EXPOSED
The informational core of tutoring — explaining a concept, working a sample problem, generating practice sets, reviewing an essay draft — is exactly what chatbots do for free, and homework-help demand is already leaking to them. What survives is the part parents actually pay for: a human who shows up weekly, notices the kid is anxious rather than confused, enforces accountability, and reports honestly to the family. Scored at the modal worker (private/agency academic tutor); credentialed special-education and dyslexia-intervention tutors sit meaningfully safer.
Headcount grew steadily across the period.
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.
BLS projection, 2024–2034
+0.6%
Percentage only. The projection counts a different population from the 175,070 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +0.6% 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.
~37,100 openings a year on average, including replacing people who leave.
TutorMath TutorPeer TutorOnline TutorAlgebra TutorBiology TutorCollege TutorEnglish TutorFinance TutorHistory TutorIn-home TutorPhysics TutorPrivate TutorReading TutorScience TutorSpanish TutorWriting TutorAcademic CoachAcademic TutorCalculus TutorLiteracy TutorAcademic MentorChemistry TutorComputing Tutor
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Explaining the quadratic formula, generating twenty more practice problems, and marking up a five-paragraph essay are the bulk of a tutoring hour and an LLM does all three on demand, which caps this at 7; the residue that holds — sitting across from a 14-year-old who says 'I get it' when he doesn't, and pacing a session against a Thursday test — keeps it out of the 0-6 band.
Some physical or field component Most sessions run at a kitchen table, a library branch, or a learning-center cubicle, with driving between clients, printed worksheets, and manipulatives for younger students — but nothing physical is being done to anything, and the same session already runs over Zoom without loss, which puts it at 8 rather than up with in-home caregivers.
No licence, no signature requirement No state licence, no exam, no continuing-education hours: an agency can hire a strong undergrad on a background check alone and Wyzant will list anyone who passes a subject quiz, so the 1 reflects that nothing legally stops a parent from replacing you with software or a cheaper tutor tomorrow.
Meaningful discretion You decide whether to reteach fractions before tackling algebra, when to tell a parent their child is behind, and how to handle a student who's clearly copying answers — real calls made alone in the room — but they're bounded by the school's syllabus and the test date, and you don't diagnose, place, or write anything that follows the child on record, so 7 sits at the floor of genuine discretion.
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 (7/20 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 (1/20) is whether the law requires a licensed human to sign. Trust premium (14/20) is whether buyers specifically pay for a person. Judgment and accountability (7/20) 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 37 points (59%).
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 50/100, still EXPOSED.
Testing bodies and school districts tightening in-person proctoring/behavior norms (e.g., College Board's Bluebook lockdown, and district AI-plagiarism policies) push families toward tutors as verifiable human accountability partners rather than content explainers; parents already pay premium for the weekly in-person adult who notices anxiety. A visible market bifurcation where agencies advertise 'no-AI, human-only, in-home' as a paid tier would show this.
Task-mix shift: once free chatbots absorb homework explanation and practice generation, the paid remainder is diagnosis of why a student is stuck, motivation/executive-function coaching, and honest reporting to parents — genuinely two-tiered work. Watch for tutoring firms repositioning as 'academic coaching' rather than subject help.
IEP/504 and dyslexia-intervention work moving further under state licensure: states adopting structured-literacy interventionist credential requirements (following Arkansas/Mississippi-style dyslexia laws) plus IDEA-funded services requiring a credentialed provider of record for reimbursement would give a named human legal accountability for the intervention plan. This applies mainly to the special-ed/dyslexia segment, not the modal academic tutor.
If tutors are formally written into district-funded high-dosage tutoring contracts (post-ESSER state programs in TN, TX) with attendance/progress-monitoring duties and mandatory-reporter status, the role owns escalation calls about a child's welfare and placement, not just content delivery.
The limit. Even with all of these, the modal private academic tutor stays exposed: the paid product for most families is subject-matter explanation, which is free elsewhere, and no licensure regime is being proposed for general tutoring. The gains concentrate in credentialed intervention and district-contracted roles — a different job than most of these 175,070 workers hold.
| Los Angeles-Long Beach-Anaheim, CA | 18,180 | $45,400 +5% |
| New York-Newark-Jersey City, NY-NJ | 13,980 | $46,550 +7% |
| San Francisco-Oakland-Fremont, CA | 5,240 | $48,340 +12% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 4,940 | $38,500 -11% |
| Chicago-Naperville-Elgin, IL-IN | 4,780 | $38,100 -12% |
| Riverside-San Bernardino-Ontario, CA | 4,270 | $42,260 -3% |
| Atlanta-Sandy Springs-Roswell, GA | 3,980 | $48,180 +11% |
| Boston-Cambridge-Newton, MA-NH | 3,550 | $64,710 +49% |
| Casper, WY | 100 | $103,420 +139% |
| Midland, MI | 40 | $76,680 +77% |
| Amherst Town-Northampton, MA | 70 | $68,220 +57% |
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 37. 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.