Career/Technical Education Teachers, Secondary School
In short: today's best AI models could do about 32% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about the same by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 68% of the time, stays with people.
Teach occupational, vocational, career, or technical subjects to students at the secondary school level.
111,420 people do this job in the US. $66,270 a year.
Reviewed on September 30, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on2 h
- A person checks AI's work11 h
- Desk work AI can't do yet0 h
- Stays with people27 h
Career/Technical Education Teachers, Secondary School, now: of a 40-hour week, AI could take on 2 hours, 11 hours become checking its work, 0 hours are desk work it can't do yet, and 27 hours stay with people.
The largest pieces: Prepare course objectives and outlines (about 3 h, a person checks); Keep student records (about 1 h, a person checks); Prepare, give and grade tests (about 1 h, a person checks).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 28%
- 4%
- 8%
- 55%
- 5%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
What handing it over would take, now
The 32% within reach splits by what a model would need. The dates, pace and reliability follow the controls above. Estimates.
of the working time. The material can be handed to a model and the result handed back.
of the working time. The model has to be connected to the system the work lives in.
is conversation short enough by length. This site leaves conversation with people, so it is not in the total.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 0%. With Claude Mythos Preview (early), measured in April 2026, 22%. Today's estimate: 32%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, 0.0078% went to this job's tasks in May 2026, the 431st largest of the 447 jobs that showed up at all; 3 of its 33 tasks showed up. Within each of Anthropic's methods, never across a change: 0.1% to 0.04% (released February and March 2025); 0.19% to 0.12% (August 2025 to February 2026); 0.0083% to 0.0078% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.1% | 165 of 708 | 13 of 33 |
| Released March 2025 | 0.04% | 303 of 703 | 15 of 33 |
| The week of Aug 4, 2025 (released September 2025) | 0.19% | 97 of 655 | 9 of 33 |
| The week of Nov 13, 2025 (released January 2026) | 0.18% | 96 of 672 | 16 of 33 |
| The week of Feb 5, 2026 (released March 2026) | 0.12% | 138 of 677 | 12 of 33 |
| April 2026 (released June 2026) | 0.0083% | 405 of 434 | 3 of 33 |
| May 2026 (released June 2026) | 0.0078% | 431 of 447 | 3 of 33 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates 33% of this job's working time as physical work. For 2% of the working time, a robot has been shown doing most of the task somewhere: 2% in a purpose-built cell. 0.02 of 3, the 638th highest of the 923 jobs.
- Not physical 67%
- No robot shown 31%
- In a purpose-built cell 2%
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
What changes for the people in this job
From the task-by-task review, by the end of 2028 on the long-run pace at four in five. It says which parts of the work stay with people and which change, not whether anyone keeps a job: that depends on decisions no data set can see.
What stays with people
67% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Teach occupational knowledge and skills, the in-person work, 9%
- Instruct individually and in groups, the in-person work, 7%
- Plan and supervise projects and trips, the in-person work, 4%
- Guide and counsel students, the in-person work, 4%
- Plan and run balanced class activities, the in-person work, 3%
- Run work-experience programs, the in-person work, 3%
What becomes checking the machine
26% of the working time: desk work a model could do by then, in tasks where a mistake costs something or a licensed person signs. A person checks it. Another 6% is desk work that is small if wrong, which can be handed over with lighter checks.
- Prepare course objectives and outlines, costly if wrong, 7%
- Keep student records, costly if wrong, 3%
- Prepare, give and grade tests, a licensed person signs, 3%
- Assign and grade work, a licensed person signs, 2%
- Prepare reports on students, costly if wrong, 2%
- Plan and supervise projects and trips, a licensed person signs, 2%
What to learn
- The conversations. About 63% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- Signing off under your license. Some tasks need a licensed, certified or sworn person to do or sign them. The license, and the judgment it stands for, stays with the person.
- Dealing with people outside the organization. Customers, patients, suppliers or the public: trust with them is built by a person.
- Reviewing work you did not do yourself. The tasks under What becomes checking the machine, where a mistake costs something, stay a person's to check or sign: spotting what a draft gets wrong is a skill of its own.
- Briefing a model and checking its drafts. About 28% of the time is documents and data, the part models reach first: writing a clear brief, giving the right material, and checking what comes back.
Your own version of this job: read it against your job description. The headline is free; the full personal report, every task with its timeline and what to learn, is $19 (a sample).
The tasks
Sorted by . 33 tasks from .
| Task | ||||
|---|---|---|---|---|
| Instruct students in the knowledge and skills required in a specific occupation or occupational field, using a systematic plan of lectures, discussions, audio-visual presentations, and laboratory, shop, and field studies.Lectures, demonstrations and shop and lab work with the students.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 9% | 1.3 h42 min to 2.5 h | In person 100% | Stays with people |
| Instruct students individually and in groups, using various teaching methods, such as lectures, discussions, and demonstrations.Teaching a room of students, and one to one.Done hourly or more by most who do it. | 7% | 50 min25 min to 1.7 h | In person 100% | Stays with people |
| Prepare objectives and outlines for courses of study, following curriculum guidelines or requirements of states and schools.Objectives and outlines written to the guidelines.Done daily by most who do it. AI observed on this task. | 7% | 2.5 h50 min to 6.7 h | Documents 100% | 96%Half by now |
| Plan and supervise class projects, field trips, visits by guest speakers or other experiential activities, and guide students in learning from those activities.Projects and trips planned and run with the students.Done more than monthly by most who do it. | 6% | 2.5 h50 min to 6.7 h | In person 70%, documents 30% | 29%Desk part half by now |
| Plan and conduct activities for a balanced program of instruction, demonstration, and work time that provides students with opportunities to observe, question, and investigate.Activities planned and run in the room.Done daily by most who do it. AI observed on this task.Robots: no robot shown. Anthropic's rating. | 5% | 1.3 h38 min to 2.5 h | In person 70%, documents 30% | 30%Desk part half by now |
| Plan and supervise work-experience programs in businesses, industrial shops, and school laboratories.Placements arranged with employers by call and supervised on site, with the plan.Done daily by most who do it. | 5% | 2.5 h50 min to 6.7 h | In person 40%, documents 30%, conversation 30% | 29%Desk part half by now |
| Prepare, administer, and grade tests and assignments to evaluate students' progress.Marking tests and assignments, and giving them in class.Done more than weekly by most who do it. AI observed on this task. | 5% | 1.3 h38 min to 2.5 h | Documents 70%, in person 30% | 70%Desk part half by now |
| Guide and counsel students with adjustments, academic problems, or special academic interests.Conversations with students about adjustments and problems.Done daily by most who do it. | 4% | 38 min13 min to 1.3 h | In person 100% | Stays with people |
| Collaborate with other teachers and administrators in the development, evaluation, and revision of secondary school programs.Meetings with teachers and administrators, with the documents.Done more than monthly by most who do it. AI observed on this task. | 4% | 2.5 h50 min to 1 work day | Conversation 60%, documents 40% | 37%Desk part half by now |
| Maintain accurate and complete student records as required by law, district policy, and administrative regulations.Records kept in the school system as the law requires.Done daily by most who do it. | 3% | 50 min25 min to 1.7 h | Systems 70%, documents 30% | 100%Half by now |
| Observe and evaluate students' performance, behavior, social development, and physical health.Watching students in the room, with notes on their progress.Done daily by most who do it. AI observed on this task. | 3% | 25 min13 min to 50 min | In person 70%, documents 30% | 30%Desk part half by now |
| Prepare materials and classroom for class activities.Preparing lesson materials and setting up the room.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 3% | 33 min17 min to 1.3 h | Documents 60%, hands-on 40% | 60%Desk part half by now |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach. Made of: how the task's time splits among five kinds of doing. Only the documents and systems parts can count as within reach, so a task that is 30% documents can be at most 30% within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
- Career/Technical Education Teachers, Postsecondary40% now, 53% by 2028
- Career/Technical Education Teachers, Middle School39% now, 39% by 2028
- Instructional Coordinators39% now, 68% by 2028
- Special Education Teachers, Secondary School36% now, 36% by 2028
- Secondary School Teachers, Except Special and Career/Technical Education40% now, 41% by 2028
- Business Teachers, Postsecondary29% now, 51% by 2028
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of Career/Technical Education Teachers, Secondary School is within reach of AI now?
- An estimated 32% of the working time is within reach of AI models now and 32% by the end of 2028, on METR's long-run pace at four in five. Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of Career/Technical Education Teachers, Secondary School come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Prepare course objectives and outlines (96% of the task's time now); Keep student records (100% of the task's time now); and Prepare, give and grade tests (70% of the task's time now).
- Which tasks of Career/Technical Education Teachers, Secondary School stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Teach occupational knowledge and skills; Instruct individually and in groups; and Guide and counsel students. Together such tasks are about 43% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Career/Technical Education Teachers, Secondary School learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are the conversations; signing off under your license; and dealing with people outside the organization. By the end of 2028 on the long-run pace at four in five, about 67% of the working time stays with people, 26% becomes checking a model's work and 6% could be handed over with lighter checks. It says which parts of the work change, never whether anyone keeps a job.
- Do people already use AI for the work of Career/Technical Education Teachers, Secondary School?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.0078% went to this job's tasks, the 431st largest share of the 447 jobs that showed up; 3 of its 33 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 0%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of Career/Technical Education Teachers, Secondary School?
- Anthropic's robot exposure rating (September 2026) finds 33% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 2% of the working time. Its index is 0.02 of 3. One example: Snap-on AutoCrib RoboCrib VX1000 / LX2000 for "order and inventory classroom supplies". A robot shown somewhere is not one in every workplace; this site's own estimates still count no physical work as within reach.
- How is this estimated?
- From the 33 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, reviewed one task at a time on September 30, 2026 for what each is made of (documents, business systems, conversation, in person, hands-on), and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
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