AALS Partners With West Academic to Study AI Use in Law Schools and the Legal Profession
The Association of American Law Schools (AALS) is teaming up with West Academic to run a focused study on how AI is being used across legal education and practice. The aim is simple: give schools a clear path to prepare students for an AI-driven profession.
What the study covers
- Faculty confidence: Comfort with AI tools, training needs, and how teaching and assessment may change.
- Student trust and use: Where students rely on AI, what they avoid, and how they credit or disclose use.
- Administrator policy decisions: Honor codes, privacy, procurement, and classroom/clinic rules.
- Practitioner expectations: What firms and legal departments want new hires to know and do with AI.
Why this matters for legal education
Clients will expect faster research, tighter drafting, and clear judgment on when AI helps or hurts. Schools that set standards now will graduate lawyers who can work with AI without risking confidentiality, privilege, or accuracy.
This research can help align curricula, policies, and employer expectations. That reduces confusion for students and gives employers more confidence in entry-level talent.
Practical steps schools can take now
- Publish a plain-English policy on acceptable AI use for courses, clinics, journals, and competitions.
- Add disclosure rules for AI assistance and reinforce citation integrity.
- Embed AI exercises in Legal Research and Writing, Professional Responsibility, and Clinics.
- Set up a faculty development track for prompt practices, verification, and assessment design.
- Protect data: block tools that train on user input, use privacy-first options, and require local redaction for client matter text.
- Pilot secure research/drafting tools with small cohorts before scaling.
- Coordinate with career services so students can discuss AI skills and limits during interviews.
Key risks to manage
- Confidentiality and privilege: Prevent exposure of client or student data to public models.
- Accuracy and bias: Teach verification, red-teaming, and sourcing as default habits.
- Overreliance: Make students show their work and reasoning, not just results.
- Equity: Provide baseline access so students aren't split by tool availability.
Signals and metrics to track
- Faculty comfort levels and training completion rates.
- Student disclosure rates and common use cases.
- Policy compliance incidents and remediation outcomes.
- Externship and employer feedback on graduate readiness.
- Time-to-completion for research and drafting tasks with quality checks.
What legal employers can do
- Share hiring criteria and sample tasks that reflect actual workflows with AI.
- Offer clinics and courses anonymized matter sets for practice.
- Provide feedback loops on graduate performance to refine curricula.
- Align firm policies with school policies where possible to reduce mixed messages.
Quick actions for this semester
- Issue an AI addendum to the honor code and syllabi.
- Run a faculty workshop and a student briefing on safe, effective use.
- Pilot AI-assisted legal research in one LRW section with tight verification.
- Create a disclosure template students can attach to assignments.
- Open an intake form for employer input on entry-level AI expectations.
Resources
For context on policy and governance, see the AALS site and widely used risk frameworks:
Skill-building for legal teams
If you're formalizing training plans for faculty, staff, or students, curated AI course paths by job function can speed things up:
The AALS-West Academic study signals a practical shift: clear standards, measured adoption, and verifiable skills. Schools that move on this now will give graduates an edge-and give employers fewer reasons to hesitate.
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