EXPOSED
The paper-and-screen half of this job — lecture decks, syllabi, reading summaries, literature reviews, quiz banks, rubric-based essay grading, discussion-board moderation — is exactly what current models do at usable quality, and asynchronous online sections are where that substitution bites first. What holds is the live seminar: reading a room, pushing a student past a sloppy argument in real time, advising theses, writing credible recommendation letters, and owning grades that carry institutional weight. The real threat to this occupation is not a bot replacing a professor but budget-driven consolidation of course sections and continued shift to adjunct labor, with AI as the excuse.
Roughly flat across the period, with year-to-year wobble.
Median pay $71,530 → $72,990 -18.4% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
+1.7%
Percentage only. The projection counts a different population from the 16,580 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +1.7% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~1,500 openings a year on average, including replacing people who leave.
LecturerCollege TeacherScience TeacherHumanities TeacherLiberal Arts TeacherCity Planning TeacherNaval Science TeacherUrban Planning TeacherForeign Service TeacherIndustrial Arts TeacherLabor Relations TeacherSurvey Research TeacherWeight Control LecturerMilitary Science TeacherSocial Science ProfessorUrban Planning ProfessorSurvey Research ProfessorFamily Consumer Science Teacher
The BLS uses Social Sciences Teachers, Postsecondary, All Other for work that doesn't fit any named occupation, so it covers roles that have little in common with each other. Two consequences worth knowing before you read anything below:
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Roughly half the workload — building the syllabus, generating lecture slides and quiz banks, summarizing assigned readings, grading short essays against a rubric, answering routine emails and moderating discussion boards — is already producible at passing quality by a model, and only the live seminar, thesis supervision, and defensible grade decisions push it up to 8 rather than into the bottom band.
Some physical or field component Being physically in a room matters — projecting to a 60-seat lecture hall, catching the student who has checked out, running a field methods exercise or an archive/community-site visit — but the environment is a scheduled campus classroom, not an uncontrolled one, so this sits mid-range rather than at desk-only 8.
Certification preferred, not legally required No state licence gates the title: hiring turns on a PhD and departmental vote, and legal exposure for a bad grade or a Title IX or FERPA matter lands on the institution, not your personal credential — the 5 reflects the terminal degree and accreditation/SACS-type faculty-qualification rules that still make a human of record necessary on paper.
Meaningful discretion You decide what counts as a defensible argument, whether an assignment is plagiarized or AI-written, whether a student passes, and how to handle politically charged material in class — real discretion with consequences for individuals, but bounded by departmental learning outcomes, grade appeals, and curriculum committees, which caps it at 10 instead of the high band.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (8/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (5/20) is whether the law requires a licensed human to sign. Trust premium (14/20) is whether buyers specifically pay for a person. Judgment and accountability (10/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 29 of this occupation's 45 points (64%).
Embodiment (8/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 59/100, still EXPOSED.
Task-mix shift: if deck-building, quiz banks and first-pass essay grading are conceded to AI, what remains is live seminar facilitation, thesis supervision, IRB-adjacent research mentoring and oral defense examination — genuinely two-tier work. Accreditor rules pushing this direction already exist in draft form (e.g., regional accreditors requiring documented 'regular and substantive interaction' by faculty of record for Title IV distance-ed eligibility, 34 CFR 600.2).
Academic integrity adjudication: if institutions formally require a human faculty member to make and defend the final determination on AI-plagiarism allegations (appealable, FERPA-bound, sometimes litigated), the role owns consequential calls under genuine ambiguity. Several universities have already barred detector output as sole evidence, forcing human judgment.
If the Department of Education's 'regular and substantive interaction' standard is enforced with named faculty of record personally attested per section — and if state authorization boards or accreditors begin sanctioning institutions for AI-delivered instruction without a credentialed instructor signing off on grades and academic integrity findings — the signature becomes non-delegable.
Narrow route only: small-cohort seminar and thesis supervision marketed explicitly as human-taught, plus recommendation letters that graduate programs and employers accept only from a named human. If professional and graduate admissions bodies formally refuse AI-generated or AI-assisted letters, that specific human-attested artifact holds value.
The limit. Every lever here protects the faculty line, not the headcount. None of them counteract the actual mechanism named in the description: section consolidation and adjunctification. A rule requiring one credentialed human per course is compatible with that human teaching four times as many students at adjunct pay. Trust premium is also structurally capped — students buy a credential from an institution, not a professor, and for the non-elite institutions where most of these 16,580 jobs sit there is no realistic route to a high human-taught premium.
| Los Angeles-Long Beach-Anaheim, CA | 1,450 | $132,920 +82% |
| Chicago-Naperville-Elgin, IL-IN | 1,430 | $63,470 -13% |
| New York-Newark-Jersey City, NY-NJ | 1,210 | $81,180 +11% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 420 | $60,050 -18% |
| Baltimore-Columbia-Towson, MD | 330 | $75,050 +3% |
| Dallas-Fort Worth-Arlington, TX | 330 | $80,380 +10% |
| Phoenix-Mesa-Chandler, AZ | 300 | $71,230 -2% |
| Boston-Cambridge-Newton, MA-NH | 290 | $98,370 +35% |
| Riverside-San Bernardino-Ontario, CA | 180 | $180,230 +147% |
| San Francisco-Oakland-Fremont, CA | 290 | $170,240 +133% |
| Sacramento-Roseville-Folsom, CA | 50 | $169,660 +132% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 45. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.