Share and intensity of work current AI systems can materially affect.
SOC 25-1112
Law Teachers, Postsecondary AI displacement risk
Law professors teach a profession AI is actively disrupting — which makes their subject matter more relevant, not less. Research drafting assists scholarship; Socratic classroom discussion, mentorship, and evaluation of legal reasoning stay human.
Likely potential for exposed tasks to move to software after workflow integration.
AI legal research tools change what students need to learn, pushing faculty toward teaching judgment about AI-assisted work rather than memo production. The classroom and scholarly accountability remain human-led.
Distribution
Where Law Teachers, Postsecondary sits across 620 tracked roles
Displacement pressure 30 — higher than 48% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
23 O*NET task statements matched to SOC 25-1112. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $128,500 (May 2025, US national). The latest BLS row matched SOC 25-1112.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Law Teachers, Postsecondary
SOC 25-1112 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 30/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-11.5% group wage
-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Law Teachers, Postsecondary
The current evidence import matched 23 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks23
SOC25-1112
- Core task / ID 6218
Initiate, facilitate, and moderate classroom discussions.
- Core task / ID 6219
Prepare course materials, such as syllabi, homework assignments, and handouts.
- Core task / ID 6216
Compile, administer, and grade examinations, or assign this work to others.
- Core task / ID 6215
Evaluate and grade students' class work, assignments, papers, and oral presentations.
- Core task / ID 6223
Conduct research in a particular field of knowledge and publish findings in professional journals, books, or electronic media.
- Core task / ID 6220
Keep abreast of developments in the field by reading current literature, talking with colleagues, and participating in professional conferences.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Deliver lectures on legal topics
Exposure 52, automation 22%, augmentation 70%.
O*NET evidence: Prepare and deliver lectures to undergraduate or graduate students on topics such as ci... (ID 6217)
Facilitate classroom discussion
Exposure 18, automation 4%, augmentation 36%.
O*NET evidence: Initiate, facilitate, and moderate classroom discussions. (ID 6218)
Evaluate papers and oral presentations
Exposure 48, automation 24%, augmentation 62%.
O*NET evidence: Evaluate and grade students' class work, assignments, papers, and oral presentations. (ID 6215)
Conduct and publish legal research
Exposure 58, automation 28%, augmentation 74%.
O*NET evidence: Conduct research in a particular field of knowledge and publish findings in professiona... (ID 6223)
TaskExposureAutomationAugmentation
Deliver lectures on legal topics5222%70%
Facilitate classroom discussion184%36%
Evaluate papers and oral presentations4824%62%
Conduct and publish legal research5828%74%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
ModerateLegal Technology Program Lead
Training horizon: 3-8 months. Skill overlap 64. Wage preservation signal 104.
- Design AI-in-law curriculum
- Evaluate legal research tools
- Build practitioner partnerships
credentialed transition
ModerateDean of Academic Affairs
Training horizon: 12-24 months. Skill overlap 62. Wage preservation signal 122.
- Lead curriculum reform
- Manage faculty governance
- Own bar passage outcomes
Comparison guides
Compare the next move before you commit
3-8 months
Law Teachers, Postsecondary to Legal Technology Program Lead
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Law Teachers, Postsecondary into Legal Technology Program Lead.
12-24 months
Law Teachers, Postsecondary to Dean of Academic Affairs
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Law Teachers, Postsecondary into Dean of Academic Affairs.
What the AI risk score means for Law Teachers, Postsecondary
The displacement pressure score for Law Teachers, Postsecondary is 30. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Conduct and publish legal research carries 28% automation pressure, while Conduct and publish legal research carries 74% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $128,500 (May 2025, US national). Employment context: Legal academia role watching AI transform its own subject. Typical education: Law degree plus scholarly record.
Wage vulnerability is 30, while transition feasibility is 66. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Moderate displacement pressure
- AI raises demand for judgment-focused teaching
- Scholarship and mentorship persist
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Law Teachers, Postsecondary, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Legal scholarship
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Socratic instruction
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Research
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Student assessment
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Legal Technology Program Lead, such as design ai-in-law curriculum.
- By 90 days, compare internal openings and external postings for Legal Technology Program Lead or Dean of Academic Affairs and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Law Teachers, Postsecondary
Will AI replace Law Teachers, Postsecondary?
Law professors teach a profession AI is actively disrupting — which makes their subject matter more relevant, not less. Research drafting assists scholarship; Socratic classroom discussion, mentorship, and evaluation of legal reasoning stay human. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Law Teachers, Postsecondary work are most exposed to AI?
Conduct and publish legal research and Evaluate papers and oral presentations show the strongest automation pressure in this model. Conduct and publish legal research and Deliver lectures on legal topics are better treated as AI-augmented work.
What should Law Teachers, Postsecondary learn next?
Start with Legal scholarship, Socratic instruction, Research. The most practical adjacent paths in this model are Legal Technology Program Lead and Dean of Academic Affairs.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureOECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics