EXPOSED
The document-production half of this job — syllabi, lecture slides, reading summaries, exam banks, rubric-based grading of essays, literature reviews — is already within reach of current models, and enrollment in sociology majors is shrinking, which matters more than automation for headcount. What persists is live seminar facilitation on contested topics (race, class, deviance) where students specifically want a human in the room, mentoring and thesis supervision, and original empirical research with IRB accountability. The modal worker here is increasingly a contingent/adjunct instructor with no licensure shield and weak institutional protection, which is the real vulnerability.
Part 2020 shock, part continued decline in the years since.
Median pay $75,290 → $84,290 -10.4% 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
+2.1%
Percentage only. The projection counts a different population from the 11,850 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Growing, and only partly exposed
The BLS expects +2.1% more of these jobs by 2034, and at 47/100 the work is only partly exposed — some tasks are automatable, the core of the job is not. Nothing here is in tension.
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.
LecturerProfessorInstructorFaculty MemberAdjunct ProfessorCollege ProfessorAdjunct InstructorSociology LecturerAssistant ProfessorAssociate ProfessorSociology ProfessorSociology InstructorCollege Faculty MemberRace Relations ProfessorSocial Science ProfessorSociology Faculty MemberSocial Science InstructorUniversity Faculty MemberAdjunct Sociology ProfessorMarriage and Family TeacherSociology Adjunct ProfessorSociology Adjunct InstructorSocial Organization ProfessorComparative Sociology Professor
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Roughly half the week — building syllabi, slide decks, discussion prompts, multiple-choice item banks, marking 120 intro-level short answers against a rubric — is text-in/text-out work a model does credibly now, which is what pins this at 9 rather than mid-teens; the part that holds is running a live seminar where a student says something racist or wounded and you have twenty seconds to decide how to use it, plus supervising honors theses and IRB-approved data collection.
Some physical or field component The physical component is being bodily present in a room at a fixed hour — proctoring, office hours, the field-methods course that sends students out to observe a courthouse or a bus depot — but none of it involves uncontrolled environments or manual skill.
No licence, no signature requirement No state licence, no board, no certification exam gates who teaches Intro to Sociology; the PhD is a hiring convention, and for the adjunct teaching three sections on a semester contract even that is negotiable, so nothing legally requires a specific credentialed human to sign off on the course.
Meaningful discretion You decide when a discussion of policing or gender has crossed from productive discomfort into harm, whether a plagiarism case goes to the academic integrity office, how to grade an argument you find politically wrong but methodologically sound, and whether an undergraduate research design touching vulnerable populations is defensible to the IRB — real calls with consequences, but bounded by department curricula, appeals processes, and Title IX reporting rules that take the hardest decisions out of your hands.
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 (3/20) 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 (12/20) 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 47 points (62%).
Embodiment (9/20) 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 59/100, still EXPOSED.
IRB and research-integrity regimes tightening so that a named faculty PI must personally attest to human-subjects protocol compliance and to the provenance of qualitative data — plus journals (Sage, ASA journals) requiring a named human to certify no fabricated interview/ethnographic data, which is squarely sociological work
Accreditor or state-system rules requiring that courses advertised as in-person seminars be led by a human instructor of record, plus institutional 'AI disclosure' policies (already appearing in SUNY/CSU academic senate resolutions and AAUP guidance) that force programs to state whether instruction is AI-assisted — making human-taught small seminars a marketed, tuition-justifying product
Task-mix shift: if slide decks, exam banks and first-pass essay grading are conceded to AI, the residual role is live facilitation of contested-topic discussion, thesis supervision, and fieldwork design — the genuine judgment tier this occupation has. Watch for departments formally reassigning grading load to AI while protecting seminar caps
Title VI/Title IX and state 'divisive concepts' enforcement making the instructor of record personally answerable for classroom content decisions, with faculty handbooks naming them as the responsible party for AI-generated course materials; also union contracts (CFA, Rutgers AAUP-AFT) with clauses barring AI as instructor of record
The limit. Even with every lever, headcount is governed by sociology major enrollment and the tenure-track-to-adjunct ratio, not by task automation; a higher score would describe the protected instructor of record, not the modal adjunct.
| New York-Newark-Jersey City, NY-NJ | 1,020 | $99,850 +18% |
| Boston-Cambridge-Newton, MA-NH | 400 | $103,810 +23% |
| Chicago-Naperville-Elgin, IL-IN | 310 | $86,040 +2% |
| Los Angeles-Long Beach-Anaheim, CA | 280 | $128,010 +52% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 210 | $95,620 +13% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 200 | $97,580 +16% |
| Dallas-Fort Worth-Arlington, TX | 190 | $86,800 +3% |
| Milwaukee-Waukesha, WI | 180 | $98,500 +17% |
| Riverside-San Bernardino-Ontario, CA | 70 | $157,630 +87% |
| San Diego-Chula Vista-Carlsbad, CA | 110 | $141,950 +68% |
| San Francisco-Oakland-Fremont, CA | 50 | $132,930 +58% |
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.