SAFE
The paperwork half of this job — drafting IEP goal language, progress-monitoring summaries, accommodation checklists, parent update letters — is squarely in AI's wheelhouse and will get faster. The other half is bodily and unscheduled: de-escalating a meltdown in a hallway, prompting a 12-year-old through a math task hand-over-hand, reading a nonverbal student's shutdown before it becomes a crisis, and holding a contentious IEP meeting together. State licensure plus IDEA's legal machinery require a named, certified human to author and sign the IEP and to be answerable in due-process hearings.
Dipped in 2020, then grew past where it started.
Median pay $61,440 → $66,810 -13.0% 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
-1.9% 94,800 → 93,000 on the projections basis
Hard to automate, but shrinking anyway
The work resists current AI, yet the BLS projects -1.9% by 2034. Whatever is shrinking this occupation, the evidence does not point to automation — demand, demographics, offshoring and industry decline all shrink jobs that no machine could do.
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.
~6,300 openings a year on average, including replacing people who leave.
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Holding it up: trust premium . Weakest point: liability shield .
Tasks largely resist digitisation The generative gains land on goal-bank drafting and progress notes, but the daily core — running small-group reading in a resource room, co-teaching a 28-student general-ed math block, implementing a behavior intervention plan minute-by-minute, and taking frequency data on a student who elopes — is bodily supervision of adolescents that no system performs, which puts it at 14 rather than the 17+ of trades with no paperwork at all.
Hands-on in uncontrolled environments You are on your feet in hallways, cafeterias, bus lines, and toileting/hygiene routines for students with significant needs, doing physical prompting and sometimes state-certified restraint or escort holds in a building whose layout and crowd you don't control — held below the high teens only because a real share of the week is spent at a desk on IEP documents and meetings.
Licensed human required and personally liable A state special-education credential is mandatory and IDEA names you as the IEP team member whose signature and data are examined in a due-process hearing or OCR complaint, so a compliance failure — missed timeline, unimplemented accommodation — attaches to you personally; it sits at 14 not 18 because the district and LEA representative carry the financial exposure and defend the case.
Exists to be accountable for ambiguous calls You decide in the moment whether a behavior is escape-motivated or a genuine skill deficit, whether to pull a student from inclusion, whether a 13-year-old's refusal is defiance or a manifestation of disability under a manifestation determination review, and whether present-levels data justify exiting a service — calls made with incomplete information that alter a child's placement and are litigated later.
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 (14/20 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 (14/20) is whether the law requires a licensed human to sign. Trust premium (17/20) is whether buyers specifically pay for a person. Judgment and accountability (16/20) 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 47 of this occupation's 76 points (62%).
Embodiment (15/20) 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.
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 86/100, still SAFE.
State education agency guidance or an OSEP Dear Colleague letter explicitly stating that AI-generated IEP content is not compliant unless the certified case manager personally authors and attests to individualization — several states (e.g., California, Ohio, North Carolina) have issued AI-in-schools guidance that could be tightened into an attestation requirement, and due-process hearing officers already treat boilerplate IEPs as procedural violations
Task-mix shift: once goal-drafting, progress-report generation, and accommodation checklists are AI-assisted, the residual role concentrates on behavior intervention plans, crisis de-escalation, FBA observation, and contested IEP facilitation — the tier AI cannot observe or perform. Watch for districts reallocating freed paperwork time to caseload increases rather than to judgment work, which would blunt this
Parent-side pressure: IEP advocacy organizations and settlement agreements increasingly specify a named human case manager and in-person meeting attendance; districts under compensatory-services orders are already required to deliver instruction by a certified person rather than software
A published due-process or OCR decision finding a district denied FAPE because AI-drafted goals were not individualized, making named-teacher authorship a documented liability point in district policy and insurance riders
Union contract or state rule naming the special education case manager as the decision-maker of record for restraint/seclusion determinations and manifestation determination reviews, rather than an administrator — restraint-reporting laws are expanding in multiple states
The limit. Already 76 and near the practical ceiling: embodiment and trust are close to maxed by IDEA's structure and the physical nature of middle-school behavior support. The real downside risk is not automation but caseload inflation — AI absorbing paperwork so one teacher covers 40 students instead of 25, which cuts headcount without changing any dimension score.
| New York-Newark-Jersey City, NY-NJ | 8,330 | $94,740 +42% |
| Dallas-Fort Worth-Arlington, TX | 5,610 | $65,870 -1% |
| Chicago-Naperville-Elgin, IL-IN | 3,190 | $77,840 +17% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 2,780 | $78,860 +18% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 2,540 | $77,050 +15% |
| Cleveland, OH | 2,490 | $79,890 +20% |
| Atlanta-Sandy Springs-Roswell, GA | 2,200 | $77,750 +16% |
| Boston-Cambridge-Newton, MA-NH | 2,150 | $81,620 +22% |
| San Jose-Sunnyvale-Santa Clara, CA | 360 | $121,070 +81% |
| Sacramento-Roseville-Folsom, CA | 350 | $110,850 +66% |
| Fresno, CA | 80 | $104,540 +56% |
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 76. 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.