SAFE
The paperwork half of this job — drafting IEP goal language, progress-monitoring summaries, accommodation checklists, transition-plan boilerplate — is exactly what current AI drafts competently, and that is real relief for a role notorious for after-hours documentation. The other half is physically present work with teenagers who have autism, emotional disturbance, or intellectual disabilities: de-escalating a crisis in a hallway, running behavior-intervention plans, hand-over-hand skill instruction, and co-teaching in a general-ed classroom. IDEA gives the IEP legal force and states require a licensed special educator on the team, so a human signs and is answerable to parents and due-process hearings.
Headcount grew steadily across the period.
Median pay $61,710 → $74,260 -3.7% 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
-1.6% 164,200 → 161,500 on the projections basis
Hard to automate, but shrinking anyway
The work resists current AI, yet the BLS projects -1.6% 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.
~11,100 openings a year on average, including replacing people who leave.
TeacherDeaf TeacherBlind TeacherBraille TeacherInterventionistResource TeacherSpecial EducatorInclusion TeacherElementary TeacherReading SpecialistTransition TeacherBehavior SpecialistHandicapped TeacherLearning SpecialistLife Skills TeacherLip Reading TeacherResource SpecialistEducation SpecialistHome Therapy TeacherInclusion SpecialistEarly InterventionistInclusion CoordinatorPublic School TeacherSign Language Teacher
Holding it up: trust premium . Weakest point: task resistance .
Mixed — a routine tier and a judgment tier At 13 rather than 16, the split is honest: a model can generate the annual goals, the quarterly progress narratives, and the transition-services language, but nobody automates a 45-minute co-taught geometry block where you're re-teaching in the moment, or a functional behavior assessment that requires you to watch a student across four settings to find the antecedent.
Hands-on in uncontrolled environments 14 reflects that the room is not controlled — hallway crises, physical prompting for life-skills and vocational tasks, restraint or escort under state training protocols, community-based instruction on public transit — but it stays below the 17-plus band because a meaningful share of your week is still IEP meetings and desk-bound documentation.
Licensed human required and personally liable State special-education licensure plus IDEA's requirement that a qualified provider deliver the services means your name is on the IEP that a hearing officer can rule inadequate, and a compliance failure follows your certificate — the 14 rather than 18 is because the district and the LEA representative absorb the financial and legal exposure, not you personally.
Exists to be accountable for ambiguous calls 15 covers the calls with no procedure to hide behind: whether a behavior is a manifestation of disability, when to recommend a more restrictive placement, whether a 17-year-old goes to a diploma track or a certificate, and when a report to child protective services is warranted — each defensible only through your documented reasoning.
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 (13/20 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 (15/20) 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 46 of this occupation's 73 points (63%).
Embodiment (14/20) 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 83/100, still SAFE.
State education agency guidance or an IDEA reauthorization provision explicitly requiring that AI-generated IEP content be reviewed and signed by the named licensed special educator, with the signature admissible in due-process hearings — several states (e.g. California, New York) have already issued AI-in-education guidance touching on IEP drafting, and OSEP has been asked for clarification on whether AI-drafted IEPs satisfy the 'individualized' requirement.
Genuine two-tier structure: if AI absorbs goal-bank drafting, progress summaries and transition boilerplate, the residual day is crisis de-escalation, behavior-intervention fidelity, parent conflict, and co-teaching negotiation — none currently automatable. No law needed; this is task-mix shift.
A due-process or OCR ruling finding a district out of compliance because an IEP was substantially AI-generated without individualized human determination, prompting districts and their insurers to mandate documented educator authorship of goals and placement decisions.
Restraint-and-seclusion reporting laws (expanding in states like Illinois and Texas) plus manifestation-determination review requirements naming the special educator as an accountable decision-maker on discipline and placement calls concentrate the role's consequential-call ownership as documentation is automated.
Parent-advocacy pressure (COPAA, disability-rights groups) producing district commitments that IEP meetings be conducted by humans and that parents can refuse AI-drafted plan content — trust premium here is exercised by parents through due-process leverage rather than purchase.
The limit. Already high; headroom is small. Trust premium is near ceiling because the buyer is a public district under legal obligation, not a discretionary purchaser. Embodiment cannot rise — the physical work is already the bulk of the non-paperwork day. Downward pressure exists too: staffing shortages and paraprofessional substitution could dilute the licensed-signer requirement in practice even if it stays on paper.
| New York-Newark-Jersey City, NY-NJ | 14,700 | $100,430 +35% |
| Chicago-Naperville-Elgin, IL-IN | 7,360 | $82,350 +11% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 5,790 | $79,760 +7% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 3,930 | $85,000 +14% |
| Boston-Cambridge-Newton, MA-NH | 3,920 | $87,630 +18% |
| Los Angeles-Long Beach-Anaheim, CA | 3,920 | $100,900 +36% |
| Dallas-Fort Worth-Arlington, TX | 2,670 | $66,440 -11% |
| Minneapolis-St. Paul-Bloomington, MN-WI | 2,590 | $79,190 +7% |
| San Jose-Sunnyvale-Santa Clara, CA | 400 | $117,640 +58% |
| San Diego-Chula Vista-Carlsbad, CA | 1,220 | $115,160 +55% |
| San Francisco-Oakland-Fremont, CA | 1,260 | $105,160 +42% |
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 73. 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.