EXPOSED
The modal postsecondary TA is a graduate student who grades problem sets and essays, writes feedback, holds office hours, answers course-forum questions, and runs discussion or lab sections. Grading, rubric application, feedback drafting, and 24/7 Q&A are exactly what LLM course assistants now do at usable quality and near-zero marginal cost, and universities are deploying them explicitly to cut grading load. What survives is the embodied part: standing in a lab or seminar room, reading confusion on faces, proctoring, and running hands-on demonstrations — plus the fact that TA lines exist partly as graduate funding, which insulates headcount from pure efficiency logic.
Dipped in 2020, then grew past where it started.
Median pay $32,080 → $42,910 +7.0% 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
+3.1%
Percentage only. The projection counts a different population from the 164,090 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 +3.1% 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.
~24,600 openings a year on average, including replacing people who leave.
GraderProctorCo-TeacherTest GraderExam ProctorPaper GraderParaeducatorTeacher AideGrading ClerkClassroom AideHelper TeacherPreschool AideTeacher's AideGraduate FellowStudent TeacherTeaching FellowTesting ProctorTutor AssistantGraduate StudentParaprofessionalPractice TeacherCollege AssistantStudent AssistantTeacher Assistant
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Rubric-based grading of problem sets, drafting written feedback, and answering repeat forum questions are already being handed to LLM graders in large lecture courses, and what holds this at 8 rather than 4 is the live discussion section, wet-lab or studio supervision, and proctoring that still requires a body in a room at a fixed hour.
Some physical or field component A 10 fits the split day: office hours and grading happen at a laptop anywhere, but running a chemistry or biology lab section means handling reagents and equipment, enforcing PPE, and physically walking benches — done in a controlled campus space on a fixed timetable, not an uncontrolled field site, which is why it sits mid-scale rather than in the teens.
No licence, no signature requirement TAs hold no teaching licence and no credential beyond enrolled-student status; the instructor of record signs the grade roster, owns FERPA compliance and academic-integrity findings, and the lab PI holds the safety authority, so a TA's judgment can be overturned by an email.
Executes defined procedures on defined inputs Grades are set against a rubric and a curve the instructor of record defines, suspected plagiarism gets referred upward rather than adjudicated, and syllabus and accommodation decisions are not the TA's to make; the discretion that exists is pacing a section and deciding how much partial credit an unusual solution earns.
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 (1/20) is whether the law requires a licensed human to sign. Trust premium (11/20) is whether buyers specifically pay for a person. Judgment and accountability (6/20) 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 18 of this occupation's 36 points (50%).
Embodiment (10/20) 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.
Substitute Teachers, Short-Term 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 55/100, still EXPOSED.
Graduate-worker union contracts (UAW-affiliated locals at Columbia, Harvard, Michigan, the 48,000-worker UC system) adding explicit clauses on TA-to-student section ratios and bans on replacing bargained TA appointments with automated grading — the UC 2022 contract already fixed appointment counts and workload caps; an AI-displacement clause of the kind WGA won in 2023 would decouple headcount from efficiency further
Task-mix shift as auto-graded problem sets and forum Q&A are absorbed: the residual TA day becomes live section facilitation, in-person oral/whiteboard assessment, and academic-integrity adjudication of suspected AI submissions — a genuine two-tier job where the judgment tier is what remains
Reversion to proctored in-person and oral examination as a response to AI cheating (already spreading in Australian universities under TEQSA guidance and in US STEM gateway courses), plus lab safety rules requiring a trained human present for hands-on wet-lab and machine-shop sections
Accreditor and federal 'regular and substantive interaction' enforcement: ED's distance-education rule requires instructor-initiated human interaction for Title IV eligibility, and if ED or regional accreditors (e.g. SACSCOC, WSCUC) issue guidance that AI course assistants do not count as RSI, human section leaders and graders become a compliance requirement for aid-eligible courses
Honor-code and Title IX procedures naming the section leader as the reporting party of record: if institutional AI-misconduct policy requires a named human TA to make the initial determination and testify at hearings — and courts continue to strike sanctions based on unverified AI detectors — the ambiguity call sits with an identifiable person
The limit. No route to a real liability_shield: TAs hold no license, sign nothing legally operative, and grade under a faculty instructor of record who bears whatever accountability exists. Also note the funding logic cuts both ways — TA lines survive as stipends, but that same logic makes them a target when graduate cohorts shrink, and no lever here protects against enrollment-driven cuts.
| Ann Arbor, MI | 12,860 | $38,990 -9% |
| New York-Newark-Jersey City, NY-NJ | 12,430 | $45,100 +5% |
| Chicago-Naperville-Elgin, IL-IN | 8,860 | $35,760 -17% |
| Los Angeles-Long Beach-Anaheim, CA | 5,990 | $39,940 -7% |
| Sacramento-Roseville-Folsom, CA | 4,800 | $61,780 +44% |
| Phoenix-Mesa-Chandler, AZ | 4,450 | $49,810 +16% |
| College Station-Bryan, TX | 4,370 | $57,790 +35% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 4,290 | $58,330 +36% |
| Canton-Massillon, OH | 310 | $78,510 +83% |
| Bridgeport-Stamford-Danbury, CT | 100 | $68,230 +59% |
| Lubbock, TX | 1,830 | $64,160 +50% |
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 36. 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.