EXPOSED
Much of this job — writing lecture slides, building syllabi, drafting quiz banks, grading short-answer work, summarizing nutrition or child-development literature — is already within reach of current AI. What holds is the hands-on tier: supervising foods labs and test kitchens, teaching sewing and textiles technique, running practicum placements in childcare centers and dietetics sites, and evaluating student performance in person. The bigger threat to this small occupation (about 2,800 jobs) is program consolidation and adjunctification, not a model replacing the instructor.
Headcount grew steadily across the period.
Median pay $76,480 → $75,870 -20.6% 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
+3.4%
Percentage only. The projection counts a different population from the 2,770 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 +3.4% more of these jobs by 2034, and at 53/100 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.
~200 openings a year on average, including replacing people who leave.
LecturerProfessorInstructorSewing TeacherChef InstructorCooking TeacherAdjunct ProfessorCollege ProfessorTailoring TeacherWeaving ProfessorAdjunct InstructorAssistant ProfessorAssociate ProfessorDietetics ProfessorNutrition InstructorCollege Faculty MemberHome Economics TeacherHome Economics ProfessorNutrition Faculty MemberUniversity Faculty MemberFood and Nutrition TeacherHuman Development ProfessorChild Development InstructorFood and Nutrition Professor
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier An 11 reflects the split inside a single course: the nutrition-science lecture, the reading list, the rubric, and the online discussion prompts can be generated and graded by a model today, but nobody can watch a student debone a chicken, judge whether a seam allowance is even, or catch a food-safety violation at a steam table through a screen — and that lab tier is roughly half the contact hours in foods, textiles, and child-development practica.
Some physical or field component At 12 you are in a test kitchen with commercial ranges and a serving line, at sewing and pressing stations, and out doing site visits to childcare centers and dietetics placements — real physical presence in spaces you don't control — but you are not scoring higher because the semester still runs on a classroom, an LMS gradebook, and department meetings rather than continuous fieldwork.
No licence, no signature requirement A 4 is right because a postsecondary teaching appointment requires no practice licence: the master's or doctorate and any ServSafe or RD credential you hold are hiring criteria and program-accreditation inputs (AAFCS, ACEND), not a legal barrier — the institution carries the liability for what happens in your lab, and an adjunct with a different credential mix can be hired into your section next term.
Meaningful discretion 11 covers calls that are genuinely yours and genuinely contested — failing a student on professional conduct at a childcare site, deciding whether a food-safety lapse is teachable or removable, handling a mandated-reporter disclosure that surfaces in a child-development practicum — but curriculum content, competency lists, and grade appeals are bounded by accreditor standards and departmental policy rather than left to you.
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 (4/20) 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 30 of this occupation's 53 points (57%).
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 65/100, still EXPOSED.
Accreditation-driven expansion of supervised lab/practicum hours — e.g. ACEND's dietetics standards requiring documented in-person competency verification of food-production skills, and NAEYC-accredited child development programs requiring on-site practicum observation by qualified faculty — pushes the job's center of gravity toward test-kitchen and placement supervision that cannot be done remotely or by a model
Faculty of record designated as the credentialed supervisor-of-record for students in externship sites: ACEND preceptor/program-director qualification rules and state childcare licensing rules that name a specific qualified individual responsible for students working with minors or preparing food for the public. Also RD/RDN licensure in the ~30 states with dietetics practice acts, where instruction bleeding into individualized nutrition counseling requires a licensed human
Task-mix shift: if slide decks, quiz banks and literature summaries are automated, what remains is the consequential-call tier — signing off that a student is competent to handle food safely or be placed with children, removing a student from a practicum, and mandated-reporter judgments arising from placement supervision. Formalized competency-gate sign-offs in accreditation self-studies make this ownership explicit
Same two-tier shift: the residual job becomes lab safety management, technique correction by hand, and individualized remediation. This raises the score only if institutions retain the lab courses rather than converting the program to online general-education nutrition surveys — the more likely path for a 2,800-job occupation under consolidation pressure
The limit. The binding constraint is enrollment and program survival, not capability. FCS departments are being merged into nutrition, education, or hospitality units and taught by adjuncts; a higher per-role resistance score does not protect the headcount. Trust premium has no realistic route upward — students and accreditors buy the credential and the required contact hours, not this specific instructor.
| Los Angeles-Long Beach-Anaheim, CA | 100 | $97,020 +28% |
| New York-Newark-Jersey City, NY-NJ | 70 | $53,460 -30% |
| Greensboro-High Point, NC | 60 | $82,420 +9% |
| Raleigh-Cary, NC | 60 | $82,430 +9% |
| San Diego-Chula Vista-Carlsbad, CA | 50 | $97,860 +29% |
| Greenville, NC | 40 | $84,300 +11% |
| Salt Lake City-Murray, UT | 40 | — |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 30 | — |
| Sacramento-Roseville-Folsom, CA | 30 | $99,510 +31% |
| San Diego-Chula Vista-Carlsbad, CA | 50 | $97,860 +29% |
| Los Angeles-Long Beach-Anaheim, CA | 100 | $97,020 +28% |
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 53. 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.