How Can Law Professors Effectively Teach AI Literacy to Law Students? Legal AI Studio

By Dennis Kennedy

This spring at the Michigan State University College of Law and the MSU Center for Law, Technology & Innovation we introduced the “LegalRnD AI Studio,” a groundbreaking mini-course series designed to elevate law students' AI literacy, focusing on practical skills in generative AI. I want to share how you can replicate this successful model and provide your students with the essential AI literacy they need at your school.

Our students need practical AI skills, and they need them now.

The LegalRnD AI Studio was born out of a simple observation: our students need practical AI skills, and they need them now. The legal industry is rapidly evolving because of technology, and generative AI is at the forefront of this change. Yet, many law schools are still grappling with how to incorporate AI training into their curricula.

The LegalRnD AI Studio is more than just a series of lectures; it’s a hands-on, practical approach to learning AI with the goal of helping law students (and others) level up their AI literacy. By focusing on effective prompting techniques and aligning with current industry expectations, this mini-course prepares law students for the future of legal practice. The course’s blend of lectures and hands-on exercises ensures that students not only understand the theoretical aspects of AI but also gain the practical skills needed to excel in their careers.

Session 1: Prompting 101 for Law Students: Start with the Basics

The first session laid the foundation for understanding generative AI. As anyone who's worked with AI tools knows, the quality of your output is directly related to the quality of your input. Teaching law students how to craft effective prompts is a valuable skill that will serve them well in the future. Law students were introduced to AI capabilities and tools, learning the art of crafting effective prompts. This session was all about getting the basics right, ensuring students could generate useful responses from AI applications.

Key components included:

? Ethical and Responsible AI Use within University Policies and Guidelines: We started each session with a reminder about following university rules, policies, and guidelines, and keeping AI use ethical and responsible. We stressed the importance of data privacy and made it clear that students are responsible for what they input into third-party AI applications.

? Introduction to Generative AI: We explained the basics, including the role of large language models (LLMs), temperatures, comparing results from the same prompts, and the significance of tokens versus words.

? Crafting Effective Prompts: Students learned to create clear, specific prompts to guide AI outputs.

Session 2: Advanced Prompting for Law Students: Build on Foundation Skills

Building on the basics, the second session took students deeper into the realm of AI prompting. Advanced techniques like iterative refinement, chain of thought, RAG, personas, and prompt chaining were explored. This session aimed to equip students with the ability to handle more complex tasks using generative AI. The hands-on practice component was particularly valuable, allowing students to apply sophisticated structured prompting strategies and receive constructive feedback.

Highlights included:

? Advanced Prompt Examples: We provided students with structured prompts and hands-on practice to enhance their AI interaction skills and to achieve desired outputs.

? Enhancing Conversational AI Interactions: We demonstrated techniques for refining AI responses through iteration, personas, memory refreshing, and conversational styles.

? PCRO Approach: We used my PCRO model (Persona, Context, Request, Outcome) for crafting detailed and structured AI prompts and getting desired output.

Session 3: What Legal Employers Expect and Want from Law Students and Graduates in 2024

The final session bridged the gap between academic learning and professional expectations. Students gained insights into what legal employers are looking for in new hires, with a special emphasis on AI literacy. Practical exercises based on real-world scenarios ensured that students could align their learning path with industry needs. This session was a critical component, preparing students to meet employer expectations and stay competitive in the job market.

? Industry Insights: We discussed research findings on employer expectations regarding AI proficiency.

? Practical Exercises: We experimented with scenario-based activities that simulated real-world legal tasks.

? Future Steps: We shared strategies for continuing AI learning and staying competitive.

Comprehensive Handout

We provided a detailed handout that served as both a course guide and a future reference. This included everything from basic concepts to advanced prompting techniques and useful resources. You can download a free PDF of the handout here. Consider creating something similar for your students. I’m happy with you adapting a version of my handout so long as you include attribution to the Center and me.

Key Takeaways for Law Professors

1. Ethical Considerations: Emphasize the importance of ethical and responsible AI use, data privacy, and compliance with university policies.

2. Start Small: A mini-course or workshop series can be a great way to test the waters without overhauling your entire curriculum.

3. Interactive Learning: A blend of lectures and hands-on practice is essential. Students must see the practical application of theoretical concepts. This approach helps students understand theoretical concepts and see their practical applications.

4. Progressive Skill Building: We started with basic prompting techniques and gradually introduced more advanced concepts. This allowed students of all skill levels to engage and improve.

5. Focus on Prompting: Effective prompting is the cornerstone of generative AI success. Help students master basic and advanced prompting techniques.

6. Real-World Relevance: Align your course content with industry expectations and employer expectations. This prepares students for the demands of the job market and makes the learning experience more relevant. Engage students in practical exercises simulating legal tasks they might encounter in their careers.

7. Stay Flexible: Be prepared to adapt your course as AI technology and industry needs evolve and you learn your students’ interests and needs. I started all the sessions with a short segment I called “What did I try in AI” so students could share what they had experimented with and what they had found.

8. Continued Learning: Offer strategies for ongoing AI education and staying competitive in the job market.

9. Feedback and Practice: Regular feedback is vital. It helps students refine their skills and understand the nuances of effective AI use. Short “homework” assignments are also useful.

10. Collaborate: Reach out to legal tech companies, law firms, and other schools. Their insights can be invaluable in shaping your course.

Challenges to Consider

Ethical Considerations: We had to carefully navigate the ethical implications of AI in legal practice, which added another layer of complexity to the course, especially for students who have not already had a professional responsibility class.

Balancing Depth and Breadth: While it's tempting to try to cover everything, we found it more effective to focus on core skills that students can build upon. We wanted to provide valuable skills without overwhelming students who might be new to AI. Our solution was to start with the basics and progressively introduce more complex concepts, always tying them back to legal practice.

Time Constraint: Covering such a broad topic in just three sessions was challenging. We had to be very selective about what to include.

Keeping up with Rapid AI Advancements: What's cutting-edge today might be obsolete tomorrow. We needed to be prepared to continually update our course materials.

Why This Course Matters

By following this “AI Studio” approach, you can create a successful AI literacy course that equips your students with the skills they need to thrive in the modern legal landscape. I intentionally chose the word “studio” to reflect that using generative AI is both an art and a science and is learned best by working hands-on.

Short, focused courses like this can be an effective and helpful way to introduce generative AI right now without overhauling the entire curriculum.

Consider implementing a similar course at your institution to help your students stay ahead in a rapidly evolving field. Of course, a three-session mini-course isn't a complete solution to the tech skills gap in legal education and it won't turn anyone into an AI expert overnight. But it's an important step in the right direction. What it can do is provide a solid starting point and, perhaps more importantly, spark an interest in further learning. My hope is that more law schools will follow MSU's lead and start integrating practical AI training into their curricula.

The response from students has been overwhelmingly positive. They appreciate the practical skills they're gaining and feel more confident in navigating the world of AI. I also created a certificate of completion graphic for them to put on their LinkedIn profiles.

I can’t wait to teach more offerings of the LegalRnD AI Studio this academic year. I’ve also done versions for our law librarians and as a CLE for Michigan lawyers. I’m also considering training my research assistants to teach it.

For law students reading this: if you have the opportunity to take a course like this, jump on it. If your law school does not offer something like it, advocate for your law school to create something similar.

For law schools, it's a model worth considering. Short, focused courses like this can be an effective and helpful way to introduce generative AI right now without overhauling the entire curriculum.

Download PDF handout