← Risk register SOC 25-3099 · reviewed 2026-08-11

Teachers and Instructors, All Other

113,790 US workers · median $66,140/yr · Education

EXPOSED

This is a catch-all bucket — driving instructors, adult education and GED instructors, test-prep and enrichment tutors, community-ed and skills instructors — so the modal worker teaches live to small groups or one-on-one outside the licensed K-12 and postsecondary systems. Curriculum writing, worksheet and quiz generation, slide decks, grading, and one-on-one explanation of standard material are exactly what chatbots and adaptive tutoring platforms now do at near-zero marginal cost, which hollows out the paid-tutoring tier hardest. What survives is the in-room work: keeping a reluctant teenager behind the wheel, motivating an adult who dropped out at 16, physically correcting form, and being the person a learner shows up for.

10-year outlook: The materials-and-explanation half of this work gets absorbed by cheap AI tutoring within a decade, while instructors tied to vehicles, tools, bodies, and pass-rate accountability keep steady demand.

US employment, 2021–2025-30.9%
164,650113,790 workers

Most of this decline happened after 2021 — it is not the pandemic dip.

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.

BLS projection, 2024–2034

-0.1%

Percentage only. The projection counts a different population from the 113,790 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.

Shrinking, but not obviously because of AI

The BLS projects -0.1% by 2034, but at 48/100 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.

~18,000 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 — 17 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.

TutorTeacherLecturerAutism TutorAdjunct TeacherVisiting TeacherBilingual TrainerSubstitute TeacherBilingual InstructorLink Trainer OperatorSunday School TeacherEnvironmental EducatorExtension Work InstructorConsumer Education SpecialistSpecial Education Preschool TeacherHuman Resource Management InstructorSpecial Education Teacher for Adults with Disabilities

This is a catch-all code, not a single job

The BLS uses Teachers and Instructors, All Other for work that doesn't fit any named occupation, so it covers roles that have little in common with each other. Two consequences worth knowing before you read anything below:

If a more specific occupation on the register describes what you actually do, that page is the one to trust.

Score — 48/100 resistance

Holding it up: trust premium (13/20). Weakest point: liability shield (4/20).

Five dimensions, 0–20 each, summed. Higher means more protected. The arithmetic is shown so you can check it: 10 + 12 + 4 + 13 + 9 = 48. · Scored 2026-08-11, and re-examined when evidence accumulates rather than on a schedule.

Task resistance 10/20

Mixed — a routine tier and a judgment tier Lesson planning, quiz-writing, and walking a student through standard GED math or SAT reading are already delivered by adaptive platforms and chatbots at near-zero cost, but the pacing decisions, re-explaining when a learner stalls, and holding an adult's attention through a two-hour night class are not, which splits the job roughly down the middle rather than leaving it mostly intact.

Embodiment 12/20

Some physical or field component A driving instructor sits in the passenger seat with a dual brake in live traffic and a welding or CPR instructor corrects grip and posture by hand, but a large share of this bucket — test prep, ESL conversation, adult basic ed — runs fine over Zoom from a rented classroom, so the physical requirement is real for a subset rather than universal.

Liability shield 4/20

No licence, no signature requirement Most of this bucket needs no teaching licence: state driver-training certificates and program-specific credentials exist, but a community-ed instructor or private tutor can be hired on subject knowledge alone, and no statute makes them personally answerable for a learner's outcome the way a licensed professional signs off on work.

Trust premium 13/20

The human relationship is the product Adult learners who failed once in school and teenagers who are frightened of the road come back for a specific person, not a curriculum — retention in test prep and adult ed tracks the instructor's name and referrals — but placement is often through a school, dealership, or district program that controls the client, which keeps this short of the fully portable book of business a private coach holds.

Judgment & accountability 9/20

Meaningful discretion Instructors decide when a student is road-ready, when to move a class off the syllabus, and when a learner's silence is confusion or something worse, but they work inside curricula, state driver-ed hour requirements, and standardized test frameworks that set most of the calls in advance.

Confidence: medium · 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, trust, physical-presence

How to future-proof this job

All 35 skills ranked by how many jobs they open →

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 69/100 — SAFE.

6 specific changes that would raise this score
  • already happening liability shield +5

    State DMV/licensing rules that require a state-certified driving instructor to be physically present and to sign the behind-the-wheel completion certificate that unlocks a provisional license — already the structure in most states (e.g. California DMV secondary-license driving instructor certification, Texas TDLR driver education instructor licensing). Extension of similar sign-off mandates to other regulated instruction (OSHA 10/30 authorized trainers, ServSafe proctors, flight/CDL and forklift certifiers) where an AI-issued credential is explicitly not accepted would raise this further.

  • already happening task resistance +4

    Task-mix shift: as chatbots absorb worksheet generation, standard-material explanation and grading, the paid hours that remain are the ones AI cannot do — in-vehicle risk coaching, re-engaging an adult who dropped out at 16, hands-on form correction. This occupation genuinely has two tiers, and the routine tier is the one already collapsing; the surviving job scores higher on resistance even as total headcount falls.

  • already happening embodiment +2

    Growth in the share of hours spent in genuinely unpredictable physical settings — dual-control vehicles in traffic, shop floors, kitchens, pools — as the classroom-and-worksheet share is automated. No new rule needed; it is composition change, and it is capped because much of this bucket is still seated instruction.

  • plausible judgment accountability +4

    Instructor-of-record duty for readiness calls with real consequences: declaring a student road-test-ready, signing off that a trainee may operate a forklift or scaffold, or flagging a learner as unsafe. If insurers of driving schools or trade-training providers require documented named-instructor judgment on each pass/fail (as some commercial auto and E&O carriers do), the role owns a consequential ambiguous call.

  • plausible liability shield +3

    Test-integrity rules from credentialing bodies (GED Testing Service, Pearson VUE, state adult-ed offices) requiring a named human proctor personally accountable for identity verification and irregularity reports, with remote-AI-only proctoring disallowed after cheating scandals.

  • plausible trust premium +3

    Buyers who pay specifically for a human accountability relationship — parents purchasing behind-the-wheel packages, adults paying for a coach they show up for. A visible route up is contract structures where the fee is explicitly for scheduled human contact hours (workforce-board and WIOA-funded adult ed reimbursing per human instructional hour, not per completion), which prices out AI-only providers.

The limit. The ceiling is low because this is a catch-all: the levers are real for the licensed sub-slices (driving, safety, trade certification) and nearly absent for the test-prep and enrichment tutors, who make up a large share of the bucket and face no licensure, no sign-off, and no buyer willing to pay a premium for a human explaining algebra. Any aggregate score improvement is likely to come from the low-shield tier disappearing rather than from workers in it gaining protection.

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 239 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

Los Angeles-Long Beach-Anaheim, CA 8,300 $97,680 +48%
Atlanta-Sandy Springs-Roswell, GA 7,420 $58,870 -11%
Washington-Arlington-Alexandria, DC-VA-MD-WV 3,730 $97,920 +48%
San Francisco-Oakland-Fremont, CA 3,180 $79,250 +20%
San Diego-Chula Vista-Carlsbad, CA 3,100 $80,460 +22%
Sacramento-Roseville-Folsom, CA 3,070 $81,720 +24%
New York-Newark-Jersey City, NY-NJ 2,910 $77,260 +17%
Baltimore-Columbia-Towson, MD 2,810 $99,430 +50%

Best paid

Providence-Warwick, RI-MA 430 $128,150 +94%
Kiryas Joel-Poughkeepsie-Newburgh, NY 360 $124,490 +88%
Salinas, CA 2,170 $113,360 +71%

Percentages are against this occupation's national median of $66,140. 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 48. 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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Kept current

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