EXPOSED
The screen half of this job — building syllabi, writing lecture slides on exercise physiology, generating quiz banks, grading written assignments, drafting program review documents — is already well within reach of current AI. The half that holds is embodied: demonstrating and correcting movement technique in the gym or pool, supervising practicum and internship placements, spotting unsafe form, and coaching students through certification exams and career entry. There is no licensure requirement for the professor role, so the moat is physical presence and mentorship, not regulation; adjunct-heavy staffing and enrollment pressure in this small field are bigger near-term threats than AI itself.
Part 2020 shock, part continued decline in the years since.
Median pay $64,380 → $77,270 -4.0% 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
+2.4%
Percentage only. The projection counts a different population from the 12,630 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 +2.4% more of these jobs by 2034, and at 48/100 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.
~1,100 openings a year on average, including replacing people who leave.
TeacherProfessorInstructorExercise TeacherHealth ProfessorAdjunct ProfessorCollege ProfessorGymnasium TeacherAdjunct InstructorAquatic InstructorFitness InstructorRecreation TeacherAssistant ProfessorAssociate ProfessorAthletic InstructorExercise InstructorWellness InstructorRecreation ProfessorKinesiology ProfessorCollege Faculty MemberFitness Studies TeacherPhysical Fitness TeacherLeisure Studies ProfessorUniversity Faculty Member
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Half your week — the kinesiology lecture on the Krebs cycle, the quiz bank on anatomical planes, the rubric-scored reflection papers from a practicum journal, the CAAHEP self-study documents — is text generation that AI drafts credibly, while the other half, running a lab where students take skinfold measurements and VO2 max readings on each other and correcting a student's squat depth in real time, does not compress into a screen, which is what puts this at 10 rather than 6 or 14.
Some physical or field component You are on a gym floor, in a pool, or in a biomechanics lab several times a week — spotting a bench press, cueing hand position, running lifeguard or CPR practicals — but it is a controlled campus facility with mats, known equipment, and pre-screened students, not a job site or a patient's home, so this lands mid-scale rather than at the 16+ of athletic trainers on a field or firefighters.
No licence, no signature requirement Nothing in the professor role requires a licence: your ACSM, NSCA-CSCS, or NASM credential is what you teach students to earn, not what the institution legally requires of you to teach, and the 3 reflects only that accreditation reviews and the institution's insurance care whether a credentialed person supervises the practical lab.
Meaningful discretion You decide whether a student is safe to be turned loose supervising a client at a practicum site, whether a shoulder complaint means stopping a lab, and how to weigh a borderline internship evaluation — real calls with injury and licensure-exam consequences, but bounded by department curricula, accreditation competency lists, and institutional risk-management protocols rather than made from scratch.
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 (10/20 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 (13/20) is whether buyers specifically pay for a person. Judgment and accountability (10/20) 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 26 of this occupation's 48 points (54%).
Embodiment (12/20) 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 63/100, still EXPOSED.
Task-mix shift: once slide decks, quiz banks and rubric grading are AI-generated, the residual load is live skills assessment, practicum placement negotiation with clinical sites, and remediation of students who fail hands-on competency — genuinely two-tiered work in this field
State athletic-trainer or exercise-physiologist licensure expanding to require that clinical/practicum hours be signed off by a credentialed supervisor of record who is personally accountable for student scope-of-practice violations — analogous to the clinical preceptor sign-off already required in nursing and AT education. A handful of states license exercise physiologists (Louisiana) and licensure bills recur
Accreditation bodies (CAAHEP for exercise science, NSCA/ACSM program endorsement) tightening required in-person supervised practicum hours and requiring a named faculty member physically present for hands-on skills assessment (goniometry, VO2max testing, spotting) rather than video-submitted competency checks — several programs loosened this post-2020 and reversal is under discussion in CAAHEP standards revisions
Formal faculty gatekeeping authority over practicum progression — a documented decision to bar a student from an internship or clinical site for unsafe technique or professionalism concerns, with the faculty member owning the appeal, as is standard in nursing and AT programs and increasingly written into kinesiology program handbooks
Employers and certification bodies (NSCA CSCS, ACSM) restricting exam eligibility or preferred-program status to degrees with verified in-person faculty-supervised instruction, making the human-taught credential the marketable one
The limit. Even with every lever, the binding constraint is enrollment and adjunctification in a small field: institutions can meet tightened in-person supervision requirements with low-paid part-time instructors, which protects the task but not the occupation's headcount or pay.
| Los Angeles-Long Beach-Anaheim, CA | 770 | $106,660 +38% |
| New York-Newark-Jersey City, NY-NJ | 750 | $78,730 +2% |
| Chicago-Naperville-Elgin, IL-IN | 330 | $61,880 -20% |
| Dallas-Fort Worth-Arlington, TX | 310 | $79,310 +3% |
| Portland-Vancouver-Hillsboro, OR-WA | 280 | $73,840 -4% |
| Houston-Pasadena-The Woodlands, TX | 240 | $82,100 +6% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 240 | $83,600 +8% |
| San Francisco-Oakland-Fremont, CA | 200 | $134,190 +74% |
| Riverside-San Bernardino-Ontario, CA | 170 | $159,690 +107% |
| Fresno, CA | 60 | $137,850 +78% |
| San Francisco-Oakland-Fremont, CA | 200 | $134,190 +74% |
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.
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.