Stillman Teaching Workshop Prep | 02/14/25

Introduction

This document was developed as an initial framework to guide my participation at the Stillman Teaching Workshop on AI Use. It is a starting point for engaging with the opportunities and challenges presented by generative AI in education, particularly in the domain of legal studies.

As this is a very preliminary version, further refinements will be necessary to enhance clarity and align with pedagogical objectives and practical applicability. Additional input from various stakeholders and additional iterative feedback will be essential in shaping this into a finalized resource. Suggestions and revisions are welcome as I continue to refine this approach.

JHS

Agenda

Dean Strawser:

“To maximize the value of our time together, I’d like to ask you to try to identify, in advance, a discipline-specific application for one of the approaches outlined in the Mollick and Mollick article (e.g., mentor, tutor, coach) from the introductory course in your discipline, with the aim of creating an appropriate Gen AI prompt. For example (and these are merely suggestions):

– Economics – create a prompt that helps students understand the concept of price elasticity of demand

– Financial Accounting – create a prompt that reinforces the preparation of a basic income statement (or one or more elements thereof)

– Management – create a prompt introduces the elements of a SWOT analysis and the role it plays in strategic decision making

In the hands-on portion of the workshop, we’ll break into groups by discipline and develop a workable Gen AI prompt.”

“Assigning AI: Seven Approaches for Students”

Prompt

“Please review, summarize, and create discussion points for the attached document.”

Summary

The paper by Dr. Ethan Mollick and Dr. Lilach Mollick examines the role of Large Language Models (LLMs) in education, presenting them as transformative tools with great potential and inherent risks. While AI can enhance learning experiences by providing personalized feedback, fostering collaboration, and simulating learning environments, the authors stress the importance of critical oversight. AI should serve as a complement to human learning rather than a replacement.

The framework introduces seven distinct approaches to integrating AI into student learning. Each approach is designed to encourage active engagement with AI and help students develop critical thinking, problem-solving, and collaboration skills. The authors emphasize that students must take responsibility for their work and actively evaluate AI-generated outputs to avoid errors, biases, and misinformation.

The seven AI-assisted learning approaches are:

AI as Mentor: AI provides structured and ongoing feedback to students, helping them refine their work. Effective feedback is critical for learning, but educators often struggle to provide frequent, detailed guidance in large classrooms. AI can help fill this gap by offering immediate feedback on students’ work. However, students must learn to evaluate AI’s feedback critically, as AI-generated suggestions may contain inaccuracies or lack contextual understanding.
AI as Tutor: AI provides direct instruction, helping students learn concepts through interactive questioning and explanations. Tutoring is one of the most effective forms of learning, as it allows for personalized instruction tailored to the student’s level and prior knowledge. However, AI tutors present risks, particularly regarding confabulation (AI generating false or misleading information). Students must be trained to verify AI-generated explanations against reliable sources.
AI as Coach: AI facilitates metacognitive exercises, prompting students to reflect on their learning experiences, challenges, and strategies for improvement. These can be structured reflection prompts or pre-mortem exercises that help students anticipate potential failures and plan accordingly. AI-driven coaching can enhance self-awareness and self-regulation, but there is a risk that students may become overly reliant on AI-generated insights rather than developing independent reflective skills.
AI as Teammate: AI assists in collaborative intelligence by acting as a virtual team member. It can help students recognize and leverage each team member’s skills, provide alternative viewpoints, and challenge group assumptions (e.g., playing the role of a “devil’s advocate”). AI can support teamwork by enhancing decision-making and problem-solving processes. However, students may defer too much to AI or struggle with its unpredictable personality-like responses, requiring careful instructor guidance.
AI as Student: AI becomes a tool for reinforcing learning by allowing students to “teach” it. Students explain concepts to the AI and evaluate its responses, identifying inaccuracies and missing elements. This approach is based on research showing that teaching others strengthens understanding and knowledge retention. The challenge is ensuring that students engage critically with AI responses rather than accepting them at face value.
AI as Simulator: AI generates simulated learning environments, allowing students to practice skills and test their knowledge in realistic scenarios. This is particularly useful in disciplines that benefit from experiential learning, such as law, business, or medicine. Simulations can help students apply theoretical concepts in practical settings, but ensuring that the AI-generated scenarios are sufficiently accurate and pedagogically useful requires instructor oversight.
AI as Tool: AI enhances student productivity by assisting with tasks such as writing, brainstorming, research, and organization. While AI can be a powerful tool for efficiency, there is a significant risk of students outsourcing cognitive effort rather than using AI to augment their thinking. Educators must ensure that AI supports student learning rather than replacing essential critical thinking and problem-solving processes.

The authors stress that AI integration in education is still in its early stages and requires continuous refinement. Educators must balance learning opportunities with potential risks, ensuring that students remain actively engaged in their learning process. Transparency, ethical considerations, and explicit guidelines for AI use are crucial to successful implementation.

Discussion Questions

AI’s Role in Education: Enhancing or Undermining Learning?

- How can AI best complement traditional teaching methods without replacing essential cognitive skills?
- Should AI be used primarily as a support tool or deeply embedded into the curriculum?
- What are the risks of students becoming overly dependent on AI, particularly in critical thinking, creativity, and problem-solving?

Critical Thinking and Oversight: Keeping Students in Control

- How can educators train students to critically assess AI-generated responses rather than accept them at face value?
- What strategies can prevent students from becoming complacent about AI-generated feedback?
- Should students be required to fact-check AI responses as part of their assignments? If so, what methods would be most effective?

AI as a Learning Partner: Understanding Different Roles

- How do AI’s mentor, tutor, and coach roles differ regarding student engagement and learning outcomes?
- What are the key challenges in using AI as a teammate or collaborator in group projects?
- What role does human oversight play in ensuring AI is used effectively in these different roles?

Bias and Ethical Considerations in AI-Assisted Learning

- What biases are embedded in AI-generated responses, and how can students and educators recognize them?
- Should institutional policies on AI usage in assignments be similar to plagiarism policies?
- How can AI developers and educators work together to create AI tools that align with ethical standards and inclusive education practices?

Collaboration and Teamwork: AI as a “Teammate”

- How can AI assist in team decision-making without overpowering human contributions?
- What are the risks of students unquestioningly accepting AI-generated suggestions in group work?
- Should students be required to explain why they chose to follow or reject AI’s input in collaborative assignments?

The Power of Teaching AI: Using AI as a Learning Reflection Tool

- How does having students teach AI reinforce their understanding of a topic?
- What cognitive benefits arise from engaging in elaborative interrogation with AI?
- How can educators ensure students actively evaluate AI responses rather than correcting surface-level mistakes?

Simulated Learning Experiences: Expanding Experiential Education with AI

- What disciplines or subjects benefit most from AI-generated simulated environments?
- How can AI-driven simulations be used to bridge the gap between theory and practice?
- What are the limitations of AI-generated practice environments, particularly in fields requiring emotional intelligence, ethical reasoning, or human interaction?

Transparency and Accountability in AI Use

- Should students be required to disclose AI usage in assignments? If so, how should this be documented?
- How can educators evaluate effective vs. ineffective AI use in student learning?
- What consequences should exist for students who misuse AI tools, such as submitting AI-generated work without proper engagement?

Future Implications: AI’s Long-Term Role in Education

- How might the evolution of AI tools change the educational landscape over the next decade?
- What role will AI literacy play in preparing students for careers in an increasingly AI-driven workforce?
- Should AI be treated as a temporary tool for experimental learning, or should institutions begin formalizing AI integration into teaching and assessment models?

This expanded summary and discussion guide provides a comprehensive framework for integrating AI into education while maintaining students’ academic integrity and active learning engagement. These insights can be used to develop curriculum policies, faculty training programs, and student engagement strategies in higher education.

Mollicks’ Seven AI-Assisted Learning Approaches for a Legal Environment of Business Course

Prompt

“You are an expert in undergraduate teaching and learning, the subject matter included in a legal environment of business course, the development of GenAI prompts, and the content and application of the above article, ‘Assigning AI – Seven Approaches for Students’ by Mollick and Mollick. Please identify the strengths and weaknesses of each of the seven AI-assisted learning approaches when used in a legal environment of business course. Then, rank the appropriateness of each approach for use in a legal environment of business course.”

Analysis

1. AI as Mentor (Rank: #1 – Highly Appropriate)

Strengths:

Frequent, individualized feedback: AI can provide immediate, structured feedback on law-related assignments, such as case briefs and contract analyses.
Improves legal writing: AI can assess clarity, logical reasoning, and structure in legal arguments, guiding students to refine their analytical writing.
Enhances critical thinking: Encouraging students to evaluate AI’s feedback critically reinforces their ability to assess legal arguments and case analysis.

Weaknesses:

AI lacks complete legal comprehension: AI may misinterpret legal principles or apply them inaccurately due to confabulations.
Students may overrely on AI. Students might uncritically accept AI feedback without proper guidance rather than develop independent legal reasoning.

Appropriateness Ranking: #1 – Since LEB students benefit from iterative legal writing feedback and structured critiques, AI as a mentor aligns well with course objectives.

2. AI as Tutor (Rank: #2 – Highly Appropriate)

Strengths:

Enhances conceptual understanding: AI can interactively and adaptively explain complex legal principles (e.g., tort liability, contract formation, and administrative law).
Personalized instruction: Students at different levels of legal comprehension can receive customized explanations.
Supports exam preparation: AI-generated hypotheticals and practice problems can reinforce legal reasoning skills.

Weaknesses:

Risk of inaccurate explanations: AI may generate misleading or incorrect interpretations of legal concepts.
Limited adaptability to case-specific nuances: While AI can generalize about legal topics, it may struggle with the case-law-based nature of LEB coursework.

Appropriateness Ranking: #2 – Highly effective for reinforcing foundational legal principles and assisting students with individualized learning, but must be supplemented with instructor oversight.

3. AI as a Simulator (Rank: #3 – Highly Appropriate)

Strengths:

- Engages students in applied learning: AI-generated legal simulations can immerse students in hypothetical contract disputes, tort cases, or regulatory compliance issues.
- Prepares students for real-world applications: AI-generated role-play exercises (e.g., acting as legal counsel in a negotiation) help students apply legal principles in practice.
- Enhances understanding of legal processes: AI can simulate courtroom arguments, business disputes, or arbitration settings.

Weaknesses:

- AI-generated scenarios may lack legal precision: Legal simulations require instructor validation to ensure accuracy.
- Inconsistent fidelity: AI may oversimplify complex legal doctrines or misrepresent procedural elements of the legal system.

Appropriateness Ranking: #3 – Highly effective for experiential learning and legal applications, but needs careful fact-checking by instructors.

4. AI as Teammate (Rank: #4 – Moderately Appropriate)

Strengths:

- Enhances group collaboration: AI can help student teams analyze case law, generate legal arguments, and critique each other’s reasoning.
- Acts as a “devil’s advocate”: AI can present counterarguments, helping students anticipate opposing legal positions.
- Supports structured legal analysis: AI can outline arguments in a persuasive format, modeling effective legal writing.

Weaknesses:

- Risk of AI-generated bias: AI may reinforce legal misinterpretations if not actively challenged.
- Students may disengage from collaboration: If AI is relied on too heavily, students may defer too much to its outputs rather than developing their legal reasoning.

Appropriateness Ranking: #4 – Useful as a supplementary tool for team-based projects, but requires active human oversight to ensure accuracy.

5. AI as Student (Rank: #5 – Moderately Appropriate)

Strengths:

- Helps students refine legal knowledge: Teaching AI about legal doctrines forces students to articulate concepts clearly.
- Encourages higher-order thinking: By evaluating AI-generated legal responses, students engage in a more profound critique of legal reasoning.
- This reinforces retention of legal concepts: Students must structure and explain legal principles in a way AI can “understand.”

Weaknesses:

- AI lacks proper comprehension: While AI can generate legal analyses, it cannot truly engage in legal reasoning like a student or legal professional.
- May encourage misinformation: Students who do not critically assess AI responses may internalize flawed interpretations of the law.

Appropriateness Ranking: #5 – Useful for reinforcing understanding through AI critique exercises but less essential than AI as a mentor, tutor, or simulator.

6. AI as Coach (Rank: #6 – Somewhat Appropriate)

Strengths:

Promotes legal metacognition: AI can help students reflect on their approach to legal analysis, argument development, and case interpretation.
Encourages iterative thinking: AI can guide students through revising legal arguments and anticipating challenges in legal debates.
Assists in test-taking strategy: AI can help students self-assess strengths and weaknesses in legal knowledge.

Weaknesses:

Limited contextual intelligence: AI lacks the depth of a human mentor to guide nuanced legal reasoning.
Coaching style may be inconsistent: AI-generated coaching may not align well with individual learning preferences or legal pedagogy.

Appropriateness Ranking: #6 – Useful for self-reflection exercises, but less directly impactful than AI’s role as a mentor, tutor, or simulator in legal studies.

7. AI as Tool (Rank: #7 – Least Appropriate)

Strengths:

Increases efficiency: AI can assist with legal research, case law summaries, and contract drafting.
Helps organize legal writing: AI can generate structured outlines for case briefs or legal memos.
Supports citation formatting: AI can assist with correct Bluebook or APA citation styles.

Weaknesses:

Risk of over-reliance: Students may let AI complete work rather than develop legal reasoning skills.
AI-generated content may be inaccurate: Legal research should always be verified with primary sources.
Weakens deep learning: While AI excels at task completion, it does not engage students in legal reasoning.

Appropriateness Ranking: #7 – Useful for administrative/legal research tasks but should not replace student engagement in legal analysis.

AI-enhanced Learning Plan for a Legal Environment of Business Course

Prompt

“Please develop a plan for using the following prompts in a legal environment of business course, persona, audience persona, chain of thought, and question refinement prompts. Please use the following subject areas: constitutional law, regulatory environment, courts, contract law, business associations, intellectual property, antitrust, employment law, and employment discrimination.”

Objective

Leverage AI-assisted learning techniques—persona, audience persona, chain of thought, and question refinement—to deepen student engagement, critical thinking, and application of legal concepts.

Methodology

Each AI-enhanced approach will be applied to key subject areas: constitutional law, regulatory environment, courts, contract law, business associations, intellectual property, antitrust, employment law, and employment discrimination. AI will be used in multiple roles (e.g., mentor, tutor, coach, teammate, student, and simulator) to facilitate different aspects of learning.

Constitutional Law

– AI Approach: Persona + Chain of Thought

– Activity: Students assume the role of a Supreme Court Justice evaluating a constitutional dispute.

– Prompt: “You are a Supreme Court Justice preparing a majority opinion on a First Amendment case. Analyze the legal arguments using constitutional principles, relevant precedents, and opposing viewpoints. Use a chain of thought approach to break down your reasoning step by step.”

Refinement: Introduce a dissenting opinion scenario in which students must revise their reasoning in response to a counterargument. Require students to submit both the original reasoning and the refined response.

– Outcome: Encourages deep legal reasoning, structured decision-making, and the ability to engage in legal rebuttal.

Regulatory Environment

– AI Approach: Audience Persona + Question Refinement

– Activity: Students craft policy recommendations for different stakeholders.

– Prompt: “You are an advisor drafting a regulatory impact report on environmental compliance laws. Tailor your recommendations to two different audiences: a corporate CEO and a consumer advocacy group. Refine your questions to anticipate their concerns and objections.”

Refinement: After receiving AI-generated counterarguments, students must revise their regulatory proposal. Have them compare AI-generated concerns with real-world examples from case law or current policy debates.

– Outcome: Develops adaptability in legal writing, strategic communication, and engagement with regulatory policy complexities.

Courts and Alternative Dispute Resolution

– AI Approach: Chain of Thought + Question Refinement

– Activity: Students explore the logic behind judicial decision-making.

– Prompt: “You are a law clerk summarizing a court’s reasoning in an arbitration case. Break down the ruling using a chain of thought approach, identifying key legal principles. Before finalizing your summary, refine your questions to ensure clarity and completeness.”

– Refinement: Have students compare two different court rulings on arbitration and refine their analysis to account for differences in legal reasoning. A final reflection is required on how courts interpret contract terms differently across jurisdictions.

– Outcome: Enhances legal analysis, synthesis of court rulings, and comparative judicial interpretation.

Contract Law

– AI Approach: Persona + Audience Persona

– Activity: Contract negotiation simulations.

– Prompt: “You are a corporate attorney negotiating a contract for a startup seeking venture capital. Identify and explain key contract clauses to the CEO (a non-lawyer) and an opposing counsel (a contract law expert). Adjust your language accordingly.”

– Refinement: Require students to generate multiple versions of the contract language: one that is legally precise and another that is simplified for non-lawyers. Have them compare their drafts to real-world contracts and assess clarity versus legal risk.

– Outcome: Develop practical contract negotiation skills, audience-specific communication, and precision in legal drafting.

Business Associations

– AI Approach: Chain of Thought + Persona

– Activity: Corporate structure analysis.

– Prompt: “You are a legal consultant advising an entrepreneur on choosing between an LLC and a C-Corporation. Use a chain of thought approach to analyze legal, financial, and operational implications before providing a recommendation.”

Refinement: Students should simulate a client meeting to justify their recommendation based on AI-generated counterarguments. They should also incorporate ethical considerations, such as fiduciary duties and corporate social responsibility, into their analysis.

– Outcome: Strengthens students’ ability to compare legal structures, justify business decisions, and engage with ethical business practices.

Intellectual Property Law

– AI Approach: Audience Persona + Question Refinement

– Activity: IP enforcement strategy.

– Prompt: “You are an IP attorney explaining patent infringement risks to a startup founder. First, identify key concerns. Then, refine your questions to guide the founder toward understanding their legal exposure and available protections.”

– Refinement: Have students generate a cease-and-desist letter and a licensing agreement as alternative enforcement strategies. A comparative analysis of the risks and benefits of litigation versus negotiation is required.

– Outcome: Improves skills in legal risk assessment, client counseling, and strategic decision-making in IP law.

Antitrust Law

– AI Approach: Persona + Chain of Thought

– Activity: Antitrust case analysis.

– Prompt: “You are an antitrust investigator analyzing a potential price-fixing case. Walk through your investigative steps using a chain of thought approach, applying the Sherman Act and relevant precedents.”

Refinement: Have students compare AI-generated analysis with actual DOJ or FTC antitrust cases. Then, have them refine their reasoning based on actual enforcement actions and legal challenges.

– Outcome: Develop analytical thinking, regulatory application, and real-world legal comparison skills.

Employment Law

– AI Approach: Audience Persona + Question Refinement

– Activity: Compliance training for HR professionals.

– Prompt: “You are a legal advisor creating a training session on employment laws for HR managers. Tailor your content to their level of expertise and refine your questions to ensure they grasp compliance requirements effectively.”

– Refinement: Require students to generate an AI-assisted FAQ for HR managers based on common workplace legal issues. Have them refine AI-generated responses to ensure accuracy and alignment with current labor laws.

– Outcome: Enhances legal education for practical workplace application, compliance training, and policy interpretation.

Employment Discrimination

– AI Approach: Persona + Chain of Thought

– Activity: Title VII discrimination case analysis.

– Prompt: “You are a legal analyst preparing an amicus brief on a workplace discrimination case under Title VII. Use a chain of thought approach to evaluate legal arguments, potential defenses, and the broader implications of the ruling.”

Refinement: Students should generate both a plaintiff-side and defense-side brief using AI. They should then critique AI-generated legal arguments for bias, gaps, or misinterpretations of precedent.

– Outcome: Strengthens legal writing, argumentation skills, and the ability to assess AI-generated legal reasoning critically.

Assessment & Reflection

Students will:

- Submit AI interaction reports summarizing how AI helped refine their legal analysis.
- Reflect on AI’s effectiveness in improving their understanding of legal concepts.
- Compare AI-generated responses to real-world legal materials and assess strengths and weaknesses.
- Discuss ethical considerations related to AI in legal research and practice, including risks of over-reliance on AI-generated legal reasoning.
- Include an AI Use Statement that meets the requirements of the Use of Artificial Intelligence and GenAI policy

Conclusion

This structured approach integrates AI-assisted learning with legal education, using persona-driven reasoning, audience-specific communication, logical problem-solving, and iterative question refinement. The refinements emphasize active student engagement, critical thinking, and legal accuracy, ensuring AI is a tool for enhancement rather than replacement. This approach strengthens legal knowledge and builds AI literacy, a crucial skill for future legal professionals.