Self-Enrichment Teachers
In short: today's best AI models could do about 40% 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 60% of the time, stays with people.
Teach or instruct individuals or groups for the primary purpose of self-enrichment or recreation, rather than for an occupational objective, educational attainment, competition, or fitness.
332,110 people do this job in the US. $46,800 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 on16 h
- A person checks AI's work0 h
- Desk work AI can't do yet0 h
- Stays with people24 h
Self-Enrichment Teachers, now: of a 40-hour week, AI could take on 16 hours, 0 hours become checking its work, 0 hours are desk work it can't do yet, and 24 hours stay with people.
The largest pieces: Review and revise courses (about 3 h); Adapt teaching to students (about 3 h); Write lesson plans and outlines (about 2 h).
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.
- 37%
- 3%
- 4%
- 54%
- 2%
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 40% 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, 31%. Today's estimate: 40%.
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, 0.11% went to this job's tasks in May 2026, the 154th largest of the 447 jobs that showed up at all; 5 of its 30 tasks showed up. Within each of Anthropic's methods, never across a change: 0.27% to 0.28% (released February and March 2025); 0.46% to 0.35% (August 2025 to February 2026); 0.1% to 0.11% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.27% | 73 of 708 | 15 of 30 |
| Released March 2025 | 0.28% | 67 of 703 | 13 of 30 |
| The week of Aug 4, 2025 (released September 2025) | 0.46% | 41 of 655 | 10 of 30 |
| The week of Nov 13, 2025 (released January 2026) | 0.35% | 49 of 672 | 13 of 30 |
| The week of Feb 5, 2026 (released March 2026) | 0.35% | 63 of 677 | 10 of 30 |
| April 2026 (released June 2026) | 0.1% | 156 of 434 | 5 of 30 |
| May 2026 (released June 2026) | 0.11% | 154 of 447 | 5 of 30 |
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 53% of this job's working time as physical work. For 7% of the working time, a robot has been shown doing most of the task somewhere: 7% in a purpose-built cell. 0.07 of 3, the 553rd highest of the 923 jobs.
- Not physical 47%
- No robot shown 46%
- In a purpose-built cell 7%
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
59% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Teach classes and individuals, the in-person work, 12%
- Teach classes and workshops, the in-person work, 8%
- Size up students' abilities, the in-person work, 6%
- Monitor student progress, the in-person work, 4%
- Plan and run class activities, the in-person work, 3%
- Adapt teaching to students, the in-person work, 3%
What becomes checking the machine
1% 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 39% is desk work that is small if wrong, which can be handed over with lighter checks.
- Run projects, trips and events, costly if wrong, 0.7%
What to learn
- The conversations. About 58% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- Briefing a model and checking its drafts. About 37% 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.
- Dealing with people outside the organization. Customers, patients, suppliers or the public: trust with them is built by a person.
- Handling protected records. Much of the work touches protected personal records: knowing what may be shared with a model, and how, is part of the job.
- 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: spotting what a draft gets wrong is a skill of its own.
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 . 30 tasks from .
| Task | ||||
|---|---|---|---|---|
| Instruct students individually and in groups, using various teaching methods, such as lectures, discussions, and demonstrations.Teaching in the room.Done hourly or more by most who do it. Robots: no robot shown. Anthropic's rating. | 12% | 50 min25 min to 1.7 h | In person 100% | Stays with people |
| Adapt teaching methods and instructional materials to meet students' varying needs and interests.Reworking the material and methods for the group.Done daily by most who do it. AI observed on this task. | 11% | 1.1 h25 min to 2.1 h | Documents 70%, in person 30% | 70%Desk part half by now |
| Review instructional content, methods, and student evaluations to assess strengths and weaknesses, and to develop recommendations for course revision, development, or elimination.Reading the evaluations and reworking the course on paper.Done daily by most who do it. AI observed on this task. | 8% | 2.1 h50 min to 4.2 h | Documents 100% | 100%Half by now |
| Conduct classes, workshops, and demonstrations, and provide individual instruction to teach topics and skills, such as cooking, dancing, writing, physical fitness, photography, personal finance, and flying.Teaching in the room, the kitchen or the studio.Done daily by most who do it.Robots: no robot shown. Anthropic's rating. | 8% | 1.7 h50 min to 2.5 h | In person 100% | Stays with people |
| Observe students to determine qualifications, limitations, abilities, interests, and other individual characteristics.Watching students in class.Done daily by most who do it. | 6% | 38 min17 min to 1.3 h | In person 100% | Stays with people |
| Prepare instructional program objectives, outlines, and lesson plans.Course design at the desk.Done more than weekly by most who do it. AI observed on this task. | 6% | 1.7 h50 min to 4.2 h | Documents 100% | 100%Half by now |
| Assign and grade class work and homework.Marking work and handing it out and back.Done more than monthly by most who do it. | 5% | 50 min25 min to 1.7 h | Documents 80%, in person 20% | 80%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.Teaching in the room, with the planning behind it.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 |
| Monitor students' performance to make suggestions for improvement and to ensure that they satisfy course standards, training requirements, and objectives.Watching students work and noting who needs what.Done daily by most who do it. | 5% | 25 min13 min to 1.1 h | In person 80%, documents 20% | 20%Desk part half by now |
| Establish clear objectives for all lessons, units, and projects and communicate those objectives to students.Writing the objectives into the plan and telling the class.Done daily by most who do it. | 4% | 25 min13 min to 50 min | Documents 60%, in person 40% | 60%Desk part half by now |
| Prepare materials and classrooms for class activities.Making the materials and setting the room up.Done more than weekly by most who do it. Robots: in a purpose-built cell, Amazon Sparrow (deployed). Anthropic's rating. | 4% | 33 min17 min to 1.1 h | Documents 60%, hands-on 40% | 60%Desk part half by now |
| Observe and evaluate the performance of other instructors.Sitting in on classes and writing the evaluation.Done more than yearly by most who do it. | 4% | 1.7 h50 min to 3.3 h | In person 70%, documents 30% | 30%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.
- Secondary School Teachers, Except Special and Career/Technical Education40% now, 41% by 2028
- Elementary School Teachers, Except Special Education35% now, 35% by 2028
- Tutors23% now, 23% by 2028
- Adult Basic Education, Adult Secondary Education, and English as a Second Language Instructors66% now, 66% by 2028
- Middle School Teachers, Except Special and Career/Technical Education43% now, 44% by 2028
- Special Education Teachers, Kindergarten34% now, 34% 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 Self-Enrichment Teachers is within reach of AI now?
- An estimated 40% of the working time is within reach of AI models now and 40% 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 Self-Enrichment Teachers 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: Review and revise courses (100% of the task's time now); Adapt teaching to students (70% of the task's time now); and Write lesson plans and outlines (100% of the task's time now).
- Which tasks of Self-Enrichment Teachers stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Teach classes and individuals; Teach classes and workshops; and Size up students' abilities. Together such tasks are about 38% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Self-Enrichment Teachers 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; briefing a model and checking its drafts; and dealing with people outside the organization. By the end of 2028 on the long-run pace at four in five, about 59% of the working time stays with people, 1% becomes checking a model's work and 39% 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 Self-Enrichment Teachers?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.11% went to this job's tasks, the 154th largest share of the 447 jobs that showed up; 5 of its 30 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 7%. 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 Self-Enrichment Teachers?
- Anthropic's robot exposure rating (September 2026) finds 53% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 7% of the working time. Its index is 0.07 of 3. One example: Amazon Sparrow for "prepare materials and the room". 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 30 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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