Engineering Teachers, Postsecondary
Scrub through 213years of this role's history, from when it first emerged, through every wave of technology that reshaped it, to the cited projections for where it's heading next.
The tools that defined the work
Select an era to see how it reshaped the work.
Chalkboard, drafting instruments, and physical models (pre-industrial pedagogy)
The first engineering professors taught with chalk, physical demonstration models, field surveys, and hand-drafted engineering drawings. Amos Eaton at Rensselaer used specimens and hands-on experiments as primary pedagogical devices. The chalkboard, introduced to American schools in the early 19th century, allowed professors to derive equations in front of students for the first time rather than dictating from a text. Engineering drawings and physical scale models of machines, structures, and surveying instruments were the primary media for conveying design intent.
Work toolChanging equipment Slide rule (universal engineering calculation tool from the 1880s to 1976)
The slide rule was the defining physical artifact of engineering education for nearly a century. Throughout the 1950s and 1960s, a slide rule case clipped to a belt was as common a sight on engineering campuses as a cell phone case in the 1990s. Professors taught with the slide rule as the primary computation medium: numerical solutions to structural, thermodynamic, and electrical problems were derived by hand and slide rule together. The HP-35 scientific calculator, introduced in 1972, began the end of this era. The last slide rule was manufactured in the United States on July 11, 1976. For engineering professors, the shift from slide rule to calculator changed not just the precision of answers but the pedagogical emphasis: the calculator made exact numerical answers cheap, shifting the teaching challenge toward physical intuition and problem-setup rather than computation.
Effect on the workThe slide rule era defined what an engineering faculty member taught: dimensional analysis, order-of-magnitude estimation, and the discipline of working within the precision limits of the tool. The calculator's arrival did not reduce faculty headcount but substantially changed the content of the curriculum.
Work toolChanging equipment FORTRAN + mainframe computing (IBM 704, CDC 6600, VAX era)
IBM released FORTRAN in 1957 as the first practical high-level programming language for scientific and engineering computation. By the mid-1960s, FORTRAN programming had become a required component of engineering curricula at major universities. Engineering professors who had previously used slide rules and log tables to assign numerical problems could now assign programs that ran overnight on the university's mainframe, producing results the slide rule era could not have generated at all. FORTRAN enabled finite element analysis, computational fluid dynamics, and control system simulation to enter engineering education. WATFOR and WATFIV, educational FORTRAN implementations from the University of Waterloo, spread through engineering schools in the late 1960s and 1970s. The VAX minicomputer of the late 1970s brought computation into engineering department laboratories for the first time rather than requiring a separate computing center.
Mainframe processingComputerized records PC + CAD + MATLAB (desktop engineering tools era)
The IBM PC (1981) and Apple Macintosh (1984) brought computing to individual faculty desks and student workstations for the first time. AutoCAD (launched 1982) began displacing hand drafting from engineering education; by the early 1990s most engineering programs had replaced drafting tables with CAD workstations. MATLAB, first released by MathWorks in 1984, quickly became the standard numerical computing environment in electrical, mechanical, and aerospace engineering courses. MATLAB's command-line interface and matrix-native language were pedagogically accessible in ways FORTRAN and C were not, allowing professors to write and run simulations live in lecture for the first time. By the late 1990s the combination of MATLAB, CAD, and finite element packages (ANSYS, first commercialized in 1970 and widely adopted in universities by the 1980s) had restructured engineering curricula around software-mediated simulation and design.
Effect on the workThe PC-and-software era expanded what engineering faculty could teach rather than contracting who was needed to teach it. Laboratory instruction remained unchanged because physical equipment still required physical presence; the software tools multiplied the complexity of problems students could tackle without reducing faculty headcount.
Work toolChanging equipment Internet + course management systems (email, Blackboard, Coursera precursors)
The World Wide Web, reaching US universities in 1993-94, transformed how engineering faculty distributed course materials, communicated with students, and collaborated with research peers. Blackboard (1997), WebCT, and later Moodle moved course logistics, homework submission, and grade distribution online. Email replaced office-hours queues as the primary channel for student questions. Lecture slides in PowerPoint largely displaced the blackboard-derivation format that had defined engineering teaching since Eaton. For faculty who embraced the transition, asynchronous communication reduced the administrative burden of course management; for those who did not, a technology competency gap emerged between faculty generations for the first time.
Work toolChanging equipment MOOCs + simulation software (Coursera 2012, edX, ANSYS Academic, Gradescope)
Coursera launched in 2012 offering Stanford engineering courses at scale; edX followed with MIT and Harvard; within two years MOOCs were widely cited as an existential threat to engineering faculty employment. The threat proved overstated: MOOC completion rates averaged under 10%, and the hands-on laboratory instruction, mentoring, and accreditation functions that define engineering education were not substitutable by video. What did shift was the competitive landscape for lecture content: engineering professors whose primary value was delivering standard course material found themselves competing with world-class recorded versions. The premium shifted toward faculty who added value through mentorship, lab supervision, and research training. Simultaneously, ANSYS SimAI and commercial FEA tools expanded university licensing, and Gradescope (launched 2014, acquired by Turnitin 2018) introduced AI-assisted grading that began reducing the time burden of large homework-set grading for the first time.
Effect on the workMOOC adoption reduced postsecondary enrollment in some continuing-education and professional-development segments, but did not measurably contract degree-program engineering faculty employment. ABET-accredited engineering programs require faculty presence; MOOCs cannot satisfy those requirements.
Work toolChanging equipment Generative AI tools (ChatGPT, MATLAB Copilot, Ansys SimAI, GitHub Copilot for Education)
ChatGPT's public release in November 2022 arrived in engineering classrooms immediately: ASEE 2025 research documented ChatGPT-4o performing with "robust explanations" on mechanical engineering concept inventories, sufficient to complete many traditional take-home problem sets. MATLAB Copilot launched in R2025b (October 2025), integrating natural-language-to-MATLAB code generation into the standard environment already site-licensed at most engineering schools. Ansys SimAI, deployed at 2,750+ universities in 92 countries as of 2026 R1, enables surrogate-model-based simulation at 10-100x less compute time, making FEA and CFD computationally accessible to courses that previously could not afford the solver runtime. GitHub Copilot became free for verified faculty through GitHub Education. The net effect is a restructuring of the faculty workload rather than displacement: routine prep-and-grade tasks are compressible by AI; laboratory supervision, doctoral mentorship, and ABET governance are not.
Effect on the workBLS projects 8.1% employment growth for 25-1032 through 2034, suggesting AI tool adoption has not reversed the growth trajectory. The Federal Reserve's 2026 analysis of educational AI exposure found engineering and technology majors face above-average occupational AI exposure, but the faculty role's physical-lab and governance core remains structurally resistant.
AI audit toolsPattern detection
What credible sources project
Scrub the slider past now to anchor each scenario on the scrubber. The spread is the range of futures credible sources project for this role.
What's shifting in the work right now
The historical view above shows how this role has moved. This is the present-day detail: which AI tools are picking up which tasks, where the edge still is, and the natural directions this work can grow.
What's changing in your day
Three parts of your work where AI is already doing real lifting, and what stays yours.
AI is sitting alongside you hereGrade and provide feedback on handwritten engineering problem sets, lab reports, and design project deliverables — using Gradescope AI-assisted answer grouping to cluster similar solutions and apply rubrics at scale across large cohorts in ME, CE, EE, and ChemE courses, then applying expert engineering judgment to evaluate the physical reasoning and design trade-offs that automated scoring cannot assess.
Grade and provide feedback on handwritten engineering problem sets, lab reports, and design project deliverables — using Gradescope AI-assisted answer grouping to cluster similar solutions and apply rubrics at scale across large cohorts in ME, CE, EE, and ChemE courses, then applying expert engineering judgment to evaluate the physical reasoning and design trade-offs that automated scoring cannot assess.[8],[12]
Adopt Gradescope AI-assisted grouping for handwritten problem sets — documented to cut engineering homework grading time by up to 80%, with HKU mechanical engineering faculty cited in a February 2025 workshop as direct users. Reinvest that saved time into written feedback on students' physical reasoning quality: "your approach to the moment balance is correct but you missed the sign convention — here is why it matters physically." This is where students learn most from grading, and it is where AI cannot reliably add value.
AI is sitting alongside you hereConduct and publish original engineering research — formulating research questions, designing experiments, running simulations, analyzing data, writing papers, and contributing to the engineering knowledge base — using AI tools (MATLAB Copilot for simulation scripting, Elicit for literature synthesis, ChatGPT Edu for drafting) to compress the mechanical phases while keeping the intellectual contribution human-authored.
Conduct and publish original engineering research — formulating research questions, designing experiments, running simulations, analyzing data, writing papers, and contributing to the engineering knowledge base — using AI tools (MATLAB Copilot for simulation scripting, Elicit for literature synthesis, ChatGPT Edu for drafting) to compress the mechanical phases while keeping the intellectual contribution human-authored.[6],[1]
Use AI to compress the mechanical research phases: Elicit can map a 200-paper literature in a fraction of the time manual reading requires; MATLAB Copilot generates simulation scripts from natural-language descriptions of the setup; ChatGPT Edu can draft a related-work section from a structured notes file. Protect the intellectual core — research question formulation, experimental design, and the novel interpretive contribution — as what peer reviewers and grant panels actually evaluate. NSF's 2025 CAREER program and AI Institutes funding demonstrate sustained federal investment in engineering research; early-career faculty who adopt AI research tools without outsourcing originality are the most competitive candidates.
AI is sitting alongside you hereWrite grant proposals to secure NSF, DARPA, DOE, and industry research funding — framing engineering research contributions, budgeting personnel and equipment, describing broader impacts, and submitting through sponsored programs — using ChatGPT Edu for first-draft scaffolding on boilerplate sections while keeping technical approach and specific aims human-authored, and navigating evolving federal AI-disclosure requirements for grant applications.
Write grant proposals to secure NSF, DARPA, DOE, and industry research funding — framing engineering research contributions, budgeting personnel and equipment, describing broader impacts, and submitting through sponsored programs — using ChatGPT Edu for first-draft scaffolding on boilerplate sections while keeping technical approach and specific aims human-authored, and navigating evolving federal AI-disclosure requirements for grant applications.[1],[13]
Use ChatGPT Edu to generate first drafts of broader-impact and facilities sections, then invest human effort in the technical approach and specific aims that reviewers scrutinize. Be aware of evolving federal AI-disclosure requirements: NSF is expected to tighten AI-use disclosure in PAPPG 26-1, and NIH (as of September 2025) does not fund applications where the original ideas were substantially AI-generated. Build program officer relationships early — grant success remains as much relational as textual, and AI cannot substitute for that capital.
Where this role is heading
Natural next steps for someone with your foundation: not exits, evolutions.
Education Administrators, Postsecondary
Engineering faculty frequently transition into department chair, associate dean of engineering, or dean of engineering roles — particularly those who enjoy governance, curriculum strategy, and institutional leadership more than day-to-day research production. This path is especially compelling in the 2025-2026 period as engineering colleges urgently need administrators who can articulate AI's technical implications for engineering curricula, navigate ABET's evolving AI-related accreditation guidance, and make credible decisions about which AI simulation and design tools to invest in institutionally. Engineering faculty bring rare technical credibility to administrative roles that are now overwhelmingly concerned with AI governance and workforce-readiness strategy. The CRI increase reflects that engineering education administrators have lower routine-task AI exposure than faculty (less grading and homework generation), offset slightly by increased administrative coordination work that AI tools now meaningfully assist.
- · Higher education budget management: managing faculty lines, equipment budgets, overhead allocations, and external research account administration
- · ABET accreditation leadership: program self-study coordination, continuous-improvement documentation, evaluator-visit preparation, and AI-curriculum integration evidence collection
- · Faculty development and governance: tenure and promotion criteria administration, faculty hiring committee leadership, sabbatical and workload policy development
- · Strategic enrollment management: engineering program positioning, domestic and international student recruitment strategy, articulation agreements with community colleges
- · University AI governance: institutional AI policy development, vendor evaluation for AI educational tools, data-privacy and academic-integrity policy leadership for engineering programs
See the same long-arc view for your own profession.
Browse the directory by industry, or search by title or SOC code. New roles ship every few weeks. Every profile cites every claim.
Browse all roles