← Risk register SOC 25-9045 · reviewed 2026-08-11

Teaching Assistants, Except Postsecondary

1,420,350 US workers · median $36,780/yr · Education

EXPOSED

The core of this job — sitting beside a struggling second-grader, de-escalating a meltdown, assisting a student with feeding, toileting, or mobility, watching a playground — is physical child supervision that no current robot or chatbot performs. What AI does erode is the paperwork edge: drafting worksheets, grading practice sets, prepping materials, logging behavior data, and one-on-one reading/math drill, where adaptive software is already deployed in classrooms. The modal worker here is a special-education paraprofessional whose real risk is district budget cuts and edtech substitution for tutoring blocks, not automation of the caregiving core; the weak spot is the near-total absence of a licensure shield (ParaPro test or associate degree, not a protected license).

10-year outlook: Headcount holds or grows because special-education caseloads and supervision ratios are legally driven, but general-classroom aides whose day is worksheets, prep, and drill tutoring will be thinned first as districts substitute adaptive software under budget pressure.

US employment, 2019–2025+5.5%
1,346,9101,420,350 workers

Dipped in 2020, then grew past where it started.

Median pay $27,920 → $36,780 +5.4% in real terms (nominal +31.7%, 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

-1.5% 1,422,800 → 1,401,700 on the projections basis

Shrinking, but not obviously because of AI

The BLS projects -1.5% by 2034, but at 60/100 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.

~170,400 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.

GraderProctorCo-TeacherTest GraderPaper GraderParaeducatorTeacher AideGrading ClerkClassroom AideHelper TeacherPreschool AideTeacher's AideStudent TeacherTutor AssistantParaprofessionalPractice TeacherInstructional AideClassroom AssistantSchool Reading AideTeacher's AssistantAssistant InstructorKindergartner HelperEducational AssistantElementary Instructor

Score — 60/100 resistance

Holding it up: embodiment (17/20). Weakest point: liability shield (5/20).

Five dimensions, 0–20 each, summed. Higher means more protected. The arithmetic is shown so you can check it: 16 + 17 + 5 + 13 + 9 = 60. · Scored 2026-08-11, and re-examined when evidence accumulates rather than on a schedule.

Task resistance 16/20

Tasks largely resist digitisation Physically restraining a student mid-crisis under a district's approved restraint protocol, changing a diaper for a nonverbal 10-year-old, and shadowing a runner on the playground are the tasks that fill the day, and none of them decompose into anything a model can do — the 16 rather than 19 reflects that grading practice sets, prepping laminated materials, logging ABC behavior data, and running the scripted reading fluency drill are genuinely being handed to adaptive software.

Embodiment 17/20

Hands-on in uncontrolled environments You work in hallways, cafeterias, buses, sensory rooms, and playgrounds — uncontrolled spaces with unpredictable bodies — doing lifting, transfers, hand-over-hand feeding, and physical de-escalation; it sits at 17 rather than 20 only because a meaningful slice of the week is spent seated at a table or a computer doing small-group instruction and data entry.

Liability shield 5/20

Certification preferred, not legally required The ParaPro Assessment or two years of college credit under ESSA is a hiring screen, not a license — no board can revoke your practice, and when an IEP goal is missed or an incident report is filed, the certified special-education teacher and the district own it, which is why this is 5 and not 0: the ESSA paraprofessional requirement is a real federal floor a district cannot simply waive.

Trust premium 13/20

The human relationship is the product You are often the one adult a specific student will accept — the paraprofessional assigned 1:1 whose removal triggers regression, whose reading of a kid's pre-meltdown cues is documented nowhere; 13 rather than 17 because the assignment is made by the district, follows the placement, and parents did not choose you by name.

Judgment & accountability 9/20

Meaningful discretion You make live calls the IEP does not spell out — when to prompt versus wait, when a behavior plan is failing today, when to pull a student from the room — but the plan, the goals, and the consequential decisions are written by the teacher and the IEP team, so 9 reflects real in-the-moment discretion inside someone else's framework rather than ownership of the outcome.

Confidence: high · 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: embodiment, physical-presence, trust

How to future-proof this job

All 35 skills ranked by how many jobs they open →

Where this experience transfers — nothing clears the bar

No occupation passed every test: close enough to teaching assistants, except postsecondary on skills and subject matter, at least 10 points more resistant, no big jump in training, no new licence, no pay cut, and not shrinking on its own. That happens for 223 of the 654 occupations here that aren't SAFE, and it is worth stating plainly rather than leaving the section off.

The usual reason is that exposure travels with the skill profile. The jobs most similar to yours tend to be exposed for the same reasons yours is, so the near neighbours don't clear the gap — and the ones that do are a different kind of work, not a transfer of what you already know. Read that as a limit of this method, not a verdict that you're stuck: it only compares whole occupations, and it cannot see specialisation, industry, or anything you'd bring that isn't in a federal skill survey.

What would move this occupation up is the other direction, and on this page it's the more useful one.

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 78/100 — SAFE.

6 specific changes that would raise this score
  • already happening liability shield +3

    Due-process settlements and OCR findings requiring districts to prove a qualified human adult was physically present for specified minutes of 1:1 support, making software substitution for an IEP service minutes non-compliant.

  • already happening task resistance +2

    Task-mix shift: if adaptive reading/math software absorbs the drill blocks and AI absorbs behavior-data logging, the residual day is de-escalation, toileting/feeding, seizure and allergy response, and physical prompting — the tier no software touches. Genuine two-tier job.

  • plausible liability shield +5

    IDEA-driven change making the 1:1 paraprofessional a named, legally required service on the IEP with state-set qualification credentials — e.g. state boards moving paraprofessionals onto a credentialed 'educational aide certificate' with renewal and disciplinary authority (Texas and Ohio already issue such certificates; expansion plus IEP-mandated staffing ratios would harden it). Also state mandatory-reporter and restraint/seclusion statutes naming the aide personally.

  • plausible judgment accountability +4

    Formal role in behavior-intervention-plan implementation and crisis response — district policy requiring the trained aide (CPI/Safety-Care certified) to make the restraint/no-restraint call and document it, with the aide's log as the legal record in due-process hearings.

  • plausible trust premium +3

    Parent-side demand encoded in IEP negotiation — parents refusing edtech-only tutoring blocks and winning named human aide minutes; plus growth of privately-funded 1:1 aides and shadow aides in private/charter placements.

  • plausible embodiment +1

    Little headroom; already near ceiling. Rises only marginally if medical-adjacent duties (g-tube feeding, catheterization, lift transfers) shift further onto paraprofessionals under state delegated-nursing-task rules.

The limit. The binding risk here is fiscal, not technical: ESSER cliff budget cuts and aide-to-student ratio increases displace these workers regardless of dimension scores. A licensure shield would also raise entry cost into a job that currently pays near minimum wage, so districts and unions may resist the very credentialing that would protect it.

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 391 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 126,920 $38,890 +6%
Los Angeles-Long Beach-Anaheim, CA 53,010 $46,240 +26%
Chicago-Naperville-Elgin, IL-IN 46,770 $37,840 +3%
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 26,990 $36,080 -2%
Boston-Cambridge-Newton, MA-NH 26,350 $40,930 +11%
Dallas-Fort Worth-Arlington, TX 25,270 $31,480 -14%
Washington-Arlington-Alexandria, DC-VA-MD-WV 25,050 $46,200 +26%
Houston-Pasadena-The Woodlands, TX 23,930 $29,300 -20%

Best paid

Mount Vernon-Anacortes, WA 870 $57,950 +58%
Seattle-Tacoma-Bellevue, WA 21,040 $53,950 +47%
Bellingham, WA 1,200 $51,670 +40%

Percentages are against this occupation's national median of $36,780. 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 60. 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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