Instructional Coordinators
In short: today's best AI models could do about 39% 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 68% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 31% of the time, stays with people.
Develop instructional material, coordinate educational content, and incorporate current technology into instruction in order to provide guidelines to educators and instructors for developing curricula and conducting courses. May train and coach teachers. Includes educational consultants and specialists, and instructional material directors.
227,760 people do this job in the US. $77,440 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 work14 h
- Desk work AI can't do yet12 h
- Stays with people12 h
Instructional Coordinators, now: of a 40-hour week, AI could take on 2 hours, 14 hours become checking its work, 12 hours are desk work it can't do yet, and 12 hours stay with people.
The largest pieces: Run teacher training (about 2 h, a person checks); Update programs for current technology (about 1 h, a person checks); Evaluate curricula and materials (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.
- 68%
- 0%
- 6%
- 26%
- 0%
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 39% 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, 8%. Today's estimate: 39%.
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, 1.1% went to this job's tasks in May 2026, the 22nd largest of the 447 jobs that showed up at all; 9 of its 30 tasks showed up. Within each of Anthropic's methods, never across a change: 0.75% to 1.4% (released February and March 2025); 0.96% to 0.98% (August 2025 to February 2026); 1% to 1.1% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.75% | 22 of 708 | 20 of 30 |
| Released March 2025 | 1.4% | 11 of 703 | 19 of 30 |
| The week of Aug 4, 2025 (released September 2025) | 0.96% | 19 of 655 | 20 of 30 |
| The week of Nov 13, 2025 (released January 2026) | 1.3% | 18 of 672 | 18 of 30 |
| The week of Feb 5, 2026 (released March 2026) | 0.98% | 22 of 677 | 19 of 30 |
| April 2026 (released June 2026) | 1% | 24 of 434 | 9 of 30 |
| May 2026 (released June 2026) | 1.1% | 22 of 447 | 9 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 almost none of this job's working time as physical work, so robots do not reach it.
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
31% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Run teacher training, the in-person work, 10%
- Advise and teach students, the in-person work, 9%
- Run workshops and committees, the in-person work, 3%
- Observe and coach teachers, the in-person work, 3%
- Advise staff on curriculum, the conversation, 2%
- Assess learning needs, the conversation, 1%
What becomes checking the machine
60% 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 8% is desk work that is small if wrong, which can be handed over with lighter checks.
- Write master course documentation, costly if wrong, 8%
- Update programs for current technology, costly if wrong, 7%
- Run teacher training, costly if wrong, 7%
- Evaluate curricula and materials, costly if wrong, 6%
- Design online learning products, costly if wrong, 6%
- Assess learning needs, costly if wrong, 3%
What to learn
- Briefing a model and checking its drafts. About 68% 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.
- 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.
- The conversations. About 31% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
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 | ||||
|---|---|---|---|---|
| Plan and conduct teacher training programs and conferences dealing with new classroom procedures, instructional materials and equipment, and teaching aids.Sessions led, with the planning.Done more than yearly by most who do it. | 16% | 1 work day3.3 h to 3.1 work days | In person 60%, documents 40% | 25%Desk part half by now |
| Advise and teach students.In the room with students.Done more than monthly by most who do it. AI observed on this task. | 9% | 50 min17 min to 2.5 h | In person 100% | Stays with people |
| Develop master course documentation or manuals according to applicable accreditation, certification, or other requirements.Writing at the desk. | 8% | 2.1 work days5 h to 1.3 work weeks | Documents 100% | 23%Half by May 2027 |
| Update the content of educational programs to ensure that students are being trained with equipment and processes that are technologically current.Rewriting content at the desk.Done more than yearly by most who do it. | 7% | 1 work day2.5 h to 4.2 work days | Documents 100% | 43%Half by Dec 2026 |
| Design learning products, including Web-based aids or electronic performance support systems.Design and build at the desk. AI observed on this task. | 6% | 1.7 work days3.3 h to 1.3 work weeks | Documents 100% | 24%Half by May 2027 |
| Research, evaluate, and prepare recommendations on curricula, instructional methods, and materials for school systems.Research and recommendations at the desk.Done more than yearly by most who do it. AI observed on this task. | 6% | 1.3 work days3.3 h to 4.2 work days | Documents 100% | 44%Half by Dec 2026 |
| Observe work of teaching staff to evaluate performance and to recommend changes that could strengthen teaching skills.Classroom observation and the write up.Done more than monthly by most who do it. | 5% | 1.7 h50 min to 3.3 h | In person 60%, documents 40% | 40%Desk part half by now |
| Conduct needs assessments and strategic learning assessments to develop the basis for curriculum development or to update curricula.Interviews and surveys, then the analysis. | 5% | 1.3 work days3.3 h to 4.2 work days | Documents 70%, conversation 30% | 30%Desk part half by Dec 2026 |
| Research and evaluate emerging instructional technologies or methods.Research at the desk. | 5% | 1.3 work days3.3 h to 4.2 work days | Documents 100% | 44%Half by Dec 2026 |
| Conduct or participate in workshops, committees, and conferences designed to promote the intellectual, social, and physical welfare of students.Workshops in person and committee meetings.Done more than monthly by most who do it. | 3% | 3.3 h1.3 h to 6.7 h | In person 60%, conversation 40% | Stays with people |
| Interpret and enforce provisions of state education codes and rules and regulations of state education boards.Reading the codes and deciding at the desk.Done more than yearly by most who do it. | 2% | 1.3 h25 min to 4.2 h | Documents 100% | 98%Half by now |
| Analyze performance data to determine effectiveness of instructional systems, courses, or instructional materials.Analysis at the desk. AI observed on this task. | 2% | 5 h1.7 h to 2.1 work days | Documents 100% | 73%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.
- Training and Development Specialists43% now, 64% by 2028
- Education Teachers, Postsecondary27% now, 45% by 2028
- Training and Development Managers32% now, 88% by 2028
- Career/Technical Education Teachers, Middle School39% now, 39% by 2028
- Elementary School Teachers, Except Special Education35% now, 35% by 2028
- Special Education Teachers, Secondary School36% now, 36% 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 Instructional Coordinators is within reach of AI now?
- An estimated 39% of the working time is within reach of AI models now and 68% by the end of 2028, on METR's long-run pace at four in five (64% to 69% 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 Instructional Coordinators 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: Run teacher training (25% of the task's time now, 40% by the end of 2028); Update programs for current technology (43% of the task's time now, 100% by the end of 2028); and Evaluate curricula and materials (44% of the task's time now, 100% by the end of 2028).
- Which tasks of Instructional Coordinators stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Advise and teach students; Run workshops and committees; and Train instructors on technology. Together such tasks are about 14% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Instructional Coordinators learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are briefing a model and checking its drafts; reviewing work you did not do yourself; and the conversations. By the end of 2028 on the long-run pace at four in five, about 31% of the working time stays with people, 60% becomes checking a model's work and 8% 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 Instructional Coordinators?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 1.1% went to this job's tasks, the 22nd largest share of the 447 jobs that showed up; 9 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 30%. 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 Instructional Coordinators?
- Anthropic's robot exposure rating (September 2026) finds almost none of the working time is physical work, so robots do not reach it.
- 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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