EXPOSED
The written core of this job — lecture notes, syllabus construction, reading summaries, essay feedback, literature reviews for publications — is exactly what large language models do at usable quality, and students are already using them on the other side of the desk. What survives is live Socratic seminar work, thesis and honors supervision, letters of recommendation that carry a named human's reputation, and departmental judgment calls on curriculum and admissions. The real threat to this occupation is less the model than the budget: political science departments are shrinking through adjunctification, and AI gives administrators a cheaper story about section sizes.
Dipped in 2020, then grew past where it started.
Median pay $85,930 → $98,070 -8.7% 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%
Percentage only. The projection counts a different population from the 16,970 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% 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.
~1,600 openings a year on average, including replacing people who leave.
LecturerProfessorInstructorFaculty MemberAdjunct ProfessorCollege ProfessorAdjunct InstructorGovernment TeacherAssistant ProfessorAssociate ProfessorGeopolitics TeacherGovernment ProfessorGovernment InstructorCollege Faculty MemberPublic Policy ProfessorUniversity Faculty MemberPolitical Theory ProfessorPolitical Science ProfessorPolitical Science InstructorPublic Administration TeacherInternational Relations TeacherPublic Administration ProfessorPolitical Science Faculty MemberInternational Relations Professor
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Drafting a comparative-politics syllabus, summarizing Tocqueville or Dahl for undergraduates, writing multiple-choice and short-answer exams, and marking 60 five-page papers on federalism are all tasks a model completes at grading-curve quality today, which is why this sits at 8 rather than mid-teens; the residue that holds the number above 6 is the unscripted seminar where you press a student on why their causal claim about democratic backsliding doesn't follow, and dissertation-committee work where you read a bad chapter and know which of three problems to fix first.
Some physical or field component The physical component is showing up in a specific room at a specific hour — lecture halls, office hours, proctoring blue-book finals, faculty senate meetings — plus conference travel and occasional field or study-abroad supervision, which is bodily presence in controlled, climate-controlled settings rather than work that requires hands on anything.
No licence, no signature requirement There is no license to teach political science: a PhD is a hiring credential, not a statutory gate, no board can strike you off, and nothing you say in a lecture on constitutional law creates personal legal exposure — tenure is a contract protection, not a liability shield, and adjunct appointments carry not even that.
Meaningful discretion You own real calls — plagiarism and AI-use accusations that can end a student's enrollment, grade appeals, graduate admissions rankings, curriculum votes on what counts as the methods requirement — but these run through department chairs, honor councils, and university appeals processes rather than resting on you alone, which is what keeps this at 10 instead of the high teens.
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 (2/20) 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 26 of this occupation's 42 points (62%).
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 52/100, still EXPOSED.
Accreditor or institutional policy that makes named-human instruction of record a condition of credit-bearing courses — e.g. SACSCOC/HLC 'regular and substantive interaction' rules (already the US Dept. of Education standard distinguishing distance education from correspondence courses) enforced against AI-delivered sections, plus AAUP-backed faculty contract language (as in the 2024-25 Rutgers and CSU/CFA bargaining over AI) requiring bargaining before AI substitutes for instructors
Task-mix shift as assessment moves off the take-home essay: oral examinations, in-class blue-book writing, and defended thesis work become the graded core (visible now in Australian universities' 'assessment reform' mandates and in many US departments' post-2023 syllabus rewrites), leaving the day dominated by live seminar facilitation and individual supervision rather than text production
Formal ownership of AI-integrity adjudication: departments designating faculty as the accountable decider on suspected AI misconduct and on graduate admissions/funding where AI-screening outputs must be overridden by a named human, with the decision appealable to that person
Only a thin route: named instructor-of-record signature required on grade appeals, Title IX/mandatory-reporting duties, and F-1 visa academic-progress certifications — administrative rather than personal-liability exposure, so this stays low
The limit. Even with all of these, the binding constraint is enrollment and state appropriations, not capability. A higher trust premium protects the tenure-line role while adjunct sections consolidate; the occupation's headcount can fall sharply with its resistance score unchanged.
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 1,480 | $102,310 +4% |
| New York-Newark-Jersey City, NY-NJ | 1,220 | $107,070 +9% |
| Boston-Cambridge-Newton, MA-NH | 670 | $131,690 +34% |
| Portland-Vancouver-Hillsboro, OR-WA | 430 | $82,480 -16% |
| Los Angeles-Long Beach-Anaheim, CA | 420 | $119,590 +22% |
| Houston-Pasadena-The Woodlands, TX | 360 | $102,960 +5% |
| Dallas-Fort Worth-Arlington, TX | 340 | $79,660 -19% |
| Atlanta-Sandy Springs-Roswell, GA | 320 | $86,930 -11% |
| Riverside-San Bernardino-Ontario, CA | 70 | $150,940 +54% |
| Ann Arbor, MI | 140 | $137,430 +40% |
| Boston-Cambridge-Newton, MA-NH | 670 | $131,690 +34% |
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 42. 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.