly divided into the following parts: Part II offers an overview of teaching law with the help of AI. Part III highlights the prospects and impacts of AI on legal education, and Part IV offers pedagogical approaches and policy guidelines. Part V describes the University of Kansas School of Law’s summer 2025 course on “AI for Lawyers.” Part VI offers valuable suggestions to improve the performance of AI systems in imparting legal education, and Part VII concludes the paper. The paper’s Appendix includes the course’s syllabus and some of the professor’s (and author’s) observations.

II. Overview of AI in Legal Education

Recent studies suggest that AI will significantly impact the legal profession by automating legal tasks.Footnote 4 Traditional legal education inspires a Socratic approach to critical thinking. However, the emergence of AI has revealed a gap between theory and practice, necessitating the intersection of AI in legal education. Although AI solutions offer research efficiency and personalized learning, their implications remain unavoidable. The current pace of AI integration into the legal profession indicates that it cannot replace human lawyers, but mastering AI is essential for a tech-driven future where legal professionals must confront the ethical issues surrounding AI systems.Footnote 5 AI presents an unparalleled opportunity to leverage the legal profession, transforming traditional legal practice by providing numerous services such as research, contract drafting, document analysis, and case management. AI solutions automate repetitive tasks, saving time and human resources, which allows legal professionals to focus on more complex and strategic aspects of their work.Footnote 6

Given the transformative potential of AI, legal pedagogy should integrate AI solutions to navigate the legal complexities with modern technological solutions. By realizing the capacities and intrinsic limitations of AI systems, prospective lawyers can leverage these technologies to harness their professional careers. AI-oriented legal education can help prepare students to make the best use of AI solutions while considering their ethical challenges, allowing them to provide efficient and effective legal services, including advice to their clients on issues related to data privacy and potential outcomes of their cases.Footnote 7

Unlike traditional technologies that were used for the execution of various tasks, GAI, a subset of AI, creates new content in response to users’ prompts.Footnote 8 GAI has significantly impacted several industries, including legal education, transforming traditional teaching and learning methods.Footnote 9 However, AI systems confront certain inherent challenges, necessitating a thorough assessment of their performance.Footnote 10 Despite these challenges, legal educators should adapt to the AI systems to ensure that potential lawyers remain competitive in the global market and the legal industry does not fall behind in a rapidly changing landscape.

Researchers have identified several key elements integral to the teaching and learning process existing in law schools: the transmission of substantive legal knowledge; the development of core analytical skills essential for legal practice; legal research methods; and practical lawyering skills.Footnote 11 Modern research has also stressed interdisciplinary knowledge relevant to the legal profession, primarily preparing potential lawyers with the knowledge and skills essential for a successful legal career.Footnote 12

GAI has added new trends to the existing educational system, such as prompting skills to guide an AI system in generating a desired response. Given the increasing tendency of AI integration into the legal profession, law schools must focus on how to effectively engage with AI solutions for legal research, case analysis, and contract drafting. Effective integration of GAI into legal education necessitates addressing key concerns while leveraging its potential. Law schools must develop strategies that enhance learning through AI tools without compromising academic integrity.Footnote 13

Nevertheless, this shift demands a deeper understanding of the ethical implications associated with the employment of AI systems in the legal sphere to navigate the intersection of technology and legal practice.Footnote 14 Hence, examining GAI’s impact on the pedagogy of law is essential for law schools to stay ahead of technological advancements in legal practice, preparing potential lawyers for evolving professional demands.

Given its broadened scope in the legal field, AI is expected to transform the traditional legal profession.Footnote 15 Therefore, legal education must integrate AI literacy to prepare legal professionals with essential skills.Footnote 16 The widespread use of AI underscores the necessity of adopting tech-oriented legal pedagogy and learning skills.Footnote 17 Educational institutions worldwide have taken diverse approaches to the incorporation of AI in the educational sector: some have banned the use of AI solutions, while others have embraced its use as a significant part of their pedagogy.Footnote 18

Those who advocate for a ban on the use of AI solutions raise concerns about the potential negative impacts on students’ learning, which may impair their analytical thinking and accuracy, and present ethical challenges associated with the employment of AI.Footnote 19 While proponents of integrating AI stress its instructional potential, elaborating on complex concepts and assisting students in advancing their writing skills, determining its use in legal education should not be solely based on describing its pros and cons.Footnote 20 It should be acknowledged that the legal profession has already begun to adopt the integration of AI, which could be one of the most significant revolutions in human history.Footnote 21 Hence, it is crucial for academic institutions to fully realize and embrace their transformative potential in reshaping teaching methods, learning models, and academic research.

III. Prospects and Impacts

Excessive reliance on AI tools may erode critical thinking, potentially encouraging academic dishonesty as students increasingly use them for assignments and exams.Footnote 22 AI-generated content often contains inaccuracies or fabricated responses, stemming from the probabilistic output of AI tools, posing a risk to the integrity of legal research.Footnote 23 These tools often work like black boxes; their internal processes are hidden from users, and the quality of their output depends heavily on the data they were trained on and how they were built. This makes transparency and oversight essential for ensuring reliable performance.Footnote 24 In addition to their inaccuracies, the GAI models can perpetuate and amplify biases present in their training data, leading to unfair and discriminatory outcomes.Footnote 25 Algorithmic bias poses serious ethical challenges, as seen in the grading controversy in the United Kingdom (UK), where state school students were unfairly penalized compared to their peers in private institutions.Footnote 26 Similarly, default gendered pronouns in AI systems can reinforce stereotypes by linking certain professions to specific genders.Footnote 27

To tackle these challenges, educators, policymakers, and developers should work in coordination, train AI models on diverse datasets, and routinely audit outputs to address these ethical concerns. Transparency and accountability remain critical, as the opaque nature of AI decision-making makes it hard to trace errors or assign responsibility.Footnote 28

As AI enhances personalized learning, it also introduces ethical risks that demand clear guidelines. Educators must promote academic honesty and responsible AI use, fostering independent reasoning and judgment. Moreover, the continuous evaluation of AI is critical to balance technological advancement with ethical standards and uphold the integrity of legal education.

Since the emergence of AI tools like ChatGPT, GAI has been reshaping education and legal practice, prompting debates over academic integrity. While human lawyers remain essential, AI has become integral to legal practice, making AI literacy essential. Lawyers need not be AI experts but must grasp its uses, limits, and ethical dimensions.Footnote 29 GAI empowers law students with tools that advance research efficiency, refine drafting, and simplify complex legal concepts. Given the complex nature of law and its application, AI won’t replace lawyers; rather, it will reshape legal practice, making AI literacy essential. As AI’s influence grows, integrating it into legal education becomes inevitable.

A report by the Institute for the Advancement of the American Legal System (IAALS) stresses that competent lawyers require knowledge, legal expertise, and practical skills. While law schools address the first two, practical skills are developed through experiential learning, such as internships and legal aid projects, which should now incorporate AI proficiency.Footnote 30

Despite its numerous advantages, integrating GAI into legal education poses challenges. Academics fear that students may use AI to bypass traditional assessments, such as writing essays, problem-based tasks, and critical thinking, potentially hindering the development of essential legal skills, resulting in divergent opinions on the use of GAI. Some faculty have tested the capabilities of these tools and found them instrumental in academic tasks, such as curriculum design, content creation, teaching, assessment, and providing feedback.Footnote 31

Successful integration of GAI into legal education requires striking a balance between its benefits and ethical concerns. Law schools should enhance learning while preserving academic integrity, establishing guidelines for its responsible use, designing assignments that enhance critical capacities, and consistently updating the curricula to keep pace with technological advancements.Footnote 32

While promoting AI literacy, law schools face challenges because practical application is key to adopting GAI: some institutions consider integrating GAI in their curricula unnecessary, while several schools have issued guidelines to regulate it or necessitate its disclosure in the completion of assessments.Footnote 33 Nevertheless, given its growing impact on the legal profession, students must develop AI proficiency to remain competitive since excluding AI literacy could impede their ability to meet evolving demands. Certainly, many students are already employing GAI for their assignments; unregulated use may compromise critical thinking and depth of knowledge. Legal education must embrace assessment methods, fostering creativity and active engagement, and diminishing passive dependence on AI.Footnote 34 With thoughtful integration, clear guidelines, and balanced strategies, GAI can significantly enrich learning and train students for a technology-driven legal future.

Integrating GAI in legal curricula calls for a holistic approach, necessitating a comprehensive strategy that embeds AI across courses to align with core educational objectives and prepare students for evolving professional demands.Footnote 35 Ethical use of AI can be taught through dedicated modules on AI ethics, data privacy, and professional responsibility, enabling students to navigate complex ethical issues in legal practice.

Faculty experimentation and the exchange of best practices play a significant role in continuously improving teaching methods, preparing students for an AI-driven legal profession. Despite the promising potential of GAI in revolutionizing legal education, critics argue that relying on AI tools may undermine critical pedagogical principles, leading to passive learning instead of the desired active engagement.Footnote 36 GAI has the potential to transform passive learning into active learning.Footnote 37 By generating complex legal scenarios and ethical analysis, cognitive skills can be developed, which are critical for modern legal practice.Footnote 38

To enhance students’ competitiveness in the legal market, law schools should integrate AI tools into their curricula. Though some schools have started offering AI-focused courses, these initiatives remain in their infancy. Effective integration requires identifying and reinforcing essential human-centric skills, such as critical analysis, problem solving, and client-centered advocacy, where AI cannot outperform human judgment.Footnote 39

Three skills will remain crucial for human lawyers in the modern-day, AI-influenced legal landscape: innovation, judgment, and accountability.Footnote 40 Unlike AI, which relies on data patterns, human creativity and experience enable nuanced reasoning, ethical decision-making, and strategic problem-solving, which are the skills essential to legal practice.Footnote 41

IV. Pedagogical Approaches and Policy Guidelines

The integration of AI in education has yielded promising results, as illustrated by various initiatives like the development of Jill Watson, an AI teaching assistant, by the Georgia Institute of Technology in 2016. It was designed to manage high volumes of student queries in an online Knowledge-Based AI (KBAI) course, which demonstrated how AI can scale personalized learning by delivering efficient, individualized support.Footnote 42 It reflects that the potential of AI can be leveraged to scale personalized learning, providing individualized assistance to a large number of students efficiently.

The growing influence of AI in the legal profession has led to a significant shift in legal education. Yale Law School, through its Information Society Project (ISP), offers courses such as “AI, Robotics, and the Law” and “Law and Disruptive Technology.” Yale has also pioneered AI model development for legal applications and models for media law. Similarly, Georgetown Law has emerged as a leader in AI curriculum integration, offering at least seventeen AI-related courses.Footnote 43

Law schools face the complex challenge of integrating AI into research and assignments while maintaining academic integrity. Some schools permit the faculty to set individual AI usage policies, allowing flexibility to support foundational skill development. In response to increasing global demand, institutions like the Northwestern Pritzker School of Law have started to revise integrity policies, encouraging the exchange of best practices and offering AI tools like Lexis+ AI to final-year students.

Harvard Law School and the University of Michigan Law School are also at the forefront of AI education, advancing innovation in legal training. Harvard Law focuses on how AI is changing the legal profession and the skills required for future lawyers, while Michigan Law offers courses examining AI’s impact on legal domains, autonomous weapons, vehicles, and criminal sentencing, with a focus on regulatory and legal frameworks rather than AI as a direct legal tool. Globally, universities are incorporating AI to enhance and navigate the complexities of the modern legal profession. North Carolina Central University (NCCU) School of Law fosters international collaboration by connecting faculty from 104 law schools in AI and law courses.Footnote 44 These developments reflect a steady global shift toward embedding AI within legal education.

V. University of Kansas School of Law: “AI for Lawyers”

To address the increasing need for educating future lawyers in the use of AI, the University of Kansas School of Law offered a five-day, one-credit, one-hour course on “AI for Lawyers” during the summer of 2025. Seven students enrolled in the course.

Structured as both a practical and theoretical investigation of AI in legal settings, the course equipped students with skills to critically and ethically evaluate the performance of AI technologies. With a dynamic combination of lectures, live demonstrations, and case law analysis across various jurisdictions, such as the US, the UK, Canada, Australia, and Pakistan, students developed a solid foundation of AI fundamentals, traced AI’s historical evolution, and critically examined the rise of generative AI tools, such as ChatGPT, Copilot, Lexis+ AI, and Gemini. The course emphasized legal accuracy and the importance of safeguarding the integrity of AI-generated content, particularly by identifying and addressing AI hallucinations, where models produce plausible yet inaccurate or fabricated outputs that can pose serious risks in legal proceedings. The course also examined standing orders of the courts, judicial ethics, and policies governing the use of AI in legal operations.

The course’s core activity was a collaborative empirical research project meant to evaluate the performance of seven distinct AI tools with a series of legal prompts, along with their corresponding human answers developed by the students. Each tool was rigorously examined for its legal reasoning, doctrinal accuracy, and citation reliability, utilizing a standardized grading rubric. The research project offered comparative insight into how these AI models handle legal prompts, highlighting the practical capabilities and limitations of AI technologies in real-world legal settings. The course’s syllabus and some of the professor’s (and this author’s) observations are included in the paper’s Appendix.

VI. Recommendations

The following are recommendations for effectively integrating GAI in legal education while upholding academic integrity:

  1. 1) Mandatory AI ethics courses: Law schools should introduce mandatory AI ethics courses, encompassing topics like data privacy, algorithmic transparency, accountability, and bias mitigation strategies.

  2. 2) Sanctions on the irresponsible use of AI tools: Law schools should introduce courses on case law where the courts have imposed penalties and initiated disciplinary actions against the lawyers who employed AI tools in their filings without counterchecking their accuracy. This will help prepare future lawyers for the responsible use of technology in their practices.

  3. 3) AI and its application in legal research, drafting, and analysis: Law schools should provide practical experience and encourage interdisciplinary coordination to prepare students to navigate the complexities of the tech-driven legal landscape.

  4. 4) Faculty training: Faculty training is essential for the effective integration of AI in legal education. Schools should offer faculty development programs to enhance faculty proficiency in AI technologies, which will enable them to integrate AI into their pedagogy. Further, encouraging faculty to experiment with AI in their classrooms and share best practices among peers can facilitate this integration.

  5. 5) Ensuring ethical and effective employment of AI: Law schools should keep current with assessments to evaluate students’ ethical and effective use of AI, focusing on performance-based tasks, project-based learning, and formative assessments. Clear rubrics should be established to balance technical proficiency with critical thinking and legal reasoning.

  6. 6) Adaptable and transparent AI policies: Law schools should develop transparent AI policies that can draw a clear line between permissible and non-permissible uses of GAI. These guidelines must be easily accessible, relevant, and regularly updated to remain effective with the evolving complexities in the legal landscape.

  7. 7) Investment in AI infrastructure and resources: Law schools should invest in AI resources and infrastructure, including the provision of AI tools, computing facilities, and technical support. Collaboration with tech companies can facilitate access to advanced AI tools and real-world exposure.

  8. 8) Plagiarism detection techniques: To uphold academic integrity, law schools should adopt alternative approaches to plagiarism detection, recognizing the limitations of the prevalent tools. Underlining faculty discretion and implementing strategies like personalized assessments and discussions about students’ work processes will help maintain the integrity of student contributions.

  9. 9) Transparent communication: Promoting transparent communication among students, faculty, and law school administrations is necessary to clarify AI policies and foster continuous improvement. Accountability mechanisms for ethical AI use help ensure that technology enhances learning while upholding the core values of legal education.

  10. 10) Bridging the gap between legal education and the legal industry in AI integration: To ensure the effective integration of AI into legal education, it is essential to foster closer collaboration between law schools and the legal industry. Such coordination will enable academic institutions to align their curricula with the evolving demands of legal practice, thereby equipping students with practical competencies in the use of AI tools. Exposure to real-world applications will enhance students’ understanding of the capabilities and limitations of these technologies, particularly in areas requiring creativity, subjective legal reasoning, and professional accountability. By embedding industry-informed AI training in legal education, graduates will be better prepared to navigate the complexities of modern legal environments and contribute meaningfully to the profession.

VII. Conclusion

As exemplified by the University of Kansas School of Law’s 2025 AI summer course, the integration of AI into legal education holds the potential to transform traditional pedagogical approaches and significantly enhance the development of practical legal skills. While AI technologies are revolutionizing legal research, drafting, and personalized learning, their adoption presents a complex interplay of opportunities and challenges. To prepare future legal professionals for leadership in a digitally evolving landscape, it is imperative to equip law students with AI literacy, ethical concerns, and hands-on experience with intelligent systems.

The above underscores the importance of ongoing research to address emerging risks, improve the design and functionality of AI-driven educational tools, and ensure equitable access across diverse learning environments. Embracing the transformative potential of AI will not only modernize legal education but also empower potential lawyers to uphold justice in an increasingly tech-integrated world.

To further ensure the trustworthy and responsible use of AI in legal education, interdisciplinary collaboration is essential. Sustained engagement between legal scholars, AI developers, and policymakers is imperative to formulate comprehensive frameworks that support ethical innovation. Ultimately, while GAI offers profound pedagogical advantages, its limitations must be critically addressed to guarantee the accuracy, reliability, and contextual relevance of its contributions to legal scholarship and practice.

APPENDIX

The following pages contain the course syllabus for “AI for Lawyers” and a day-by-day description of class activities. These materials offer critical insights into how legal education can evolve to prepare future attorneys with the acumen required for AI-assisted advocacy.

ARTIFICIAL INTELLIGENCE (AI) FOR LAWYERS

(1 Credit Hour)

Summer 2025

COURSE INSTRUCTOR

Dr. Bakht Munir

Postdoctoral Research Fellow

The University of Kansas School of Law

Email:

CLASS TIME AND LOCATION

Room No. GRN Day and Time

COURSE DESCRIPTION

This introductory one-credit-hour course for JD and SJD students explores the transformative potential of Artificial Intelligence (AI) in the legal profession, requiring no prior AI knowledge. It provides a comprehensive overview of AI technologies, enhancing legal practice, improving efficiency, and addressing ethical considerations. Students will learn fundamental AI concepts, use AI tools for legal research, document review, and automation, and understand predictive analytics in law. The course also covers AI’s ethical and legal implications in legal services.

Course Goals

The primary goal of this course is to equip students with a comprehensive understanding of AI and its applications in the legal profession. By the end, students will have practical skills in using AI tools for legal research, document review, and predictive analytics, along with a deep understanding of AI’s ethical and legal implications. The course fosters critical thinking about the benefits and challenges of integrating AI into legal practice, preparing students to use AI technologies effectively and responsibly.

Learning Outcomes

  • • Understand the basic principles of AI and machine learning.

  • • Identify and utilize AI tools for legal research and document review.

  • • Apply predictive analytics to legal cases and understand their limitations.

  • • Evaluate the ethical and legal challenges AI poses in the legal profession.

GRADING POLICY

  • • Class Participation: 10%

  • • Assignment: 20%

  • • Presentation: 20%

  • • Final Exam/Project: 50%

RECOMMENDED READINGS

Each class has been provided with links to relevant literature, such as research articles, websites, and case law.

KU SCHOOL OF LAW ACADEMIC REGULATIONS

Academic Regulations | KU School of Law

See the link for policies on attendance, grading, examination, etc.

NOTE: Students are expected to review the pre-class reading materials and be prepared to discuss all assigned readings during class.

Recommended Books:

Munir, Bakht. Artificial Intelligence for Lawyers: Navigating Novel Methods and Practices for the Future of Law (2025), https://kuscholarworks.ku.edu/entities/publication/2c576502-ce94-4a0a-9c73-0e7f7f36da44.

COURSE MODULES

Day 1: Introduction to AI in Law

Theory:

  • • Basic concepts of AI, AI applications in various industries, and specific examples in the legal profession.

  • • Explaining key terminologies such as Machine Learning (ML), Natural Language Processing (NLP), Large Language Models (LLMs), and Artificial Neural Networks (ANNs).

  • • Historical Development of AI.

Readings:

Discussion: Significance of AI in legal operations.

Practical/Lab Work:

  • • Differentiating Legal and General-Purpose AI Applications.

  • • Creating a Login to access the AI Software.

  • • Guidelines for using AI solutions.

  • • What AI tool do you like most, considering its interface, responses, and guidelines?

  • • Can you identify any flaws in your favorite AI tool with a suggestion on how it can be further improved?

Day 2: AI Tools for Legal Research and Document Review

Theory:

  • • Various AI tools’ performance, capabilities, advantages, and limitations of various AI tools.

  • • Ethical Challenges, such as Bias, Data Privacy, and Hallucinations.

Readings:

Discussion: Benefits and limitations of AI tools in legal research.

Practical/Lab Work:

  • • AI Tool: Students’ live demonstrations on the use of a different AI tool, including ChatGPT 4, Copilot, DeepSeek, Gemini, and Lexis+ AI.

Day 3: Case Law Study on Bias and Hallucinations

Theory:

  • • Kinds of Bias and Hallucinations.

  • • Comparative case law analysis (US, UK, Australia, Canada, Pakistan).

Readings:

Discussion: Analyze different AI applications in legal practice and their inherent limitations.

Practical/Lab Work:

  • • AI Tools: Lexis+ AI, Copilot, ChatGPT4, Gemini, DeepSeek.

  • • Discussion on their probabilistic nature, inherent limitations, and the concept of the black box.

Day 4: Courts’ Standing Orders and Policy guidelines on the use of AI in Courts

Theory:

  • • Critical analysis of the courts’ standing orders, task force recommendations, policy guidelines, ethical regulations, and judicial ethics frameworks across the US, Canada, the UK, Australia, and Pakistan.

Readings:

Discussion: Case studies on ethical challenges in AI. Court Standing Orders and Responsible Use of AI.

Practical/Lab Work:

Discussions on the limitations and ethical implications of AI tools.

Day 5: Final Presentations and Discussions

Theory:

  • • Mitigating Strategies: Various strategies to overcome the issues of bias and hallucinations.

  • • Prompt-Crafting Techniques.

  • • Retrieval Augmented Generation (RAG) architecture and its limitations.

Readings:

Discussion: Future trends and skills needed for AI in legal practice, improving prompting skills, and creating an AI personal learning assistant.

Practical/Lab Work:

  • • Activity: Presentations on the final project: Each student will be required to present one AI tool performance based on the real-world legal propositions, human responses to the prompts, and their comparison with the AI-generated responses.

  • • Group Presentation of the Final Report and overall performance of various AI tools.

  • • Prioritizing AI tools based on the project.

Instructor’s Description and Reflections on the Course

Course Structure and Daily Activities

The five-day intensive course activities covered foundational concepts and applied case studies, ensuring students had a well-rounded understanding of AI in legal practice.

Day 1: Historical Evolution of AI

The course began with conceptualizing AI and its historical evolution, from ancient automata to modern generative language models.

The discussion covered key milestones in the evolution of AI, beginning with the creation of the mechanical pigeon in 400 BCE by Archytas of Tarentum, an early example of automata. It then moved to the introduction of the term “robot” by Karel Čapek in 1921 and the creation of Japan’s first robot, Gakutensoku, by Professor Makoto Nishimura. The development of AI as a field gained momentum with Alan Turing’s Imitation Game, a test for computer intelligence, followed by John McCarthy’s 1956 conference, where the term “artificial intelligence” was formally introduced. AI continued to advance, marked by IBM’s Deep Blue defeating chess world champion Garry Kasparov, demonstrating early machine intelligence. The foundation of OpenAI in 2015 further accelerated AI innovation, leading to the rise of generative AI and LLMs, which now play a transformative role across industries.

This discussion provided a foundational basis for technology and how it inspired the creation of AI.

Day 2: AI Tools and Hallucinations

On the second day, each student demonstrated a different AI tool relevant to legal practice, providing live presentations on their respective tool’s capabilities, advantages, and limitations. This hands-on approach allowed students to critically assess how AI can assist legal professionals while identifying potential weaknesses in its application.

Following the demonstrations, a deep discussion unfolded on the probabilistic nature of AI systems, particularly the challenges posed by their black-box mechanisms, where the internal decision-making processes remain largely opaque. Students examined ethical concerns, such as data privacy, algorithmic bias, and AI hallucinations, where AI generates fabricated or inaccurate legal content. This discussion reinforced the importance of verification and safeguards in legal research, ensuring that AI remains a supplementary tool rather than a substitute for human legal expertise.

Day 3: AI Ethics and Misuse Cases

The third day centered on ethical considerations, with a strong emphasis on bias and AI hallucinations. Discussions explored various types of bias in AI models, the underlying reasons for hallucinations, and the different forms these inaccuracies take.

Students presented case summaries, including Mata v. Avianca, Inc., People v. Crabill, United States v. Michael Cohen, United States v. Pras Michel, and Iovino v. Michael Stapleton Associates, critically analyzing instances where AI-generated hallucinations, such as fictional citations, impacted legal proceedings.

To provide a global perspective, the discussion expanded to AI misuse cases encountered in Australia, Canada, the UK, and Pakistan, comparing how different jurisdictions respond to AI-related legal challenges. By examining international regulatory approaches, students gained insight into the broader implications of AI use in legal practice and the varying degrees of accountability imposed worldwide.

Day 4: Standing Orders on the Use of AI in Legal Practice

On the fourth day, students conducted a critical analysis of over thirty courts’ standing orders, task force recommendations, policy guidelines, ethical regulations, and judicial ethics frameworks across the US, Canada, the UK, Australia, and Pakistan. This examination highlighted the growing regulatory efforts aimed at governing AI-assisted legal research and writing, emphasizing the need for heightened accuracy and accountability in AI-generated legal documents.

In the US, courts have taken varied approaches to AI regulation. While some jurisdictions prohibit AI-generated content in legal filings, others require attorneys to verify all citations and disclose AI usage. These measures reinforce the ethical obligations of legal professionals, ensuring safeguards against AI-generated hallucinations and inaccuracies that could compromise legal integrity.

By contrast, Canada, the UK, Australia, and Pakistan have introduced judicial guidance and ethical policies that focus on responsible AI adoption rather than outright bans. Courts in these regions generally mandate transparency, requiring attorneys to disclose AI use, verify cited legal authorities, and ensure accuracy in AI-assisted legal drafting, closely mirroring the regulatory stance taken by the US.

The discussion underscored a global trend toward balancing AI innovation with robust regulatory safeguards, ensuring AI remains a complementary tool rather than an unregulated substitute for human legal expertise.

Day 5: Final Project – Evaluating AI Systems for Legal Accuracy

The culminating project aimed to assess AI tools’ accuracy in legal reasoning. Each student tested one AI model—ChatGPT, Google Gemini, Microsoft Copilot, Claude, Deepseek, Lexis+ AI, and Grammarly—by posing legal questions and comparing AI-generated responses to expert human answers.

Students evaluated responses as Incorrect, Partially Correct, or Correct, based on legal precision, clarity, and doctrinal correctness.

According to the students’ research, Lexis+ AI outperformed other AI tools, aligning its responses with statutory law and case law references, while Grammarly, though useful for grammar and syntax, lacked substantive legal citations. Students also analyzed accuracy with legal questions, noting a strong correlation between AI errors and ambiguous phrasing. For instance, most models incorrectly answered a question on fault elements in Kansas, suggesting AI’s difficulty in distinguishing nuanced jurisdiction-specific legal definitions.

Conclusion and Future Directions

This course exemplifies the University of Kansas School of Law’s commitment to preparing future lawyers for the evolving landscape of AI in legal practice. Through critical evaluation and practical engagement with AI tools, JD scholars have gained insights into both AI’s potential and its limitations in law. The findings of this project underscore the significance of AI verification measures, reinforcing the role of human legal reasoning in maintaining professional integrity.

References

1 Aswathy Prakash G. and Vishnu Nair, “Integrating Generative AI into Legal Education: From Casebooks to Code, Opportunities and Challenges,” Law, Technology and Humans 6, no. 3 (2024): 60–79, https://doi.org/10.5204/lthj.3640.

2 Ahmed Raza, Bakht Munir, Gouhar Ali, Mubashar Ahmed Othi, and Rana Abrar Hussain, “Balancing Privacy and Technological Advancement in AI: A Comprehensive Analysis of the US Perspective,” International Journal of Contemporary Issues in Social Sciences 3, no. 3 (2024): 3732–38.

3 B. Munir, M.Z. Abbasi, W.B. Wilson, and A. Colombo, “Evaluating AI in Legal Operations: A Comparative Analysis of Accuracy, Completeness, and Hallucinations in ChatGPT-4, Copilot, DeepSeek, Lexis+ AI, and Llama 3,” International Journal of Legal Information, 53, no. 2 (2025): 103–14, doi:10.1017/jli.2025.10052.

4 Darryl R. Mountain, “Disrupting Conventional Law Firm Business Models Using Document Assembly,” International Journal of Law and Information Technology 15, no. 2 (2007): 170–91, https://doi.org/10.1093/ijlit/eal019; Richard Susskind, The End of Lawyers? Rethinking the Nature of Legal Services (Oxford University Press, 2008).

5 Ian Rodgers, John Armour, and Mari Sako, “How Technology Is (or Is Not) Transforming Law Firms,” Annual Review of Law and Social Science 19 (2023): 299–317, https://doi.org/10.1146/annurev-lawsocsci-111522-074716.

6 Bakht Munir, Akhtar Ali Ansari, and Yasir Arafat, “Artificial Intelligence and Legal Decision-Making in the USA and Pakistan: A Critical Appreciation of Regulatory Frameworks,” Global Foreign Policies Review 7, no. 4 (2024): 48–58, https://doi.org/10.31703/gfpr.2025(XI).06.

7 M. Salman Abbasi, Bakht Munir, V. Jayaram, and P. Rivas, “Leveraging Autocorrelation in a Dilated CNN-LSTM Framework for Predicting the US Supreme Court Decisions,” IEEE Access 13 (2025): 161250–61, https://doi.org/10.1109/ACCESS.2025.3609282.

8 Jonas Oppenlaender, Rhema Linder, and Johanna Silvennoinen, “Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering,” International Journal of Human–Computer Interaction 41, no. 16 (2024): 10207–29, https://doi.org/10.1080/10447318.2024.2431761.

9 Marjan Ajevski, Kim Barker, Andrew Gilbert, Liz Hardie, and Francine Ryan, “ChatGPT and the Future of Legal Education and Practice,” The Law Teacher 57, no. 3 (2023): 352–64, https://doi.org/10.1080/03069400.2023.2234021.

10 Bakht Munir, Muhammad Zubair Abbasi, W. Blake Wilson, and Allen Colombo Jr., “Hallucinations in Legal Practice: A Comparative Case Law Analysis,” International Journal of Law, Ethics, and Technology 5, no. 2 (2025): 85–109, https://ijlet.org/wp-content/uploads/2025/07/Vol-2025-No-2-Final-f.pdf.

11 Asyia Kazmi, “Can AI Transform Education?,” Bill & Melinda Gates Foundation, Sep. 11, 2024, https://www.gatesfoundation.org/ideas/articles/ai-tools-education-technology.

12 Stephanie Gerald, “Conclusion: Prepare for the Evolution of AI in Law,” The Colleges of Law (blog), Feb. 22, 2024, https://www.collegesoflaw.edu/blog/2024/02/22/artificial-intelligence-law-evolution/.

13 M. Perkins, “Academic Integrity Considerations of AI Large Language Models in the Post-Pandemic Era: ChatGPT and Beyond,” Journal of University Teaching & Learning Practice 20, no. 2 (2023), https://doi.org/10.53761/1.20.02.07.

14 Mohammad Ali, “Exploring the Role of AI in Modern Legal Practice: Opportunities, Challenges, and Ethical Implications,” Journal of Electrical Systems 20, no. 6s (2024): 3040–50.

15 Mateus de Oliveira Fornasier, “Legal Education in the 21st Century and the Artificial Intelligence,” Revista Opinião Jurídica 19, no. 31 (2021): 1–32.

16 Samantha A. Moppett, “Preparing Students for the Artificial Intelligence Era: The Crucial Role of Critical Thinking Skills,” Suffolk University Law School Research Paper 25-4 (Mar. 25, 2025).

17 George, A. Shaji, “Preparing Students for an AI-Driven World: Rethinking Curriculum and Pedagogy in the Age of Artificial Intelligence,” Partners Universal Innovative Research Publication 1, no. 2 (2023): 112–36.

18 Ping Xiao, Yuanyuan Chen, and Weining Bao, “Waiting, Banning, and Embracing: An Empirical Analysis of Adapting Policies for Generative AI in Higher Education,” arXiv.org (May 25, 2023): arXiv:2305.18617, https://doi.org/10.48550/arXiv.2305.18617; Yueqiao Jin, Lixiang Yan, Vanessa Echeverria, Dragan Gašević, and Roberto Martinez-Maldonado, “Generative AI in Higher Education: A Global Perspective of Institutional Adoption Policies and Guidelines,” Computers and Education: Artificial Intelligence 8 (2025): 100348, https://doi.org/10.1016/j.caeai.2024.100348.

19 Xiao, Chen, and Bao, “Waiting, Banning, and Embracing.”

20 Thomas K.F. Chiu, Qi Xia, Xinyan Zhou, Ching Sing Chai, and Miaoting Cheng, “Systematic Literature Review on Opportunities, Challenges, and Future Research Recommendations of Artificial Intelligence in Education,” Computers and Education: Artificial Intelligence 4 (2023): 100118, https://doi.org/10.1016/j.caeai.2023.100118.

21 International Bar Association, The Future Is Now: Artificial Intelligence and the Legal Profession (International Bar Association, 2024).

22 Joseph A. Crawford, Michael Cowling, and Kelly-Ann Allen, “Leadership Is Needed for Ethical ChatGPT: Character, Assessment, and Learning Using Artificial Intelligence (AI),” Journal of University Teaching & Learning Practice 20, no. 3, 1–19 art. 02 (2023), https://open-publishing.org/journals/index.php/jutlp/article/view/645/645.

23 N. Maleki, B. Padmanabhan, and K. Dutta, “AI Hallucinations: A Misnomer Worth Clarifying,” in Proceedings of the 2024 IEEE Conference on Artificial Intelligence (CAI), Singapore (2024): 133–38, https://doi.org/10.1109/CAI59869.2024.00033.

24 Joseph Crawford, Michael Cowling, and Kelly-Ann Allen, “Leadership is Needed for Ethical ChatGPT: Character, Assessment, and Learning using Artificial Intelligence (AI),” Journal of University Teaching & Learning Practice 20, no 3 (2023): 2, https://doi.org/10.53761/1.20.3.02.

25 Bakht Munir, “Islamophobic Artificial Intelligence in the USA: A Critical Analysis of Religious Bias in Datasets,” Law Library Journal (forthcoming, 2026).

26 Ryan S. Baker and Aaron Hawn, “Algorithmic Bias in Education,” International Journal of Artificial Intelligence in Education 32 (2021): 1052–92, https://doi.org/10.1007/s40593-021-00285-9.

27 J.A. de Bruijn, Martijn Warnier, and Marijn F.W.H.A. Janssen, “The Perils and Pitfalls of Explainable AI: Strategies for explaining algorithmic decision-making,” Government Information Quarterly 39, no. 2 (2022): 101666, https://doi.org/10.1016/j.giq.2021.101666.

28 Ibid.

29 Samantha Kontra, “Building GenAI into Law Assessments: How Do Students Use GenAI When Given the Chance?,” paper presented at the 2024 Australasian Law Academics Association Conference, Adelaide, Australia (July 4, 2024); Ian Rodgers, John Armour, and Mari Sako, “How Technology Is (or Is Not) Transforming Law Firms,” Annual Review of Law and Social Science 19, no. 1 (2023): 299–317, https://doi.org/10.1146/annurev-lawsocsci-121122-010104; Jordan Bigda, “The Legal Profession: From Humans to Robots,” Journal of High Technology Law 18 (2017): 396; Davy Tsz Kit Ng, Jac Ka Lok Leung, Samuel Kai Wah Chu, and Maggie Shen Qiao, “Conceptualizing AI Literacy: An Exploratory Review,” Computers and Education: Artificial Intelligence 2 (2021): 100041, https://doi.org/10.1016/j.caeai.2021.100041; Michael D. Murray, “Artificial Intelligence and the Practice of Law Part 1: Lawyers Must Be Professional and Responsible Supervisors of AI,” SSRN Scholarly Paper No. 4478588 (2023), https://ssrn.com/abstract=4478588; Jamie J. Baker, “2018: A legal research odyssey: Artificial intelligence as disruptor,” Law Library Journal 110 (2018): 5.

30 Logan Cornett, “Better Lawyer Licensing, Part 4: The Final Three Building Blocks of Minimum Competence,” Institute for the Advancement of the American Legal System (blog), Oct. 2, 2023, https://iaals.du.edu/blog/better-lawyer-licensing-part-4-final-three-building-blocks-minimum-competence.

31 Cornelia Koch, “Using Generative AI for Assessment Design, Evaluation and Feedback: Opportunities and Challenges,” paper presented at the 2024 Australasian Law Academics Association Conference, Adelaide, Australia (July 5, 2024).

32 Priten Shah, AI and the Future of Education: Teaching in the Age of Artificial Intelligence (John Wiley & Sons, 2023).

33 Angelica Dino, “American Bar Association Survey Highlights Growing Integration of AI in Legal Education,” Canadian Lawyer (July 9, 2024); Karl de Fine Licht, “Generative Artificial Intelligence in Higher Education: Why the ‘Banning Approach’ to Student Use Is Sometimes Morally Justified,” Philosophy & Technology 37, no. 3 (2024): 113, https://doi.org/10.1007/s13347-024-00799-9; Sara Migliorini and João Ilhão Moreira, “The Case for Nurturing AI Literacy in Law Schools,” Asian Journal of Legal Education 12, no. 1 (2025): 7–24.

34 Paul Fyfe, “How to Cheat on Your Final Paper: Assigning AI for Student Writing,” AI & Society 38, no. 4 (2023): 1395–405, https://doi.org/10.1007/s00146-023-01611-3.

35 Changwu Huang, Zeqi Zhang, Bifei Mao, and Xin Yao, “An Overview of Artificial Intelligence Ethics,” IEEE Transactions on Artificial Intelligence 4, no. 4 (2022): 799–819, https://doi.org/10.1109/TAI.2022.3186393.

36 Ryan Thomas Williams, “The Ethical Implications of Using Generative Chatbots in Higher Education,” Frontiers in Education 8 (2024): 1331607, https://doi.org/10.3389/feduc.2023.1331607.

37 Nikolas Dietis, “Three Ways to Use ChatGPT to Enhance Students’ Critical Thinking in the Classroom,” Times Higher Education Campus, Jan. 8, 2024, https://www.timeshighereducation.com/campus/three-ways-use-chatgpt-enhance-students-critical-thinking-classroom.

38 Paul Atchley, Hannah Pannell, Kaelyn Wofford, Michael Hopkins, and Ruth Ann Atchley, “Human and AI Collaboration in the Higher Education Environment: Opportunities and Concerns,” Cognitive Research: Principles and Implications 9, no. 1 (2024): 20, https://doi.org/10.1186/s41235-024-00486-3.

39 William J. Connell and M. Hamlin-Black, “Artificial Intelligence and Legal Education,” The Computer & Internet Lawyer 36, no. 5 (May 2019): 1.

40 Michael Simon, Alvin F. Lindsay, Loly Sosa, and Paige Comparato, “Lola v. Skadden and the Automation of the Legal Profession,” Yale Journal of Law & Technology 20 (2018): 234.

41 Raj Shamani, “3 Reasons Why AI Can Never Replace Humans,” Medium, Sep. 5, 2023, https://rajshamani.medium.com/3-reasons-why-ai-can-never-replace-humans-e5bc9a654ef7; American Bar Association, Center for Professional Responsibility, Model Rules of Professional Conduct (American Bar Association, 2006); Kalliopi Michalakopoulou, Alexandros Nikitas, Eric Tchouamou Njoya, and Jill Johnes, “Innovation in the Legal Service Industry: Examining the Roles of Human and Social Capital, and Knowledge and Technology Transfer,” International Journal of Entrepreneurship and Innovation 25, no. 4 (2024): 248–62; Simon, Lindsay, Sosa, and Comparato, “Lola v. Skadden and the Automation of the Legal Profession.”

42 Ashok K. Goel and David A. Joyner, “Using AI to Teach AI: Lessons from an Online AI Class,” AI Magazine 38, no. 2 (2017): 48–45, https://doi.org/10.1609/aimag.v38i2.2733.

43 Aswathy Prakash G. and Vishnu Nair, “Integrating Generative AI into Legal Education: From Casebooks to Code, Opportunities and Challenges,” Law, Technology & Humans 6 (2024): 60.

44 Julianne Hill, “Profs Trade Notes as Law Schools Write Generative AI Policies,” ABA Journal (Jan. 2, 2024), https://www.abajournal.com/web/article/law-profs-trade-notes-aslaw-school-write-generative-ai-policies.