Teaching Assistants, Postsecondary
In short: today's best AI models could do about 38% 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 40% 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.
Assist faculty or other instructional staff in postsecondary institutions by performing instructional support activities, such as developing teaching materials, leading discussion groups, preparing and giving examinations, and grading examinations or papers.
164,090 people do this job in the US. $42,910 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 on1 h
- A person checks AI's work14 h
- Desk work AI can't do yet1 h
- Stays with people24 h
Teaching Assistants, Postsecondary, now: of a 40-hour week, AI could take on 1 hours, 14 hours become checking its work, 1 hours are desk work it can't do yet, and 24 hours stay with people.
The largest pieces: Assist with research (about 5 h, a person checks); Develop teaching materials (about 5 h, a person checks); Grade work and record grades (about 4 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.
- 37%
- 3%
- 1%
- 33%
- 26%
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 38% 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, 11%. Today's estimate: 38%.
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, 0.63% went to this job's tasks in May 2026, the 41st largest of the 447 jobs that showed up at all; 7 of its 20 tasks showed up. Within each of Anthropic's methods, never across a change: 0.18% to 0.33% (released February and March 2025); 0.22% to 0.21% (August 2025 to February 2026); 0.61% to 0.63% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.18% | 104 of 708 | 9 of 20 |
| Released March 2025 | 0.33% | 54 of 703 | 7 of 20 |
| The week of Aug 4, 2025 (released September 2025) | 0.22% | 83 of 655 | 6 of 20 |
| The week of Nov 13, 2025 (released January 2026) | 0.28% | 63 of 672 | 9 of 20 |
| The week of Feb 5, 2026 (released March 2026) | 0.21% | 92 of 677 | 8 of 20 |
| April 2026 (released June 2026) | 0.61% | 40 of 434 | 7 of 20 |
| May 2026 (released June 2026) | 0.63% | 41 of 447 | 7 of 20 |
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 13% of this job's working time as physical work. For 10% of the working time, a robot has been shown doing most of the task somewhere: 10% in a purpose-built cell. 0.10 of 3, the 517th highest of the 923 jobs.
- Not physical 87%
- No robot shown 3%
- In a purpose-built cell 10%
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.
- Assist with research, the hands-on work, 20%
- Teach undergraduate courses, the in-person work, 9%
- Demonstrate lab equipment and enforce rules, the in-person work, 7%
- Hold office hours, the in-person work, 6%
- Lead sections and labs, the in-person work, 4%
- Tutor and mentor students, the in-person work, 4%
What becomes checking the machine
38% 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 2% is desk work that is small if wrong, which can be handed over with lighter checks.
- Develop teaching materials, costly if wrong, 15%
- Assist with research, costly if wrong, 13%
- Grade work and record grades, costly if wrong, 10%
- Prepare and proctor exams, costly if wrong, 0.6%
- Test lab projects beforehand, costly if wrong, 0.5%
What to learn
- 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.
- 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.
- The conversations. About 34% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- The hands-on craft. About 26% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- Dealing with people outside the organization. Customers, patients, suppliers or the public: trust with them is built by a person.
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 . 20 tasks from .
| Task | ||||
|---|---|---|---|---|
| Provide assistance to faculty members or staff with laboratory or field research.Lab or field work for the faculty member, with the write-up.Done more than weekly by most who do it.Robots: in a purpose-built cell, RIVA Robotic IV Automation (also Omnicell i.v.STATION) (ARxIUM / Omnicell) (deployed). Anthropic's rating. | 33% | 3.3 h1.7 h to 6.7 h | Hands-on 60%, documents 40% | 40%Desk part half by now |
| Develop teaching materials, such as syllabi, visual aids, answer keys, supplementary notes, or course Web sites.Syllabi, notes, answer keys and course pages written.Done more than weekly by most who do it. AI observed on this task. | 15% | 3.3 h50 min to 1 work day | Documents 100% | 86%Half by now |
| Evaluate and grade examinations, assignments, or papers, and record grades.Marking papers and entering the grades.Done more than weekly by most who do it. | 10% | 2.5 h50 min to 6.7 h | Documents 80%, systems 20% | 96%Half by now |
| Teach undergraduate-level courses.Teaching a room of students.Done more than weekly by most who do it. | 9% | 2.5 h1.3 h to 5 h | In person 100% | Stays with people |
| Demonstrate use of laboratory equipment and enforce laboratory rules.Showing the equipment in the lab and keeping the rules.Done more than monthly by most who do it.Robots: no robot shown. Anthropic's rating. | 7% | 1.3 h25 min to 2.5 h | In person 70%, hands-on 30% | Stays with people |
| Schedule and maintain regular office hours to meet with students.Meeting students in office hours.Done more than weekly by most who do it. | 6% | 1.7 h50 min to 2.5 h | In person 100% | Stays with people |
| Lead discussion sections, tutorials, or laboratory sections.Leading discussion sections and lab sections in person.Done more than weekly by most who do it. | 4% | 1.7 h50 min to 3.3 h | In person 100% | Stays with people |
| Tutor or mentor students who need additional instruction.Extra instruction one to one.Done more than weekly by most who do it. | 4% | 50 min25 min to 1.7 h | In person 100% | Stays with people |
| Attend lectures given by the supervising instructor.Attendance only the assistant can do.Done more than weekly by most who do it. | 3% | 1.1 h42 min to 1.7 h | In person 100% | Stays with people |
| Copy and distribute classroom materials.Copying and handing out materials.Done more than monthly by most who do it. Robots: in a purpose-built cell, Ripcord document digitization robots (sold). Anthropic's rating. | 2% | 25 min8 min to 1.3 h | Hands-on 100% | Stays with people |
| Return assignments to students in accordance with established deadlines.Handing graded work back in class or posting it online.Done more than weekly by most who do it. | 2% | 38 min13 min to 1.7 h | In person 60%, systems 40% | 40%Desk part half by now |
| Complete laboratory projects prior to assigning them to students so that any needed modifications can be made.Running the lab project at the bench and noting changes.Done more than weekly by most who do it.Robots: in a purpose-built cell, A-Lab (Lawrence Berkeley National Laboratory / UC Berkeley) (demonstrated). Anthropic's rating. | 2% | 2.5 h50 min to 6.7 h | Hands-on 70%, documents 30% | 29%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.
- Tutors23% now, 23% by 2028
- Secondary School Teachers, Except Special and Career/Technical Education40% now, 41% by 2028
- Education Teachers, Postsecondary27% now, 45% by 2028
- Career/Technical Education Teachers, Middle School39% now, 39% by 2028
- Elementary School Teachers, Except Special Education35% now, 35% by 2028
- Middle School Teachers, Except Special and Career/Technical Education43% now, 44% 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 Teaching Assistants, Postsecondary is within reach of AI now?
- An estimated 38% of the working time is within reach of AI models now and 41% by the end of 2028, on METR's long-run pace at four in five (40% to 41% across the three paces the site shows). 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 Teaching Assistants, Postsecondary 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: Assist with research (40% of the task's time now); Develop teaching materials (86% of the task's time now, 100% by the end of 2028); and Grade work and record grades (96% of the task's time now).
- Which tasks of Teaching Assistants, Postsecondary stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Teach undergraduate courses; Demonstrate lab equipment and enforce rules; and Hold office hours. Together such tasks are about 35% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Teaching Assistants, Postsecondary learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are reviewing work you did not do yourself; briefing a model and checking its drafts; and the conversations. By the end of 2028 on the long-run pace at four in five, about 59% of the working time stays with people, 38% becomes checking a model's work and 2% 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 Teaching Assistants, Postsecondary?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.63% went to this job's tasks, the 41st largest share of the 447 jobs that showed up; 7 of its 20 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 10%. 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 Teaching Assistants, Postsecondary?
- Anthropic's robot exposure rating (September 2026) finds 13% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 10% of the working time. Its index is 0.10 of 3. One example: RIVA Robotic IV Automation (also Omnicell i.v.STATION) for "assist with research". 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 20 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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