← Risk register SOC 25-1062 · reviewed 2026-08-11

Area, Ethnic, and Cultural Studies Teachers, Postsecondary

11,300 US workers · median $85,020/yr · Education

EXPOSED

The paper side of this job — syllabus drafting, lecture outlines, reading summaries, literature reviews, first-pass essay feedback — is squarely inside what current models do at usable quality, and the scholarly output (journal articles, book chapters) faces the same pressure. What holds is the live seminar: facilitating charged discussions about race, identity, and power where a human's presence, credibility, and willingness to be accountable in the room is the pedagogy, plus thesis advising and mentorship students specifically seek from a person. The real threat to this occupation over ten years is enrollment shifts, program consolidation, and political defunding more than automation — and the modal worker here is increasingly adjunct, with no licensure or tenure shield.

10-year outlook: By 2035 the teaching and grading workload is heavily AI-assisted and standalone programs keep consolidating into broader departments, so the surviving jobs concentrate in seminar-based, advising-heavy, cross-listed faculty lines.

US employment, 2019–2025+6.6%
10,60011,300 workers

Dipped in 2020, then grew past where it started.

Median pay $77,070 → $85,020 -11.7% in real terms (nominal +10.3%, less ~25% US inflation over the period)

The job count is not the verdict

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

+2.4%

Percentage only. The projection counts a different population from the 11,300 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 +2.4% 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.

~1,100 openings a year on average, including replacing people who leave.

One email if this score changes. Watch as many occupations as you like from the same address — no account, and nothing is sent on a schedule, only when a verdict actually moves.

Also known as — 24 job titles this covers

Titles reported by people doing this work, from the US Department of Labor's O*NET survey. If your job title is here, this page is about your work even though the name doesn't match.

LecturerProfessorFaculty LecturerAdjunct ProfessorCollege ProfessorEthnology TeacherAssistant ProfessorAssociate ProfessorEthnology ProfessorHumanities ProfessorCollege Faculty MemberEthnic Origins TeacherEthnic Studies TeacherAsian Studies ProfessorBlack Studies ProfessorGender Studies LecturerEthnic Studies ProfessorGender Studies ProfessorAfrican Studies ProfessorHawaiian Studies LecturerUniversity Faculty MemberWomen's Studies ProfessorAmerican Studies ProfessorLatin American Studies Professor

Score — 43/100 resistance

Holding it up: trust premium (14/20). Weakest point: liability shield (3/20).

Five dimensions, 0–20 each, summed. Higher means more protected. The arithmetic is shown so you can check it: 9 + 6 + 3 + 14 + 11 = 43. · Scored 2026-08-11, and re-examined when evidence accumulates rather than on a schedule.

Task resistance 9/20

Mixed — a routine tier and a judgment tier Roughly half your week — building the Chicana feminism unit reading list, drafting lecture notes, summarizing secondary literature, writing the first pass of comments on a 12-page paper on settler colonialism — is work a model does at draft-usable quality today, which is why this sits at 9 rather than 14; what pulls it up from 5 is that running a seminar where students are arguing about their own family histories and someone starts crying is not a text-generation task.

Embodiment 6/20

Some physical or field component You are in a physical room, and that matters — reading the silence after a hard comment, holding office hours, occasionally leading a community site visit or archival trip — but nothing in the job requires manual skill in an uncontrolled setting, and the pandemic proved much of this delivers over Zoom, which caps it at 6.

Liability shield 3/20

No licence, no signature requirement No state licence, no board, no exam: a PhD (or in adjunct hiring, an MA plus availability) is a credential the department screens on, not a legal barrier, and if a course is cut or a section handed to a cheaper instructor no statute requires that instructor be you.

Trust premium 14/20

The human relationship is the product Students choose this field and these advisors for reasons that are personal — a dissertation chair who understands what the archive silences look like, a mentor whose letter carries weight in a small subfield where everyone knows each other — and that named, specific relationship is what dissertation committees and alumni loyalty are actually made of, which is why it clears 13.

Judgment & accountability 11/20

Meaningful discretion You decide whether a text stays on the syllabus when it will hurt some students to read it, when a heated exchange gets stopped versus pushed further, and how to grade an argument you think is wrong but well-made — real, unscripted calls — but they are made inside institutional rubrics, grade appeal processes, and a department chair's oversight, so the buck does not stop at 14+ with you.

Confidence: medium · reviewed 2026-08-11 · how scoring works

What this job involves — and which parts are yours

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.

AI already does these at usable quality

These still need a person

Active moats on the surviving side: trust, judgment

How to future-proof this job

Training paths for your skill gaps: edX — performance measurement and evaluation free to audit · Toastmasters — public speaking practice at local clubs worldwide low · Coursera — teaching and instructional design, audit free free to audit · Khan Academy — mathematics, arithmetic through calculus free · Coursera — project coordination and cross-team delivery free to audit · Coursera — decision making under uncertainty free to audit

All 35 skills ranked by how many jobs they open →

Where this experience transfers — occupations you could move toward

Computed from U.S. Dept. of Labor O*NET skill and knowledge profiles: high overlap with what you already do, a materially higher resistance score, no large jump in required training, and no licence you would have to start a new pipeline to get. Targets that pay meaningfully less, that are themselves COOKED, or whose own headcount is falling are excluded — a move into a shrinking trade is not an escape.

Art, Drama, and Music Teachers, Postsecondary EXPOSED · 58/100 · you already have ~84% of the skill profile

Skills to close: Monitoring, Speaking

Social Work Teachers, Postsecondary EXPOSED · 56/100 · you already have ~82% of the skill profile

Skills to close: Instructing, Monitoring, Mathematics, Coordination

Law Teachers, Postsecondary EXPOSED · 59/100 · you already have ~80% of the skill profile

Skills to close: Judgment and Decision Making, Speaking

What would move this back up — beyond any one person

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 62/100, still EXPOSED.

6 specific changes that would raise this score
  • already happening liability shield +4

    Enforcement of the US Department of Education's 'regular and substantive interaction' requirement for distance education (34 CFR 600.2) plus regional accreditor rules (e.g. SACSCOC, MSCHE faculty credentialing standards) explicitly naming a credentialed human as instructor of record who cannot delegate interaction to an AI system — if written as an auditable Title IV condition rather than guidance, the named faculty member becomes the compliance point for federal aid eligibility

  • already happening task resistance +4

    Genuine two-tier structure: if summarization, syllabus scaffolding, and first-pass essay comments are conceded to models, the residual day is seminar facilitation, thesis direction, primary-source and oral-history fieldwork in languages with thin digital corpora, and archive work under access restrictions — the measured task mix shifts upward without any new rule

  • already happening judgment accountability +3

    Post-2024 Title VI and state 'divisive concepts' complaint regimes make a named instructor the accountable party for what is said and assigned in a charged classroom; if institutions formalize this into instructor-of-record attestations for contested content, the role owns consequential ambiguous calls with personal exposure rather than departmental cover

  • plausible liability shield +3

    Faculty union contract language barring AI-generated instruction or requiring human sign-off on grades and course content — Rutgers AAUP-AFT and several CSU/CFA and Ontario OPSEU college negotiations have raised exactly this; if a system-wide contract makes grade authorship a named-human obligation with grievance teeth, the shield stops being purely reputational

  • plausible judgment accountability +3

    IRB and community-research review roles: if universities and funders (NIH/NSF community-engaged research terms, tribal IRBs, CARE Principles for Indigenous data governance) require a named area-studies scholar to certify community consent and cultural protocol for datasets and archives, that certification is an ambiguous consequential call with a signature attached

  • plausible trust premium +2

    If institutions market and price human-taught small seminars as the differentiator against AI-delivered general education (visible in residential liberal-arts positioning and in emerging 'AI-free instruction' disclosures some campuses are debating), enrollment in discussion-based courses becomes an explicit purchase of a person

The limit. The binding constraint is not capability but demand and headcount: program consolidation, state defunding of ethnic studies units, and adjunctification cut positions regardless of how these dimensions score. Embodiment has no realistic route. Also note liability_shield gains attach to the instructor-of-record credential, which adjuncts hold too — but they hold it contingently, so the shield protects the function more than the incumbent.

These are conditions, not forecasts — what would have to happen, not what will. Specific rules, cases and bills are named so you can go and check whether they exist and where they stand; verify before relying on any of them. Nothing here is legal or financial advice.

Where this work is, and what it pays there

BLS metro figures for 32 areas. The verdict above does not change by city — the rubric judges what the work involves, not where it happens — but pay and headcount do, and the national median hides a very wide range.

Most of these jobs

New York-Newark-Jersey City, NY-NJ 1,190 $103,700 +22%
Boston-Cambridge-Newton, MA-NH 440 $100,140 +18%
Washington-Arlington-Alexandria, DC-VA-MD-WV 360 $101,700 +20%
Los Angeles-Long Beach-Anaheim, CA 310 $102,030 +20%
Atlanta-Sandy Springs-Roswell, GA 300 $80,980 -5%
Ann Arbor, MI 240 $106,140 +25%
Austin-Round Rock-San Marcos, TX 230 $97,200 +14%
Minneapolis-St. Paul-Bloomington, MN-WI 230 $81,220 -4%

Best paid

Santa Maria-Santa Barbara, CA 40 $145,530 +71%
San Jose-Sunnyvale-Santa Clara, CA 40 $129,270 +52%
San Francisco-Oakland-Fremont, CA 50 $125,890 +48%

Percentages are against this occupation's national median of $85,020. Counts are jobs in that metro, not vacancies. Metros where the BLS suppressed the cell are absent rather than shown as zero.

Who is actually doing this — nobody, on the record

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 43. 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.

Read that as a gap in the reporting we can see, not proof of absence — the dispatch runs on English-language feeds and misses plenty. If you know of a case, tell us, or add a field report from inside the job.

Quick take — do you do this job?

Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.

Self-reported and unverified — a sentiment signal, not a survey. One response per person per occupation; you can change your answer.

Field reports — what people say has changed

No field reports yet. A written account takes a paragraph rather than a tap, goes to an editor before it appears, and is the one thing on this page the rubric cannot produce on its own.

File a field report

Concrete beats general: a tool that arrived, a task that moved, a headcount decision you watched happen. Don't include anything that identifies you or your employer if that would put you at risk.

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Kept current

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