Share and intensity of work current AI systems can materially affect.
SOC 25-2031
High School Teachers AI displacement risk
AI writes serviceable lesson plans, practice sets, and essay feedback, transforming preparation and grading for subject-specialist teachers. Deep content knowledge, AP and dual-enrollment rigor, college-prep mentorship, and classroom culture remain the human core.
Likely potential for exposed tasks to move to software after workflow integration.
Compared with earlier grades, high school teaching leans harder on content expertise and postsecondary guidance. AI cheating and AI grading pressure assessment design more than they threaten staffing, which is driven by licensure and subject shortages.
Distribution
Where High School Teachers sits across 620 tracked roles
Displacement pressure 24 — higher than 32% 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.
30 O*NET task statements matched to SOC 25-2031. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $72,040 (May 2025, US national). The latest BLS row matched SOC 25-2031.
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 High School Teachers
SOC 25-2031 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 24/100 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 High School Teachers
The current evidence import matched 30 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 tasks30
SOC25-2031
- Core task / ID 6650
Prepare students for later grades by encouraging them to explore learning opportunities and to persevere with challenging tasks.
- Core task / ID 6640
Instruct through lectures, discussions, and demonstrations in one or more subjects, such as English, mathematics, or social studies.
- Core task / ID 6639
Establish and enforce rules for behavior and procedures for maintaining order among students.
- Core task / ID 6643
Prepare materials and classrooms for class activities.
- Core task / ID 6644
Adapt teaching methods and instructional materials to meet students' varying needs and interests.
- Core task / ID 6642
Prepare, administer, and grade tests and assignments to evaluate students' progress.
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
Instruct students in specialized subjects
Exposure 32, automation 10%, augmentation 56%.
O*NET evidence: Instruct through lectures, discussions, and demonstrations in one or more subjects, suc... (ID 6640)
Prepare lessons and course materials
Exposure 68, automation 26%, augmentation 76%.
O*NET evidence: Establish clear objectives for all lessons, units, and projects, and communicate those ... (ID 6641)
Grade tests and assignments
Exposure 56, automation 30%, augmentation 62%.
O*NET evidence: Prepare, administer, and grade tests and assignments to evaluate students' progress. (ID 6642)
Guide students on academic and career paths
Exposure 22, automation 5%, augmentation 40%.
O*NET evidence: Guide and counsel students with adjustments, academic problems, or special academic int... (ID 6651)
TaskExposureAutomationAugmentation
Instruct students in specialized subjects3210%56%
Prepare lessons and course materials6826%76%
Grade tests and assignments5630%62%
Guide students on academic and career paths225%40%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
LowDepartment Chair
Training horizon: 2-5 months. Skill overlap 78. Wage preservation signal 106.
- Lead curriculum alignment
- Coach department teachers
- Set AI use policies for coursework
adjacent role
LowInstructional Coach
Training horizon: 3-8 months. Skill overlap 70. Wage preservation signal 102.
- Model effective instruction
- Analyze student data with teachers
- Lead professional development
Comparison guides
Compare the next move before you commit
2-5 months
High School Teachers to Department Chair
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from High School Teachers into Department Chair.
3-8 months
High School Teachers to Instructional Coach
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from High School Teachers into Instructional Coach.
What the AI risk score means for High School Teachers
The displacement pressure score for High School Teachers is 24. 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. Grade tests and assignments carries 30% automation pressure, while Prepare lessons and course materials carries 76% 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: $72,040 (May 2025, US national). Employment context: Large public-education workforce with subject-specialist shortages. Typical education: Bachelor's degree plus state licensure.
Wage vulnerability is 42, 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.
- Low displacement pressure
- Prep and grading augment heavily
- STEM and special-ed shortages persist
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For High School Teachers, 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.
Subject-matter depth
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.
Assessment design
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.
AI-aware 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.
College-prep advising
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 Department Chair, such as lead curriculum alignment.
- By 90 days, compare internal openings and external postings for Department Chair or Instructional Coach and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and High School Teachers
Will AI replace High School Teachers?
AI writes serviceable lesson plans, practice sets, and essay feedback, transforming preparation and grading for subject-specialist teachers. Deep content knowledge, AP and dual-enrollment rigor, college-prep mentorship, and classroom culture remain the human core. 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 High School Teachers work are most exposed to AI?
Grade tests and assignments and Prepare lessons and course materials show the strongest automation pressure in this model. Prepare lessons and course materials and Grade tests and assignments are better treated as AI-augmented work.
What should High School Teachers learn next?
Start with Subject-matter depth, Assessment design, AI-aware instruction. The most practical adjacent paths in this model are Department Chair and Instructional Coach.
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