Role Definition
| Field | Value |
|---|---|
| Job Title | Engineering Teachers, Postsecondary (SOC 25-1032) |
| Seniority Level | Mid-level (Assistant/Associate Professor, 5-12 years) |
| Primary Function | Teaches courses in engineering disciplines — mechanical, electrical, civil, chemical, industrial, environmental, aerospace, and petroleum engineering — at colleges and universities. Combines classroom lectures with hands-on laboratory instruction where students operate testing equipment, build prototypes, design circuits, use CAD/CAM software on physical projects, run materials stress tests, and machine components. Conducts original engineering research, publishes in peer-reviewed journals, writes grant proposals, mentors undergraduate and graduate students through thesis and capstone design projects, and maintains ABET accreditation standards. Unlike K-12 teachers, requires a terminal degree (PhD in engineering) and an active research programme. Unlike CTE postsecondary teachers, involves original research and graduate-level instruction alongside lab teaching. |
| What This Role Is NOT | NOT a K-12 science or technology teacher (different regulatory framework, younger students). NOT a CTE postsecondary teacher (engineering professors have PhD-level research mandates; CTE instructors focus on vocational training). NOT an engineering manager in industry (no product delivery responsibility). NOT an adjunct or part-time lecturer (weaker barriers, no research mandate). NOT a postdoctoral researcher (no primary teaching duties). |
| Typical Experience | 5-12 years post-doctoral. PhD in an engineering discipline required. Postdoctoral research or industry experience typical. Active research/publication record. Often PE-eligible or licensed. May supervise graduate research labs with expensive physical equipment. |
Seniority note: Full professors with tenure score similarly — the core work is identical with stronger structural protection. Adjuncts and lecturers without tenure, research mandates, or lab supervision duties would score lower, likely Yellow, due to weaker barriers and primary exposure through lecture-only delivery.
Protective Principles + AI Growth Correlation
| Principle | Score (0-3) | Rationale |
|---|---|---|
| Embodied Physicality | 1 | Lab instruction requires physical presence — supervising students operating materials testing machines, machining lathes, 3D printers, oscilloscopes, and prototyping equipment. Engineering labs involve heavy equipment, high voltages, and rotating machinery requiring safety supervision. But labs are structured, controlled environments and lectures are desk-based. Minor-to-moderate physical component overall. |
| Deep Interpersonal Connection | 1 | Mentors graduate students through multi-year research projects and capstone design teams. Builds relationships with undergraduates during lab sessions and office hours. Important but primarily professional academic mentoring rather than therapeutic or pastoral. |
| Goal-Setting & Moral Judgment | 2 | Designs research programmes, sets intellectual direction for lab groups, makes gatekeeping decisions about student competence, directs curriculum content reflecting evolving engineering practice, navigates research ethics and professional engineering standards. Engineering education carries downstream public safety implications — bridges, circuits, and chemical plants must be designed correctly. |
| Protective Total | 4/9 | |
| AI Growth Correlation | 0 | AI adoption does not create or destroy demand for engineering professors. Demand driven by university enrolments in STEM, engineering workforce needs, research funding cycles, and faculty retirements. AI tools augment teaching and research but don't drive new faculty hiring. Neutral. |
Quick screen result: Protective 4/9 with neutral growth = likely Green Zone boundary, similar to Biological Science Teachers Postsecondary. Proceed to confirm with task decomposition and evidence.
Task Decomposition (Agentic AI Scoring)
| Task | Time % | Score (1-5) | Weighted | Aug/Disp | Rationale |
|---|---|---|---|---|---|
| Classroom & lecture teaching — delivering lectures on mechanics, thermodynamics, circuits, materials science, fluid dynamics; leading problem-solving sessions; facilitating design discussions | 25% | 2 | 0.50 | AUGMENTATION | AI generates lecture slides, creates worked examples, and produces problem sets. But the professor delivers content drawing on research and industry experience, adapts to student questions, explains complex engineering concepts through real-world applications, and models engineering reasoning. Human-led, AI-accelerated. |
| Laboratory instruction & supervision — supervising engineering labs (materials testing, circuit design/build, CAD/CAM with physical fabrication, prototyping, machining, 3D printing, structural testing, chemical process equipment) | 20% | 2 | 0.40 | NOT INVOLVED | Faculty must physically supervise students operating heavy equipment — lathes, milling machines, tensile testing rigs, high-voltage circuits, 3D printers, and chemical process equipment. A student incorrectly loading a specimen, wiring a circuit, or operating a lathe requires immediate in-person correction. Safety protocols demand a qualified human present. AI cannot physically intervene when a student misoperates a milling machine or creates an electrical hazard. |
| Research & publication — conducting original engineering research, writing papers, applying for grants, presenting at conferences, peer review | 15% | 2 | 0.30 | AUGMENTATION | AI accelerates literature review, simulation, data analysis, and draft generation. But original research questions, experimental design, physical testing, prototype construction, and interpreting unexpected results require human engineering judgment. Much engineering research involves physical benchwork, fabrication, and testing that AI cannot perform. |
| Curriculum development & course design — developing/updating engineering courses, ABET accreditation alignment, selecting lab equipment, designing design-build projects | 10% | 3 | 0.30 | AUGMENTATION | AI generates draft syllabi, creates learning materials, and suggests course structures. Faculty direct content decisions, ensure ABET compliance, design lab exercises that teach both technique and engineering reasoning, and align curricula with evolving industry practice. AI produces; faculty curate and validate. |
| Student assessment & grading — grading lab reports, design projects, problem sets, exams; evaluating design competence | 10% | 3 | 0.30 | AUGMENTATION | AI can grade numerical problem sets and analyse performance patterns. But evaluating engineering design reports — whether a student correctly assessed structural loads, whether their design choices reflect sound engineering judgment, whether their prototype meets specifications — requires expert evaluation. Faculty assess engineering reasoning, not just correct answers. |
| Student mentoring & advising — advising grad/undergrad students, supervising thesis/capstone design projects, career guidance, recommendation letters, industry introductions | 10% | 1 | 0.10 | NOT INVOLVED | Personal mentoring through the challenges of engineering research and design — guiding students through failed prototypes, helping them develop research methodologies, navigating career choices between industry and academia, writing recommendation letters. Multi-year mentorship relationships are deeply human. |
| Service & committee work — ABET accreditation reviews, faculty governance, industry advisory boards, peer review of manuscripts, professional society leadership | 5% | 2 | 0.10 | AUGMENTATION | AI assists with report drafting, data compilation, and scheduling. But ABET accreditation site visits, building industry partnerships, faculty governance decisions, and professional leadership require human judgment and institutional knowledge. |
| Industry consulting & collaboration — providing engineering expertise to government/industry, supervising industry-sponsored student projects | 5% | 2 | 0.10 | AUGMENTATION | Engineering professors frequently consult for industry and government. AI assists with analysis and report preparation, but client relationships, site visits, and expert judgment on complex engineering problems remain human-led. |
| Total | 100% | 2.10 |
Task Resistance Score: 6.00 - 2.10 = 3.90/5.0
Displacement/Augmentation split: 0% displacement, 70% augmentation, 30% not involved.
Reinstatement check (Acemoglu): AI creates new tasks: integrating AI and ML tools into engineering curricula, teaching students to use AI for simulation, design optimisation, and generative design, evaluating AI-generated engineering solutions for safety and feasibility, supervising students using AI-assisted CAD/CAM, conducting research on AI applications in engineering practice, and teaching responsible use of AI in professional engineering. Engineering professors gain oversight and integration responsibilities as AI enters engineering practice.
Evidence Score
| Dimension | Score (-2 to 2) | Evidence |
|---|---|---|
| Job Posting Trends | +1 | BLS projects "much faster than average" growth (7%+) for postsecondary teachers 2024-2034, with 4,100 projected annual openings for engineering teachers specifically. 50,300 employed (BLS 2024). STEM enrolment strong. Not an acute shortage like nursing or K-12, but consistent demand driven by engineering workforce needs and replacement. |
| Company Actions | 0 | No universities cutting engineering faculty citing AI. No surge in hiring either. ABET accreditation continues to mandate qualified human faculty for lab supervision and programme oversight. ABET AI policy (2025) explicitly states AI must "support and enhance human judgement and interaction, not replace them." Institutions adopting AI tools to augment, not replace, faculty. |
| Wage Trends | 0 | BLS median salary for engineering teachers postsecondary: $106,120 (2024). Among the higher-paid postsecondary specialties. Growing nominally but tracking inflation. Competitive with industry for some sub-disciplines but below industry for high-demand specialties (CS, EE). No significant premium or decline signals. |
| AI Tool Maturity | 0 | Production tools in use: MATLAB, SolidWorks, AutoCAD (all with AI-enhanced features), Gradescope (grading), ChatGPT/Claude (content generation), AI-assisted simulation and FEA tools. All augmentative — AI enhances simulation and design tools but cannot supervise students operating physical equipment, building prototypes, or conducting materials tests. No viable AI alternative for hands-on lab supervision. |
| Expert Consensus | +1 | Brookings/McKinsey: education among lowest automation potential (<20% of tasks). MyJobVsAI estimates ~40% of administrative/routine instructional tasks automatable by 2028 — consistent with augmentation, not displacement. ABET explicitly mandates human judgment. WEF: 78% of education experts say AI augments, not replaces. Engineering teaching adds physical lab protection beyond generic postsecondary teaching. Consensus: transformation of lecture/assessment layers, persistence of lab/research/mentoring core. |
| Total | 2 |
Barrier Assessment
Reframed question: What prevents AI execution even when programmatically possible?
| Barrier | Score (0-2) | Rationale |
|---|---|---|
| Regulatory/Licensing | 1 | PhD in engineering required. ABET accreditation mandates qualified faculty with appropriate expertise — faculty must demonstrate competence through education and professional qualifications. Professional Engineering (PE) licensure relevant for some sub-disciplines. But no state licensure required for the professor role itself, unlike K-12 teachers. ABET is meaningful but not as individually rigid as medical licensure. |
| Physical Presence | 1 | Lab instruction requires physical presence — supervising students with heavy machinery, high-voltage equipment, chemical processes, and fabrication tools. Engineering labs involve more dangerous equipment than biology labs (rotating machinery, welding, high voltages). But lectures and office hours operate effectively online/hybrid. Semi-structured environments overall. |
| Union/Collective Bargaining | 1 | Faculty unions (AAUP, AFT) at many public universities. Tenure system provides structural job protection at research institutions. Not universal — many engineering faculty are on non-tenure tracks, and engineering faculty are more likely than humanities colleagues to have industry alternatives. Moderate protection. |
| Liability/Accountability | 1 | Faculty bear responsibility for laboratory safety — students working with high-voltage circuits, rotating machinery, hazardous chemicals, and pressurised systems. Engineering has downstream public safety implications: graduates design infrastructure the public relies on. Faculty who train incompetent engineers share professional accountability. Lower stakes than patient care but meaningful. |
| Cultural/Ethical | 1 | Strong expectation that engineers are trained by experienced professionals who have conducted real engineering research and practice. The credibility of engineering education depends on faculty with authentic technical and research experience. ABET evaluators expect faculty who can demonstrate professional practice knowledge. Cultural preference operating within professional norms. |
| Total | 5/10 |
AI Growth Correlation Check
Confirmed at 0 (Neutral). AI adoption does not create or destroy demand for engineering professors. The driver is STEM enrolment patterns, engineering workforce needs, NSF/DOD/DOE research funding, and faculty retirement/replacement cycles. AI tools that enhance simulation and design capability are integrated into existing curricula — they create new content to teach (AI/ML in engineering) but this is absorbed into existing faculty roles rather than creating new positions. Engineering professors who integrate AI into their research become more productive, not redundant.
JobZone Composite Score (AIJRI)
| Input | Value |
|---|---|
| Task Resistance Score | 3.90/5.0 |
| Evidence Modifier | 1.0 + (2 × 0.04) = 1.08 |
| Barrier Modifier | 1.0 + (5 × 0.02) = 1.10 |
| Growth Modifier | 1.0 + (0 × 0.05) = 1.00 |
Raw: 3.90 × 1.08 × 1.10 × 1.00 = 4.6332
JobZone Score: (4.6332 - 0.54) / 7.93 × 100 = 51.6/100
Zone: GREEN (Green >= 48, Yellow 25-47, Red <25)
Sub-Label Determination
| Metric | Value |
|---|---|
| % of task time scoring 3+ | 20% |
| AI Growth Correlation | 0 |
| Sub-label | Green (Transforming) — >= 20% task time scores 3+, Growth != 2 |
Assessor override: None — formula score accepted. The 51.6 positions this role correctly between Biological Science Teachers Postsecondary (52.4 — wet-lab + fieldwork protection) and Psychology Teachers Postsecondary (50.6 — clinical practicum supervision). The 0.8-point gap from biology is appropriate: engineering labs involve heavy/dangerous equipment providing similar physical protection, but biology has additional fieldwork protection. Higher than Mathematical Science Teachers (37.5, Yellow) because math professors have zero physical presence protection. The lab component is the key differentiator that holds this role in Green.
Assessor Commentary
Score vs Reality Check
The Green (Transforming) label at 51.6 is honest but sits close to the zone boundary (48) — 3.6 points above Yellow. This proximity warrants flagging. The score is not barrier-dependent: stripping barriers entirely, task resistance alone (3.90) with neutral modifiers would produce a raw score above the Green threshold. The 30% of time in NOT INVOLVED tasks (lab supervision, mentoring) provides genuine structural protection. The modest evidence (+2) and moderate barriers (5/10) are realistic — there is no acute engineering faculty shortage, no state licensure for the professor role, and AI tools are meaningfully augmenting lecture and assessment work.
What the Numbers Don't Capture
- Bimodal by sub-discipline. Mechanical, civil, and chemical engineering faculty who supervise heavy-equipment labs (materials testing, machining, chemical processing) have strong physical presence protection. Electrical and computer engineering faculty who supervise digital labs (software, FPGA, simulation-heavy courses) have weaker physical protection and score closer to Yellow — their labs increasingly operate on screens rather than physical equipment.
- Bimodal by employment type. Tenured research faculty at R1 universities have strong structural protection — tenure, research mandates, grant funding, lab facilities. Adjunct lecturers at teaching-focused institutions who deliver introductory engineering courses without research mandates or lab supervision face genuine displacement risk as AI enables more scalable lecture delivery.
- Industry alternatives create pull, not push. Unlike humanities faculty, engineering professors have high-paying industry alternatives. This creates a structural faculty shortage that protects incumbents but also means the pool of potential replacements is naturally limited — a supply constraint that reinforces job security for those who stay in academia.
- Subject matter is highly codifiable. Engineering content — equations, principles, standards, design procedures — is more codifiable than clinical judgment or pastoral care. The protection comes from the physical lab and design-build core, not from subject matter complexity. If institutions shifted to simulation-only labs, the physical presence protection would erode.
Who Should Worry (and Who Shouldn't)
Shouldn't worry: Faculty who combine active research programmes with hands-on laboratory instruction — the associate professor who runs a structures testing lab, supervises graduate students building and testing prototypes, teaches upper-division lab courses with real equipment, and maintains an active research programme with physical experiments. The more time you spend with students around physical equipment and design-build projects, the safer you are.
Should worry: Faculty whose role is primarily lecture-based with minimal lab supervision — large introductory engineering lecturers in auditorium settings, online-only engineering instructors, and adjunct lecturers teaching foundational courses at multiple institutions without research or lab duties. Also at risk: computer engineering and software-focused faculty whose labs are entirely digital, removing the physical presence protection.
The single biggest separator: Whether your teaching involves supervising students in physical engineering laboratories with real equipment. Engineering professors who own the lab experience — where safety requires a qualified human in the room and real prototyping, machining, and testing cannot be simulated — are well protected. Faculty who primarily lecture about engineering without that physical anchor face steeper transformation pressure.
What This Means
The role in 2028: Engineering professors use AI to generate lecture materials, create problem sets, automate grading of numerical assignments, run enhanced simulations, and accelerate literature reviews. AI-assisted design tools (generative design, topology optimisation) become standard in curricula. But the core job — supervising a student operating a lathe for the first time, troubleshooting a failed prototype, guiding a graduate student through experimental testing, conducting original engineering research in the lab, mentoring students through the demands of engineering training — remains entirely human. The lecture layer transforms; the lab and research layers persist.
Survival strategy:
- Lean into hands-on laboratory instruction — physical lab teaching is the irreducible human core. Maintain and expand your lab teaching load; resist institutional pressure to replace physical labs with simulation-only alternatives
- Integrate AI tools into engineering curricula — teach students to use AI for simulation, generative design, and optimisation. Become the faculty member who bridges AI capability and engineering practice, making yourself essential to the evolving programme
- Build a research programme that requires physical experimentation — experimental engineering requiring prototype construction, materials testing, or field testing is harder to automate than purely computational or simulation-based research
Timeline: 10+ years for core responsibilities (lab instruction, research, mentoring). Lecture delivery and assessment layers transform within 2-5 years. Driven by ABET accreditation mandating qualified human faculty and hands-on laboratory experience, the impossibility of automating physical equipment supervision, and the enduring need for engineers trained on real hardware.
