EXPOSED
The text-heavy core of this job — lecture notes, syllabi, reading guides, essay feedback, literature summaries, exam question banks — is exactly what current language models produce at usable quality, and students already use the same tools on the other side of the desk. What resists is the live seminar: running a room where undergraduates argue about evidence, teaching source criticism by watching a student misread a document and correcting them in real time, and advising thesis work. The bigger near-term threat is not AI replacing historians but humanities enrollment decline and adjunctification shrinking the tenure-track base; the modal worker here is increasingly contingent faculty teaching survey courses.
This fall is concentrated in 2020 and has not recovered since.
Median pay $75,170 → $83,820 -10.8% 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
-0.2%
Percentage only. The projection counts a different population from the 18,790 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Shrinking, but not obviously because of AI
The BLS projects -0.2% by 2034, but at 47/100 this work is only moderately exposed — not the profile of a job current AI can simply do. Occupations shrink for many reasons, and the score does not point at automation as this one's cause.
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.
~1,700 openings a year on average, including replacing people who leave.
LecturerProfessorInstructorHistory TeacherHistory LecturerCollege ProfessorHistory ProfessorAdjunct InstructorHistory InstructorAssistant ProfessorAssociate ProfessorWorld History TeacherArt History InstructorCollege Faculty MemberHistoriography TeacherHistory Faculty MemberEconomic History TeacherHistoriography ProfessorJewish History ProfessorAfrican History ProfessorOnline History InstructorRussian History ProfessorUniversity Faculty MemberAdjunct History Instructor
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Grading 60 identical Western Civ essays against a rubric, assembling a syllabus from a canonical reading list, and writing multiple-choice exam banks on the Treaty of Westphalia are already machine work, which pins this below the midpoint — but the seminar where you make eighteen-year-olds defend a claim about a primary source, and the dissertation chapter you argue with line by line, are not, which keeps it from falling to 5.
Some physical or field component Standing in a lecture hall, handling archival material and rare-book collections in reading rooms, and running site or museum visits are genuine bodily components, but they happen in climate-controlled rooms with no manual dexterity or safety exposure, which caps this near the low end of the mid-band.
Certification preferred, not legally required No state licence gates who teaches Reconstruction to undergraduates; the PhD and departmental hiring committee are the only gates, and accreditation obligations attach to the institution and the credit hour, not to you personally, so you carry credential friction without personal legal liability.
Meaningful discretion You decide what counts as evidence, whether a paper is plagiarised, what grade closes a student's GPA, and how to teach contested historiography — real discretion with real consequences for individuals, but bounded by department curricula, grade-appeal procedures, and Title IX and FERPA reporting channels rather than owned outright.
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 (9/20 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 (6/20) 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 30 of this occupation's 47 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.
Law Teachers, Postsecondary EXPOSED
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 60/100, still EXPOSED.
Task-mix shift as generative writing collapses take-home essays: if departments move assessment to oral examination, in-class handwritten blue-book exams, viva-style thesis defense, and archival/primary-source work with physical or paywalled collections, the remaining day is judgment-tier teaching that current models cannot deliver. This is already in motion in UK/Australian universities reintroducing oral and invigilated assessment
Faculty being made the accountable decision-maker in academic-integrity adjudication for AI use — where the instructor's determination, not a detector score, is the finding of record, appealable through a human process. Several universities' honor-code revisions (e.g. Vanderbilt disabling Turnitin's AI detector) already push the consequential call back onto the instructor
Accreditors or state systems requiring that courses marketed as 'in-person' or 'seminar' be taught synchronously by a human of record, and institutions advertising small-enrollment human-led discussion sections as a tuition differentiator (already visible in liberal-arts colleges' marketing and in AAUP/faculty-senate fights over AI-generated course content at e.g. Boise State, Cal State's system-wide ChatGPT rollout backlash)
Accreditation rules (SACSCOC, HLC) tightening 'qualified faculty' and regular-and-substantive-interaction requirements for Title IV eligibility so that a credentialed human must be instructor of record for credit-bearing history courses, with AI-only sections ineligible for federal aid — a live question in Department of Education RSI guidance for distance education
The limit. The binding constraint here is not capability but demand: humanities enrollment decline and adjunctification shrink the base regardless of how these dimensions move. A higher trust premium for human-taught seminars mostly accrues to tenure-track faculty at well-resourced institutions, not to the modal contingent survey-course instructor, who is displaced by section consolidation rather than by a model.
| New York-Newark-Jersey City, NY-NJ | 1,470 | $101,170 +21% |
| Boston-Cambridge-Newton, MA-NH | 540 | $105,420 +26% |
| Los Angeles-Long Beach-Anaheim, CA | 480 | $129,120 +54% |
| Chicago-Naperville-Elgin, IL-IN | 470 | $88,360 +5% |
| Dallas-Fort Worth-Arlington, TX | 430 | $83,820 +0% |
| Houston-Pasadena-The Woodlands, TX | 430 | $102,840 +23% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 420 | $96,840 +16% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 340 | $99,580 +19% |
| Riverside-San Bernardino-Ontario, CA | 110 | $192,320 +129% |
| San Francisco-Oakland-Fremont, CA | 100 | $162,160 +93% |
| San Diego-Chula Vista-Carlsbad, CA | 160 | $135,420 +62% |
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 47. 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.