Occupation · SOC 25-1032.00
Teach courses pertaining to the application of physical laws and principles of engineering for the development of machines, materials, instruments, processes, and services. Includes teachers of subjects such as chemical, civil, electrical, industrial, mechanical, mineral, and petroleum engineering. Includes both teachers primarily engaged in teaching and those who do a combination of teaching and research.
Also called: Assistant Professor · Associate Professor · Instructor · Professor · Chemical Engineering Professor · Electrical Engineering Professor · Engineering Instructor · Engineering Professor · Environmental Engineering Professor · Mechanical Engineering Professor · Adjunct Engineering Instructor · Adjunct Instructor
Job family: Educational Instruction and Library Occupations
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/roles/role-25-1032-00/context.md directly.
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
92nd-percentile task overlap — yet about 4,100 openings a year (+8.1% projected, BLS), and observed AI use leans 6700% copilot, not hand-off (AEI) . What exposure means →
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) High | 92nd | 1.4 | |
| LLM task exposure, γ (OpenAI / Eloundou) High | 72nd | 0.9 | |
| AI assistant applicability (Microsoft) High | 95th | 0.3 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.5), and including AI-powered software (γ 0.9). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
Most of this job's tasks can be done remotely (Dingel–Neiman), which tends to track with higher digital and AI exposure.
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Keep abreast of developments in the field by reading current literature, talking with colleagues, and participating in professional conferences. | 10.5% | |
| Conduct research in a particular field of knowledge and publish findings in professional journals, books, or electronic media. | 9.6% | |
| Plan, evaluate, and revise curricula, course content, and course materials and methods of instruction. | 8.5% | |
| Compile, administer, and grade examinations, or assign this work to others. | 7.0% | |
| Provide professional consulting services to government or industry. | 6.5% | |
| Prepare and deliver lectures to undergraduate or graduate students on topics such as mechanics, hydraulics, and robotics. | 3.1% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Growing fast · +8.1% by 2034 |
| Projected annual openings | 4,100 |
| Employment 2024 → 2034 | 50,300 → 54,400 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| University and Higher Education Teachers · 2310 | 37% | Minimal |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 67.0% working with AI · 29.3% handed to AI |
| Most common way people use AI here | Iteration · you and AI go back and forth |
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 29.9% |
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Advise students on academic and vocational curricula and on career issues. | Iteration | 23.7% |
| Conduct research in a particular field of knowledge and publish findings in professional journals, books, or electronic media. | Learning | 13.9% |
| Provide professional consulting services to government or industry. | Iteration | 12.8% |
| Prepare and deliver lectures to undergraduate or graduate students on topics such as mechanics, hydraulics, and robotics. | Learning | 10.7% |
| Compile, administer, and grade examinations, or assign this work to others. | Directive | 10.6% |
| Prepare course materials such as syllabi, homework assignments, and handouts. | Directive | 3.8% |
| Compile bibliographies of specialized materials for outside reading assignments. | Directive | 2.9% |
| Plan, evaluate, and revise curricula, course content, and course materials and methods of instruction. | Iteration | 2.2% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Maintain regularly scheduled office hours to advise and assist students. | 100.0% | |
| Collaborate with colleagues to address teaching and research issues. | 100.0% | |
| Participate in campus and community events. | 100.0% | |
| Write grant proposals to procure external research funding. | 100.0% | |
| Serve on academic or administrative committees that deal with institutional policies, departmental matters, and academic issues. | 100.0% | |
| Compile bibliographies of specialized materials for outside reading assignments. | 98.6% |
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
Help me advise students on academic and vocational curricula and on career issues. From: Advise students on academic and vocational curricula and on career issues. · 23.7% of measured AI use · task iteration
Help me conduct research in a particular field of knowledge and publish findings in professional journals, books, or electronic media. From: Conduct research in a particular field of knowledge and publish findings in professional journals, books, or electronic media. · 13.9% of measured AI use · learning
Help me provide professional consulting services to government or industry. From: Provide professional consulting services to government or industry. · 12.8% of measured AI use · task iteration
Help me prepare and deliver lectures to undergraduate or graduate students on topics such as mechanics, hydraulics, and robotics. From: Prepare and deliver lectures to undergraduate or graduate students on topics such as mechanics, hydraulics, and robotics. · 10.7% of measured AI use · learning
All 24 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Engineering and Technology | 5.0 | |
| Design | 4.6 | |
| Computers and Electronics | 4.5 | |
| Mathematics | 4.5 | |
| English Language | 4.4 | |
| Physics | 4.2 | |
| Education and Training | 3.8 | |
| Mechanical | 3.5 | |
| Chemistry | 3.2 | |
| Administration and Management | 3.2 |
| Oral Expression | 4.4 | |
| Speech Clarity | 4.3 | |
| Written Comprehension | 4.1 | |
| Oral Comprehension | 4.0 | |
| Written Expression | 3.9 | |
| Deductive Reasoning | 3.9 | |
| Inductive Reasoning | 3.9 | |
| Information Ordering | 3.8 | |
| Category Flexibility | 3.8 | |
| Mathematical Reasoning | 3.8 | |
| Near Vision | 3.8 | |
| Speech Recognition | 3.8 | |
| Problem Sensitivity | 3.4 | |
| Number Facility | 3.4 | |
| Fluency of Ideas | 3.3 | |
| Originality | 3.3 |
| Speaking | 4.1 | |
| Learning Strategies | 4.1 | |
| Reading Comprehension | 4.0 | |
| Active Listening | 4.0 | |
| Writing | 3.9 | |
| Mathematics | 3.9 | |
| Critical Thinking | 3.9 | |
| Active Learning | 3.6 | |
| Science | 3.5 | |
| Monitoring | 3.4 |
| Instructing | 4.1 | |
| Judgment and Decision Making | 3.8 | |
| Complex Problem Solving | 3.3 | |
| Coordination | 3.1 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Showing the top 40 of 42.
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
What to study: Engineering , Engineering/Engineering-Related Technologies/Technicians , Physical Sciences . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| Master's Degree | 5.1% | |
| Bachelor's Degree | 0.8% | |
| Post-Baccalaureate Certificate | 0.8% |
The interests and personal qualities O*NET associates with people who do this work.
| Teaching/Education | 6.6 | |
| Professional Advising | 6.0 | |
| Engineering | 5.4 | |
| Public Speaking | 5.0 | |
| Mathematics/Statistics | 5.0 | |
| Physical Science | 4.9 | |
| Mechanics/Electronics | 3.5 | |
| Information Technology | 3.1 |
| Dependability | 6.0 | |
| Attention to Detail | 5.0 | |
| Integrity | 4.0 |
| Social | 5.8 | |
| Investigative | 5.4 | |
| Realistic | 4.8 | |
| Conventional | 3.4 | |
| Artistic | 3.2 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $59,790 |
| 25th percentile | $80,060 |
| Median (50th) | $106,120 |
| 75th percentile | $136,400 |
| 90th percentile | $200,650 |
| People employed | 39,910 |
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Educational Services · Sector | 39,890 | $106,120 |
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Educational Services · Sector | 11.3× | 39,890 |
Part of the Education career cluster.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Engineering Teachers, Postsecondary — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
On the global GenAI exposure gradient this work sits around the 70th percentile of 427 international occupations.
Engineering Teachers, Postsecondary show 92nd-percentile AI task overlap — and about 4,100 annual U.S. openings
Engineering Teachers, Postsecondary show 92nd-percentile AI task overlap — and about 4,100 annual U.S. openings • Engineering Teachers, Postsecondary rank in the 92nd percentile (High band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 4,100 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be growing fast (+8.1%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $106,120, across about 39,910 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 67% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Engineering Teachers, Postsecondary". https://singulariki.com/roles/role-25-1032-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
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Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Engineering Teachers, Postsecondary." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-25-1032-00
Singulariki. (2026). Engineering Teachers, Postsecondary. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-25-1032-00
@misc{singulariki-role-25-1032-00,
title = {Engineering Teachers, Postsecondary},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-25-1032-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.