SAFE
CTE teachers spend their days demonstrating welding beads, engine diagnostics, IV sticks on manikins, or knife work on a live line, and supervising teenagers around equipment that can injure them — none of which a language model or current robot does. The automatable slice is real but peripheral: syllabi, competency checklists, industry-certification study materials, safety quizzes, and grading rubrics. State teaching licensure plus an industry credential (ASE, AWS, ServSafe, CNA instructor approval) means a licensed adult must be in the room and is accountable for shop safety.
Headcount grew steadily across the period.
Median pay $61,710 → $66,270 -14.1% 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
-1.8% 103,400 → 101,500 on the projections basis
Hard to automate, but shrinking anyway
The work resists current AI, yet the BLS projects -1.8% 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.
~6,200 openings a year on average, including replacing people who leave.
TeacherEducatorInstructorShop TeacherCooking TeacherMasonry TeacherWeaving TeacherWelding TeacherBusiness TeacherComputer TeacherDrafting TeacherCarpentry TeacherKey Punch TeacherMarketing TeacherShorthand TeacherTailoring TeacherDriving InstructorVocational TeacherVocational TrainerWelding InstructorAgriculture TeacherBookkeeping TeacherComputer InstructorCosmetology Teacher
Holding it up: embodiment . Weakest point: judgment & accountability .
Mixed — a routine tier and a judgment tier Teaching a 16-year-old to strike an arc, feel when a torque wrench breaks over, or read a patient's blood pressure by hand requires standing beside them and correcting hand position in real time — but the lesson planning, Perkins V documentation, competency tracking, and certification-exam prep that eat a third of the week are already being drafted by software, which is what holds this at 13 rather than 17.
Hands-on in uncontrolled environments The classroom is a shop, kitchen, greenhouse, or clinical lab with live 220V circuits, running saws, hot oil, and pressurized gas — you are moving between stations, physically stopping a bad cut, and doing your own equipment maintenance and lockout/tagout, though it is still a school building on a bell schedule rather than a roof or a roadside, which is why it sits at 15 and not 19.
Licensed human required and personally liable You hold a state CTE credential plus the industry ticket (AWS, ASE, ServSafe, CNA program approval) and you personally sign off that a student is competent to test — and when a student loses a fingertip on the table saw, the OSHA-equivalent state inspection and the district's incident review land on your supervision record, not just the principal's.
Meaningful discretion You decide daily whether a specific teenager is ready to run the CNC unsupervised, whether a shaky student should be pulled from clinicals before they touch a real patient, and how to fail someone on a safety competency without ending their pathway — real calls with bodily consequences, but bounded by state frameworks and industry certification standards that define the endpoint, which caps it at 13.
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 (13/20) is whether the law requires a licensed human to sign. Trust premium (15/20) is whether buyers specifically pay for a person. Judgment and accountability (13/20) 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 41 of this occupation's 69 points (59%).
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 83/100, still SAFE.
State boards and industry credentialers tightening the rule that a credentialed instructor of record must be physically present and personally sign off on each student's competency hours — e.g. AWS SENSE and NCCER instructor-of-record requirements, state CNA programs requiring an RN instructor at a fixed student:instructor ratio, and OSHA-aligned shop supervision ratios written into district policy. Any codification of named-instructor liability for shop injury raises this.
Task-mix shift: as lesson plans, quiz banks, cert prep and grading are absorbed by AI tools, the remaining day is safety judgment calls, deciding whether a given teenager is ready to run the machine unsupervised, and signing competency attestations — the consequential-call tier. Also, formal designation as the person who authorizes student work-based learning placements under state WBL rules.
Continued expansion of clinical/shop hour mandates for dual-credit and industry certifications (welding bend tests, live-fire kitchens, patient-care clinicals) that cannot be logged via simulator — if state agencies keep refusing to let VR/simulator hours substitute for hands-on hours, the physical core is protected.
Employer-sponsored apprenticeship pipelines (registered apprenticeship, local union JATCs, manufacturer academies like Snap-on/Lincoln Electric or Toyota T-TEN) paying for a named human instructor with journeyman standing whose recommendation carries hiring weight — buyers here are employers who explicitly want a vouching human.
Expansion of the role into non-instructional duties AI does not do: advisory-board relationship management, employer placement, Perkins V compliance and equipment procurement — these are already in many CTE job descriptions and grow as content delivery is offloaded.
The limit. Realistic ceiling is high but the constraint is fiscal, not technological: CTE programs are cut for cost per pupil, equipment expense and enrollment, not because software replaced the teacher. A high register score does not protect against program closure or consolidation of shop courses into one regional center.
| Dallas-Fort Worth-Arlington, TX | 8,750 | $66,450 +0% |
| Houston-Pasadena-The Woodlands, TX | 6,070 | $67,200 +1% |
| New York-Newark-Jersey City, NY-NJ | 5,030 | $99,830 +51% |
| San Antonio-New Braunfels, TX | 2,140 | $62,390 -6% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 2,070 | $80,070 +21% |
| Boston-Cambridge-Newton, MA-NH | 1,730 | $101,250 +53% |
| Austin-Round Rock-San Marcos, TX | 1,720 | $62,780 -5% |
| Chicago-Naperville-Elgin, IL-IN | 1,640 | $98,170 +48% |
| Fresno, CA | 260 | $105,570 +59% |
| Olympia-Lacey-Tumwater, WA | 130 | $103,580 +56% |
| Bremerton-Silverdale-Port Orchard, WA | 120 | $103,240 +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 69. 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.