EXPOSED
The document half of this job — drafting brochures, translating materials to plain language, writing curricula, summarizing survey data, assembling grant narratives and program reports — is exactly what generative AI does cheaply and at volume. What holds is the in-person, relationship-carrying half: facilitating workshops in churches, clinics, schools and shelters, running health fairs, and being the trusted local face who gets a skeptical community to show up. CHES/MCHES certification is preferred by employers but almost never legally mandated, so there is no licensure moat, and most positions sit in grant-funded public health and hospital budgets that are politically fragile.
Dipped in 2020, then grew past where it started.
Median pay $55,220 → $64,070 -7.2% 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
+4.5% 71,800 → 75,000 on the projections basis
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +4.5% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
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.
~7,900 openings a year on average, including replacing people who leave.
EducatorInstructorHealth CoachFitness CoachNurse EducatorHealth EducatorHealth ScreenerClinical EducatorDiabetes EducatorCommunity EducatorNutrition EducatorClinical InstructorHealthcare EducatorEducation SpecialistLactation ConsultantLactation SpecialistEducation CoordinatorHealthcare SpecialistPublic Health AdvisorPublic Health AnalystPublic Health OfficerBreastfeeding EducatorPublic Health EducatorClinical Nurse Educator
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier At 9 the split is roughly even: needs assessments, curriculum drafts, plain-language rewrites at 6th-grade reading level, BRFSS/YRBS data summaries and grant progress reports are now first-draft work for a model, while running an eight-week diabetes self-management series in a church basement, recruiting promotores, and staffing a health fair table still require a person in the room — which is why this sits above a pure documentation role but well below a home visitor.
Some physical or field component An 8 reflects that the workshops, screenings, school assemblies and mobile-unit outreach happen in borrowed spaces you don't control — church halls, shelters, county fairgrounds, with your own boxes of BP cuffs and handouts — but you are not performing clinical procedures or entering unsafe structures, and a growing share of your hours are spent at a desk on Canva, Qualtrics and the county's reporting portal.
No licence, no signature requirement A 3 is what CHES/MCHES actually buys: it appears in job postings and NCHEC exam eligibility rules, but no state practice act reserves health education to certificate holders, nothing you produce requires your credential number on it, and when a program's claims go wrong the liability lands on the health department, hospital, or the licensed clinician who reviewed the content.
Meaningful discretion An 8 fits work bounded by evidence-based interventions from the CDC Compendium, IRB protocols, and grant workplans you must implement with fidelity, while still requiring real calls on how to adapt a curriculum for a distrustful community, when a participant's disclosure triggers a mandated report or referral, and which of three neighborhoods gets the remaining outreach dollars.
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 (9/20 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 (11/20) is whether buyers specifically pay for a person. Judgment and accountability (8/20) 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 22 of this occupation's 39 points (56%).
Embodiment (8/20) 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 55/100, still EXPOSED.
Health plans and health systems paying specifically for in-person, community-embedded outreach because AI-written outreach measurably fails on engagement — visible already in Medicaid managed care contracts and NCQA health equity accreditation crediting community engagement staff, and in HEDIS/Star ratings where measures depend on getting people to actually show up. If plans start requiring named local staff rather than digital campaigns, the human is what's being bought.
Task-mix shift: this role genuinely has two tiers. If brochure drafting, plain-language translation, curriculum boilerplate and grant narrative assembly are absorbed by AI, what remains is workshop facilitation, coalition building with churches/schools/shelters, handling vaccine or reproductive-health hostility in a live room, and adapting on the fly — none of which current models do. The residual job is harder to automate than the average of today's job.
State or federal grant conditions requiring a CHES/MCHES-credentialed staff member to sign off on health education curricula and materials — e.g. CDC or HRSA notices of funding opportunity naming certified health education specialist FTEs as a required budget line, or state Medicaid community health worker/health education benefit rules (as in Michigan, California CalAIM) tying reimbursement to a credentialed supervisor. Also plausible: hospital community benefit reporting (IRS Schedule H) or Joint Commission patient-education standards requiring a named credentialed reviewer for AI-generated patient materials.
Formal designation as the accountable party for community health needs assessment priorities or outbreak/misinformation response — e.g. state public health accreditation (PHAB) standards naming a responsible health education lead, or emergency risk-communication plans making a named specialist the decision-maker on what message goes to a distrustful population during an outbreak.
The limit. The binding constraint is not capability but budget: most of these positions are grant- and community-benefit-funded, so even a strengthened credential moat does not protect headcount if public health appropriations fall. Licensure is the weakest lever here — no state has moved toward mandatory health education licensure, and CHES remains voluntary.
| Los Angeles-Long Beach-Anaheim, CA | 4,320 | $62,380 -3% |
| New York-Newark-Jersey City, NY-NJ | 3,380 | $64,110 +0% |
| Atlanta-Sandy Springs-Roswell, GA | 2,580 | $112,450 +76% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 2,100 | $124,590 +94% |
| San Francisco-Oakland-Fremont, CA | 1,850 | $99,260 +55% |
| Boston-Cambridge-Newton, MA-NH | 1,620 | $62,260 -3% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 1,270 | $75,220 +17% |
| Dallas-Fort Worth-Arlington, TX | 1,140 | $64,050 +0% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 2,100 | $124,590 +94% |
| Atlanta-Sandy Springs-Roswell, GA | 2,580 | $112,450 +76% |
| Trenton-Princeton, NJ | 60 | $102,680 +60% |
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 39. 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.