AI for Lawyers: Obstacles and Opportunities Course

Editorial Take

The University of Michigan’s 'AI for Lawyers: Obstacles and Opportunities' is a timely, well-structured entry point for legal professionals seeking to understand artificial intelligence’s growing influence on law. Rather than diving into code or algorithms, the course focuses on practical, ethical, and strategic dimensions relevant to practicing attorneys, legal advocates, and law students.

Standout Strengths

• Real-World Relevance: The course grounds AI concepts in actual legal workflows, such as e-discovery, contract review, and legal research. This makes abstract technology feel tangible and immediately applicable to daily practice.
• Non-Technical Accessibility: Designed for lawyers without computer science backgrounds, it explains AI in plain language. Complex ideas like machine learning and natural language processing are broken down using relatable legal analogies.
• Ethical Foresight: The curriculum dedicates significant attention to confidentiality, bias, and professional responsibility. These modules help lawyers anticipate compliance risks before adopting AI tools.
• Strategic Perspective: Instead of promoting blind adoption, the course encourages critical evaluation. Learners are taught to assess AI tools based on accuracy, transparency, and alignment with client needs.
• Curriculum Design: As the first in a four-part series, this course sets a strong foundation. Its modular structure allows learners to build knowledge progressively across future courses on implementation and leadership.
• Institutional Credibility: Offered by the University of Michigan, a top-tier law school, the content carries academic rigor and practical insight. This enhances trust and perceived value among legal professionals.

Honest Limitations

• Limited Technical Depth: The course avoids coding, APIs, or data modeling. While appropriate for non-technical audiences, some learners may wish for basic tool demonstrations or sandbox environments.
• No Hands-On Projects: There are no interactive exercises with AI platforms like Casetext, Harvey, or Ross Intelligence. This reduces experiential learning despite strong theoretical framing.
• U.S.-Centric Focus: Legal ethics and regulations discussed are primarily based on American standards. International lawyers may need to adapt concepts to their own jurisdictions.
• Abstract Examples: Case studies are often hypothetical rather than drawn from real litigation or firm deployments. More concrete examples could deepen engagement and retention.

How to Get the Most Out of It

• Study cadence: Allocate 3–4 hours weekly over eight weeks. Spread sessions across the week to allow reflection on complex ethical dilemmas introduced in videos.
• Parallel project: Apply concepts by auditing an AI tool used in your firm or evaluating one for potential adoption. Use course frameworks to assess risks and benefits.
• Note-taking: Focus on definitions, ethical red flags, and opportunity areas. Organize notes by practice area (e.g., litigation, corporate law) for future reference.
• Community: Engage in Coursera discussion forums. Share concerns about AI with peers, especially around confidentiality breaches or malpractice exposure.
• Practice: Revisit key scenarios—like AI hallucinating case law—and draft internal memos outlining safeguards your organization could implement.
• Consistency: Complete quizzes promptly after each module to reinforce learning. Delaying assessments may reduce retention due to conceptual density.

Supplementary Resources

• Book: 'The Future of the Professions' by Richard Susskind and Daniel Susskind offers complementary insights on AI’s impact across expert fields, including law.
• Tool: Explore free-tier legal AI platforms like Casetext or DoNotPay to observe capabilities firsthand while applying course ethics frameworks.
• Follow-up: Enroll in subsequent courses in the specialization to build implementation skills and leadership strategies for AI in legal environments.
• Reference: Review ABA Model Rules of Professional Conduct, especially Rule 1.1 (Competence), to contextualize AI responsibilities discussed in the course.

Common Pitfalls

• Pitfall: Assuming AI proficiency means technical mastery. This course shows that legal AI literacy is more about judgment, oversight, and ethics than programming skills.
• Pitfall: Overlooking bias in training data. Lawyers must understand that AI systems can perpetuate disparities, especially in sentencing or immigration tools.
• Pitfall: Ignoring supervision duties. The course emphasizes that delegating tasks to AI does not absolve lawyers of ultimate responsibility for accuracy and compliance.

Time & Money ROI

• Time: At 8 weeks with moderate weekly effort, the time investment is reasonable for busy professionals seeking foundational knowledge without burnout.
• Cost-to-value: Priced as part of Coursera’s subscription model, the course offers strong value given its niche focus and institutional credibility, though not free to audit.
• Certificate: The credential signals proactive engagement with legal innovation, which can enhance resumes and internal mobility within law firms.
• Alternative: Free webinars or articles exist, but none offer structured, academically backed learning with consistent assessment like this specialization starter.

Editorial Verdict

This course fills a critical gap in legal education by addressing AI in a way that is both approachable and intellectually rigorous. It doesn’t promise to turn lawyers into data scientists, but it successfully equips them to be informed, responsible, and strategic users of artificial intelligence. The emphasis on ethics, professional duty, and practical implications ensures that learners walk away not just with knowledge, but with judgment—a core legal competency in the age of automation.

We recommend this course to any legal professional who reviews contracts, conducts research, or advises clients in a digitally evolving landscape. While it lacks hands-on labs, its conceptual clarity and forward-looking perspective make it a standout starting point. Paired with real-world experimentation and follow-up learning, it can catalyze meaningful transformation in legal practice. For those beginning their AI journey, this is one of the most credible and well-designed entry points available online.