Survey Finds Asia-Pacific Lawyers Want AI-Centric Legal Education Reform

Legal Education Must Change Because of AI - Survey Signals Urgency

A major LexisNexis and ALITA survey across the Asia-Pacific legal market sends a clear message: legal education needs to evolve for AI. Only 15% of respondents want law schools to stay the same. A strong majority (74%) want curriculum reform that teaches AI in a legal context, not just generic usage.

Even with many students already using tools like ChatGPT, lawyers want targeted training on how AI applies to actual matters. In-house respondents are the strongest advocates, with 85% backing curriculum changes, compared to 70% of law firm lawyers.

What the Market Is Asking Law Schools to Do

• Integrate AI literacy into core courses: model limits, retrieval, evaluation, prompt strategy, confidentiality, and hallucination risk.
• Teach applied legal tech, ethics, and interdisciplinary work with computer science and data teams.
• Offer upskilling for current practitioners and co-develop training with firms and legal departments.

The survey also highlights demand for law schools to help firms build legal AI curricula. That implies capability gaps in some organizations and points to academia-industry partnerships as the lever for progress.

Why Law Schools Can't Do It Alone

Law schools excel at doctrine. AI proficiency is use-case driven. It depends on matter type, workflow, and risk profile. Teaching students how to apply different AI tools to, say, private equity fund formation, contract playbooks, or investigations requires practice-side experience.

Similarly, students need to understand how GenAI eDiscovery pipelines differ from traditional NLP systems. Most faculties won't have that expertise in-house. The solution is collaboration with innovation teams, vendors, and in-house groups that deploy these systems daily.

Some institutions are already partnering closely with practice and industry. For example, Stanford's CodeX has long connected research with real-world use cases. See CodeX.

A Practical Roadmap for Law Schools

• Core modules: AI fundamentals for lawyers, risk and ethics, confidentiality-by-design, prompt strategy, and result verification.
• Use-case studios by matter type: M&A due diligence, fund formation, employment audits, regulatory filings, litigation support, and eDiscovery.
• Clinics and sandboxes: work with real or synthetic datasets; co-teach with firm innovation leaders and product experts.
• Assessment: portfolios of applied AI work, peer review, and documented reasoning and audit trails.
• CPD/MCLE programs: flexible, practice-focused upskilling for associates and in-house teams.
• Governance: clear policies on disclosure, client consent, provider selection, data handling, and output validation.

What Firms and Legal Departments Can Do Now

• Second practitioners into co-taught classes and clinics; appoint adjuncts from innovation and KM teams.
• Share sanitized datasets and workflows for teaching; sponsor practical modules aligned to priority matters.
• Co-develop assessment rubrics that reflect client expectations and risk controls.

The demand signal is loud: more and better training on AI. Whether law schools or consortiums lead it, clients will reward teams who can apply AI safely, efficiently, and with measurable value.

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