EXPOSED
Lecture drafting, slide decks, syllabus writing, rubric-based grading of essays and speech reflections, and discussion-prompt generation are already handled competently by LLMs — that's a large share of the weekly workload. What holds is the live room: coaching a nervous student through a five-minute persuasive speech, reading an audience in real time, running peer critique, and being the person whose judgment on a student's argument actually counts. The bigger threat isn't AI replacing the professor, it's enrollment decline plus adjunctification compressing headcount while AI absorbs prep and grading labor.
Roughly flat across the period, with year-to-year wobble.
Median pay $70,630 → $78,580 -11.0% 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
+2.1%
Percentage only. The projection counts a different population from the 29,420 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 +2.1% more of these jobs by 2034, and at 47/100 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.
~2,700 openings a year on average, including replacing people who leave.
LecturerProfessorInstructorSpeech TeacherAdjunct LecturerSpeech ProfessorAdjunct ProfessorCollege ProfessorSpeech InstructorAdjunct InstructorJournalism TeacherRhetoric ProfessorAssistant ProfessorAssociate ProfessorJournalism ProfessorJournalist ProfessorMedia Arts ProfessorJournalism InstructorCollege Faculty MemberCommunication LecturerCommunication ProfessorPublic Speaking TeacherCommunication InstructorPublic Speaking Professor
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Half the job — writing the public-speaking syllabus, building rhetoric lecture decks, generating discussion prompts on media effects, marking speech self-reflections against a rubric — is text-in/text-out work an LLM does at passable quality, while the irreducible half is standing in a room watching a student's hands shake through their informative speech and deciding in real time whether to interrupt or let them finish.
Some physical or field component You are physically in a classroom or speech lab most contact hours, positioning a camera, watching posture and eye contact and vocal projection in a body you can only assess in person — but it's a scheduled, climate-controlled room on a campus, not a job site, which is why this sits mid-scale rather than high.
No licence, no signature requirement No state licence gates who teaches COMM 101 — a master's degree and a department chair's hiring decision is the whole barrier, and adjuncts are hired on that basis every August with no personal legal exposure for what happens in the room.
Meaningful discretion You decide whether a student's argument on a contested political topic gets protected as advocacy or flagged as a classroom-conduct issue, whether a plagiarized or AI-drafted speech outline becomes an academic-integrity referral, and where the line is on a C+ versus B- for persuasive delivery — real discretion, but exercised inside departmental rubrics, appeal processes, and accreditation-driven learning outcomes that constrain the call.
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 (2/20) 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 (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 47 points (55%).
Embodiment (11/20) 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.
Law Teachers, Postsecondary EXPOSED
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 60/100, still EXPOSED.
If institutions adopt policies making the instructor of record the named human adjudicator for AI-misconduct findings in written and spoken assignments — a signed determination that survives student appeal and Title IX/due-process review, as several university honor-code revisions since 2023 have begun requiring — the role owns consequential calls under ambiguity rather than delegating to detection software.
Task-mix shift is genuinely available here: if departments formally reassign prep, slide and rubric-grading labor to AI and redefine the load as live coaching, speech-lab supervision, and in-person oral defense of arguments, the residual job is the tier LLMs cannot do. Watch for accreditation-driven 'oral competency' requirements (e.g., regional accreditors or state general-education mandates specifying a proctored live speech component) that make in-person performance assessment non-substitutable.
If employer-facing credentials for oral communication (e.g., NCA-endorsed or state workforce-board speech certifications) require assessment by a human evaluator present in the room, buyers are paying specifically for a human's verdict rather than a course.
If curricula shift toward in-person speech labs, debate coaching, and clinical-style communication training for health and law programs — where interpersonal performance must be observed physically — the share of work located in an unpredictable live room rises.
The limit. Liability shield has no plausible route: no license is required to teach postsecondary communications and no personal liability attaches to grading. And no lever here addresses the dominant threat named in the description — enrollment decline and adjunctification cutting headcount. A course can be judgment-heavy and still be taught by half as many people on contingent contracts, so the register score can rise while the occupation shrinks.
| New York-Newark-Jersey City, NY-NJ | 2,430 | $97,760 +24% |
| Los Angeles-Long Beach-Anaheim, CA | 1,360 | $130,160 +66% |
| Chicago-Naperville-Elgin, IL-IN | 1,020 | $76,730 -2% |
| Boston-Cambridge-Newton, MA-NH | 920 | $80,890 +3% |
| Dallas-Fort Worth-Arlington, TX | 920 | $77,050 -2% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 650 | $79,870 +2% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 490 | $83,810 +7% |
| Austin-Round Rock-San Marcos, TX | 470 | $79,710 +1% |
| Riverside-San Bernardino-Ontario, CA | 300 | $180,650 +130% |
| San Francisco-Oakland-Fremont, CA | 290 | $166,920 +112% |
| Fresno, CA | 80 | $136,730 +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 47. 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.