Share and intensity of work current AI systems can materially affect.
SOC 25-1054
Physics Teachers, Postsecondary AI displacement risk
Physics faculty teach quantum mechanics and optics while AI solves textbook problems and drafts lab reports. Laboratory supervision exposes what students actually understand, and research at the frontier, where training data runs out, keeps the discipline's human core intact.
Likely potential for exposed tasks to move to software after workflow integration.
AI is strongest on solved physics and weakest on the unsolved kind, which is exactly where faculty research lives. Assessment is shifting toward in-person derivation and lab verification, a redesign faculty lead rather than suffer.
Distribution
Where Physics Teachers, Postsecondary sits across 620 tracked roles
Displacement pressure 28 — higher than 43% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
24 O*NET task statements matched to SOC 25-1054. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $100,310 (May 2025, US national). The latest BLS row matched SOC 25-1054.
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 Physics Teachers, Postsecondary
SOC 25-1054 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 28/100 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 Physics Teachers, Postsecondary
The current evidence import matched 24 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 tasks24
SOC25-1054
- Core task / ID 5915
Evaluate and grade students' class work, laboratory work, assignments, and papers.
- Core task / ID 5920
Prepare course materials, such as syllabi, homework assignments, and handouts.
- Core task / ID 5917
Compile, administer, and grade examinations, or assign this work to others.
- Core task / ID 5916
Prepare and deliver lectures to undergraduate or graduate students on topics such as quantum mechanics, particle physics, and optics.
- Core task / ID 5921
Maintain regularly scheduled office hours to advise and assist students.
- Core task / ID 5922
Supervise undergraduate or graduate teaching, internship, and research work.
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
Prepare and deliver physics lectures
Exposure 54, automation 27%, augmentation 70%.
O*NET evidence: Prepare and deliver lectures to undergraduate or graduate students on topics such as qu... (ID 5916)
Supervise student laboratory work
Exposure 24, automation 8%, augmentation 50%.
O*NET evidence: Supervise students' laboratory work. (ID 5919)
Compile, administer, and grade examinations
Exposure 58, automation 34%, augmentation 62%.
O*NET evidence: Compile, administer, and grade examinations, or assign this work to others. (ID 5917)
Supervise graduate research work
Exposure 22, automation 7%, augmentation 48%.
O*NET evidence: Supervise undergraduate or graduate teaching, internship, and research work. (ID 5922)
TaskExposureAutomationAugmentation
Prepare and deliver physics lectures5427%70%
Supervise student laboratory work248%50%
Compile, administer, and grade examinations5834%62%
Supervise graduate research work227%48%
Transition pathways
Adjacent moves that preserve existing skills
credentialed transition
LowDepartment Chair
Training horizon: 12-24 months. Skill overlap 64. Wage preservation signal 116.
- Lead curriculum redesign
- Manage research portfolios
- Own program outcomes
role redesign
LowComputational Physics Lead
Training horizon: 6-12 months. Skill overlap 68. Wage preservation signal 108.
- Own simulation curricula
- Audit AI problem-solving tools
- Lead research computing
Comparison guides
Compare the next move before you commit
12-24 months
Physics Teachers, Postsecondary to Department Chair
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Physics Teachers, Postsecondary into Department Chair.
6-12 months
Physics Teachers, Postsecondary to Computational Physics Lead
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Physics Teachers, Postsecondary into Computational Physics Lead.
What the AI risk score means for Physics Teachers, Postsecondary
The displacement pressure score for Physics Teachers, Postsecondary is 28. 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. Compile, administer, and grade examinations carries 34% automation pressure, while Prepare and deliver physics lectures carries 70% 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: $100,310 (May 2025, US national). Employment context: Faculty role where labs grade the AI. Typical education: Doctoral degree typical.
Wage vulnerability is 30, while transition feasibility is 71. 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
- Textbook problems are automated
- Frontier research stays human
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Physics 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.
Physics 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.
Laboratory supervision
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.
Research mentoring
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 redesign.
- By 90 days, compare internal openings and external postings for Department Chair or Computational Physics Lead and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Physics Teachers, Postsecondary
Will AI replace Physics Teachers, Postsecondary?
Physics faculty teach quantum mechanics and optics while AI solves textbook problems and drafts lab reports. Laboratory supervision exposes what students actually understand, and research at the frontier, where training data runs out, keeps the discipline's human core intact. 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 Physics Teachers, Postsecondary work are most exposed to AI?
Compile, administer, and grade examinations and Prepare and deliver physics lectures show the strongest automation pressure in this model. Prepare and deliver physics lectures and Compile, administer, and grade examinations are better treated as AI-augmented work.
What should Physics Teachers, Postsecondary learn next?
Start with Physics instruction, Laboratory supervision, Assessment design. The most practical adjacent paths in this model are Department Chair and Computational Physics Lead.
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