EXPOSED
The core of this job — standing in a studio adjusting a student's bow arm, running a scene until an actor finds the beat, critiquing a canvas in front of the class — is embodied, real-time coaching that AI cannot deliver. What AI does erode is the surrounding load: syllabus construction, lecture slides on art history, rubric-based written feedback, and grant/assessment paperwork. The bigger threat is not the model but enrollment decline and adjunctification in arts departments, which shrinks the number of people who get to do this work at all.
Roughly flat across the period, with year-to-year wobble.
Median pay $69,530 → $78,620 -9.5% 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
+1.7%
Percentage only. The projection counts a different population from the 93,560 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 +1.7% more of these jobs by 2034, and at 58/100 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.
~9,000 openings a year on average, including replacing people who leave.
ProfessorInstructorArt TeacherArt EducatorBand TeacherArt ProfessorBaton TeacherChoir TeacherDrama TeacherOrgan TeacherPiano TeacherVocal TeacherVoice TeacherArt InstructorBallet TeacherChoral TeacherDramatic CoachGuitar TeacherMusic EducatorMusic LecturerViolin TeacherDance ProfessorDancing TeacherDrama Professor
Holding it up: trust premium . Weakest point: liability shield .
Tasks largely resist digitisation Diagnosing why a singer's soft palate is collapsing, demonstrating a phrase and having the student mirror it, or running a scene four times until the actor stops playing the result — these are real-time perceptual judgments made from sound, posture and breath in the room, which is why the score sits at 14 rather than higher: the art-history survey lecture, the syllabus, the rubric comments on a reflection paper and the accreditation assessment binder are all genuinely draftable by a model.
Hands-on in uncontrolled environments The studio, kiln room, scene shop and rehearsal hall are the workplace — you are physically repositioning a wrist on a fingerboard, handling wet clay and solvents, blocking bodies on a stage, running lighting cues — which puts this above lab or classroom teaching, but at 13 rather than 18 because the spaces are institutional, scheduled and indoors, not the uncontrolled sites a field geologist or lineworker deals with.
No licence, no signature requirement No state licence gates postsecondary studio teaching; an MFA plus a performance or exhibition record is the credential, and hiring committees can and do appoint working artists as adjuncts with no teaching certification at all, so nothing legally reserves the classroom to you.
Meaningful discretion You decide who gets cast, who advances from the sophomore review, whose portfolio goes to the juried show, and how to grade work with no answer key — calls that redirect a student's career and that you defend to the student, the parent and the dean, though within departmental jury structures and grade-appeal procedures that keep it at 13 rather than a solo high-stakes 17.
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 (2/20) is whether the law requires a licensed human to sign. Trust premium (16/20) 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 31 of this occupation's 58 points (53%).
Embodiment (13/20) 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 69/100 — SAFE.
Accreditation-backed 'human-taught studio hours' requirements: NASM/NAST/NASD (the national arts accrediting associations) already specify contact-hour and applied-lesson minimums for degree programs; an explicit rule that AI-mediated instruction cannot count toward applied studio/private lesson hours would harden the human premium for conservatory-style teaching. Also visible in motion: faculty-union contract language (e.g., AAUP and CFA chapters, and the 2023 WGA/SAG precedent spreading to campus bargaining) barring AI-generated instructional content from replacing course sections.
Task-mix shift: the occupation genuinely has two tiers. If slide decks, art-history lecture content, rubric-scored written critique, and assessment paperwork are fully absorbed, the residual day is live studio coaching, jury/portfolio evaluation, and casting/repertoire decisions — none of which current models perform. This raises the share of the day AI cannot do without any new law.
Formalized gatekeeping accountability: if institutions make named faculty individually responsible for signed juried decisions — audition and portfolio admissions, degree recital pass/fail, MFA thesis defense outcomes — with documented appeal processes naming the deciding faculty member (a direction Title IX-style due-process reform and student grade-appeal litigation is pushing), the ambiguous consequential call becomes explicitly owned.
Shop- and safety-supervision mandates in studio disciplines: OSHA-driven and campus-insurer requirements for a trained human present when students operate kilns, welding equipment, printmaking chemicals, table saws in scene shops, or rigging in theater spaces. Insurer refusal to cover unsupervised studio access is the concrete mechanism.
The limit. The binding constraint is not model capability but seat count. Enrollment decline in arts majors and adjunctification mean the score can rise while the number of people holding the job falls; a high trust premium on studio teaching does not protect headcount if departments are consolidated or closed.
| New York-Newark-Jersey City, NY-NJ | 13,080 | $100,950 +28% |
| Los Angeles-Long Beach-Anaheim, CA | 5,200 | $91,490 +16% |
| Boston-Cambridge-Newton, MA-NH | 3,820 | $96,170 +22% |
| Chicago-Naperville-Elgin, IL-IN | 2,950 | $62,960 -20% |
| Dallas-Fort Worth-Arlington, TX | 1,770 | $76,810 -2% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 1,750 | $78,380 +0% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 1,200 | $80,190 +2% |
| Houston-Pasadena-The Woodlands, TX | 1,100 | $96,050 +22% |
| Riverside-San Bernardino-Ontario, CA | 560 | $163,750 +108% |
| Fresno, CA | 170 | $135,970 +73% |
| Visalia, CA | 40 | $128,910 +64% |
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 58. 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.