EXPOSED
Education professors split their week between lecturing on pedagogy, grading reflective papers and lesson plans, advising teacher candidates, observing student teachers in K-12 classrooms, and writing accreditation reports — and AI already drafts syllabi, rubrics, discussion prompts, feedback on lesson plans, and literature reviews at usable quality. What holds is the embodied part: sitting in the back of a third-grade classroom watching a candidate manage 25 kids, then coaching them on what went wrong, plus the mentorship relationship districts and licensure boards rely on. The modal worker here is contingent or non-tenure-track faculty, so institutional cost pressure — larger sections, more asynchronous delivery — is a bigger near-term threat than the model itself.
Roughly flat across the period, with year-to-year wobble.
Median pay $65,510 → $75,350 -8.0% 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
+2.1%
Percentage only. The projection counts a different population from the 60,830 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Growing, and only partly exposed
The BLS expects +2.1% more of these jobs by 2034, and at 50/100 the work is only partly exposed — some tasks are automatable, the core of the job is not. Nothing here is in tension.
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.
~5,600 openings a year on average, including replacing people who leave.
EducatorLecturerProfessorInstructorFaculty MemberAdjunct LecturerCollege ProfessorEducation TeacherAdjunct InstructorAssistant ProfessorAssociate ProfessorEducation ProfessorEducation InstructorEducational InstructorTenure-Track ProfessorEducation Faculty MemberVisual Education TeacherMusic Education ProfessorAdjunct Education ProfessorEducation Adjunct ProfessorPrimary Education ProfessorScience Education ProfessorSpecial Education ProfessorLiteracy Education Professor
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier The lecture-plus-rubric-plus-literature-review half of the week is already drafted competently by a model, but the other half — clinical placement supervision, live observation cycles with pre-conference and post-conference debriefs, and CAEP/state program-review evidence that must be tied to actual candidate performance — has no digital substitute yet, which is what puts this at 11 rather than down at 5 with lecture-only humanities faculty.
Some physical or field component Field supervision means driving to elementary and secondary schools several times a semester, sitting in a classroom you don't control, and reading a candidate's proximity, voice, and transition management in real time — real physical presence, but it's a few hours a week in K-12 buildings rather than the daily uncontrolled-site work of a nurse or electrician, hence 10 and not 16.
No licence, no signature requirement No state licence gates the job of teaching education courses; a doctorate and, in some programs, a lapsed K-12 credential are hiring preferences, and when a program loses accreditation or a graduate fails licensure, the consequence lands on the institution and the dean, not on your personal certificate — the 3 reflects that even the state approval process names the program, not you.
Meaningful discretion Deciding whether a struggling candidate is remediated, removed from placement, or recommended for licensure is a genuinely contestable call with a child's future classroom on the other side of it, but you make it inside program rubrics, disposition checklists, and state approval standards with a committee or chair signing off, which caps it at 11 rather than the high-teens of someone who owns the decision alone.
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 (11/20 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 (3/20) 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 (11/20) 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 29 of this occupation's 50 points (58%).
Embodiment (10/20) 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 67/100 — SAFE.
Genuine two-tier structure: syllabi, rubrics, lit reviews, and written feedback on lesson plans are the routine tier and are already being absorbed. If those go, the residual job is live classroom observation, coaching a candidate mid-crisis, and accreditation defense under site visit — harder tasks. The rise is real but partly offset by section consolidation.
Districts hiring from programs, and the mentor-teacher network, buy the named supervisor's relationship and judgment about a candidate. This is a real premium but it is paid by institutions, not students, and it does not protect the lecture-and-grade portion.
State licensure boards and CAEP/ AAQEP accreditation standards already require named, qualified faculty to sign off on clinical practice hours and teacher-candidate recommendations for licensure. If boards tighten this — e.g. a state rule that only a credentialed education faculty member with in-person observation hours may certify a candidate's clinical competency, and that AI-generated observation summaries cannot substitute — the personal sign-off becomes a hard gate rather than a soft norm. Watch state boards of education revising edTPA-replacement or clinical-hour rules.
Gatekeeping decisions — recommending or denying a candidate for licensure, removing a candidate from placement after a classroom safety incident — are consequential, contested, and appealable. If institutions formalize these as documented faculty-of-record determinations subject to due-process review (as some programs already do after litigation over dismissed candidates), the ambiguity-ownership becomes explicit rather than diffuse.
If states raise required in-person clinical supervision hours or cap remote/video observation (several states expanded video observation during COVID and have been rolling it back), the unavoidable in-classroom share of the week grows.
The limit. The binding constraint is not model capability, it is that most of these workers are contingent. Every lever above protects the ROLE (in-person clinical supervisor of record) while the institution can still cut the number of people filling it via larger cohorts and asynchronous coursework. A program can satisfy a tightened licensure sign-off rule with a third of the current faculty. Realistic ceiling around 60-65.
| New York-Newark-Jersey City, NY-NJ | 5,120 | $80,640 +7% |
| Chicago-Naperville-Elgin, IL-IN | 2,650 | $61,350 -19% |
| Boston-Cambridge-Newton, MA-NH | 1,680 | $79,980 +6% |
| Dallas-Fort Worth-Arlington, TX | 1,450 | $61,970 -18% |
| Los Angeles-Long Beach-Anaheim, CA | 1,330 | $105,730 +40% |
| Phoenix-Mesa-Chandler, AZ | 1,180 | $78,520 +4% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 1,150 | $76,450 +1% |
| Houston-Pasadena-The Woodlands, TX | 1,120 | $78,510 +4% |
| San Diego-Chula Vista-Carlsbad, CA | 160 | $129,100 +71% |
| Riverside-San Bernardino-Ontario, CA | 240 | $123,910 +64% |
| San Jose-Sunnyvale-Santa Clara, CA | 430 | $121,520 +61% |
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 50. 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.