Share and intensity of work current AI systems can materially affect.
SOC 25-1032
Engineering Teachers, Postsecondary AI displacement risk
Engineering faculty lecture, run laboratories, supervise design projects, and write grants. AI handles problem-set solutions and drafts documentation, but accredited programs require hands-on lab instruction and supervised capstone work: physical verification of what students actually built.
Likely potential for exposed tasks to move to software after workflow integration.
ABET accreditation keeps laboratory and design instruction at the center of the degree, and research funding still rewards novel ideas over generated text. Faculty who teach students to verify AI-generated designs raise the discipline's value.
Distribution
Where Engineering 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 25-1032. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $109,270 (May 2025, US national). The latest BLS row matched SOC 25-1032.
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 Engineering Teachers, Postsecondary
SOC 25-1032 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 Engineering 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
SOC25-1032
- Core task / ID 5757
Conduct research in a particular field of knowledge and publish findings in professional journals, books, or electronic media.
- Core task / ID 5758
Prepare course materials, such as syllabi, homework assignments, and handouts.
- Core task / ID 5756
Evaluate and grade students' class work, laboratory work, assignments, and papers.
- Core task / ID 5760
Write grant proposals to procure external research funding.
- Core task / ID 5755
Supervise undergraduate or graduate teaching, internship, and research work.
- Core task / ID 5754
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
Supervise student laboratory work
Exposure 24, automation 8%, augmentation 50%.
O*NET evidence: Supervise students' laboratory work. (ID 5761)
Prepare and deliver engineering lectures
Exposure 54, automation 27%, augmentation 70%.
O*NET evidence: Prepare and deliver lectures to undergraduate or graduate students on topics such as me... (ID 5753)
Write grant proposals for research funding
Exposure 58, automation 32%, augmentation 66%.
O*NET evidence: Write grant proposals to procure external research funding. (ID 5760)
Evaluate laboratory work and assignments
Exposure 52, automation 28%, augmentation 62%.
O*NET evidence: Evaluate and grade students' class work, laboratory work, assignments, and papers. (ID 5756)
TaskExposureAutomationAugmentation
Supervise student laboratory work248%50%
Prepare and deliver engineering lectures5427%70%
Write grant proposals for research funding5832%66%
Evaluate laboratory work and assignments5228%62%
Transition pathways
Adjacent moves that preserve existing skills
credentialed transition
LowDepartment Chair
Training horizon: 12-24 months. Skill overlap 66. Wage preservation signal 116.
- Lead accreditation reviews
- Manage research portfolios
- Own program outcomes
role redesign
LowIndustry Liaison Director
Training horizon: 6-12 months. Skill overlap 68. Wage preservation signal 114.
- Build corporate partnerships
- Place capstone projects
- Commercialize lab research
Comparison guides
Compare the next move before you commit
12-24 months
Engineering Teachers, Postsecondary to Department Chair
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Engineering Teachers, Postsecondary into Department Chair.
6-12 months
Engineering Teachers, Postsecondary to Industry Liaison Director
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Engineering Teachers, Postsecondary into Industry Liaison Director.
What the AI risk score means for Engineering Teachers, Postsecondary
The displacement pressure score for Engineering 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. Write grant proposals for research funding carries 32% automation pressure, while Prepare and deliver engineering 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: $109,270 (May 2025, US national). Employment context: Faculty role anchored by accredited labs and design projects. 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
- Accreditation anchors lab instruction
- AI-generated designs need verification teaching
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Engineering 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.
Engineering 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.
Grant writing
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.
Design project 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 accreditation reviews.
- By 90 days, compare internal openings and external postings for Department Chair or Industry Liaison Director and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Engineering Teachers, Postsecondary
Will AI replace Engineering Teachers, Postsecondary?
Engineering faculty lecture, run laboratories, supervise design projects, and write grants. AI handles problem-set solutions and drafts documentation, but accredited programs require hands-on lab instruction and supervised capstone work: physical verification of what students actually built. 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 Engineering Teachers, Postsecondary work are most exposed to AI?
Write grant proposals for research funding and Evaluate laboratory work and assignments show the strongest automation pressure in this model. Prepare and deliver engineering lectures and Write grant proposals for research funding are better treated as AI-augmented work.
What should Engineering Teachers, Postsecondary learn next?
Start with Engineering instruction, Laboratory supervision, Grant writing. The most practical adjacent paths in this model are Department Chair and Industry Liaison Director.
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