Instructional coordinators: AI exposure and career outlook
Instructional coordinators (SOC 25-9031) sit at the 93rd percentile for measured AI exposure among the 342 occupations tracked here, measured from a composite of Microsoft Research and Anthropic Economic Index telemetry. An estimated 63% of tasks are already automated and 80% are being reshaped rather than replaced — both modelled figures, not direct measurements. Of the Claude usage observed in this occupation, 47% follows an automation pattern and 53% an augmentation pattern — close enough to even that the grouping below should be read loosely, which places it in the Orchestrator opportunity group: AI reaches deep into this work and amplifies it. The leverage goes to whoever directs it, and that can be you. In Canada the role maps to NOC 41405 (Education policy researchers, consultants and program officers), and ESDC's COPS 2024–2033 outlook for that unit group is balance.
Figures last updated 2026-06. Every number on this page is labelled measured or modelled; where a source has no coverage for this occupation, it says so rather than showing a zero.
Key facts
- SOC code
- 25-9031
- Canada NOC 2021
- 41405
- TEER level
- 1 — University Degree
- COPS outlook
- Balance
What the measurements actually say
Three independent studies measure AI exposure for this occupation directly. The two percentages below them are ours, modelled from those measurements.
| Metric | Value | Provenance |
|---|---|---|
| AI applicability | 31% | Measured — Microsoft Research, from 200,000 Copilot conversations classified against O*NET work activities. |
| Observed AI usage | 30% | Measured — Anthropic Economic Index, share of tasks observed being performed with Claude (CC-BY 4.0). |
| Academic AI exposure | 88th percentile | Measured — Felten-Raj-Seamans AIOE index across 774 occupations; the index underlying Statistics Canada's Canadian estimates. |
| Automation-pattern usage | 47% | Measured — the share of observed Claude usage in this occupation where the task is handed over rather than iterated on (Anthropic Economic Index). |
| Augmentation-pattern usage | 53% | Measured — the complement of the row above; the two sum to 100% by construction. This is the pattern where the person stays in the loop. |
| Estimated task automation (modelled) | 63% | Modelled from this occupation's measured telemetry composite, mapped through anchors calibrated to Anthropic's published aggregate findings. Our estimate, not per-occupation telemetry. |
| Estimated task reshaping (modelled) | 80% | Share of tasks where AI acts as co-pilot rather than replacement. Modelled from the measured telemetry composite, calibrated to BCG's published aggregate reshaping rates. Our estimate. |
This skill domain faces significant AI substitution. The opportunity is in pivoting to AI orchestration and higher-order human judgment within the domain.
In Canada: NOC 41405
Instructional coordinators map to NOC 41405 — Education policy researchers, consultants and program officers at TEER 1 (University Degree). ESDC's COPS 2024–2033 projection for this unit group is Balance.
Mapped via Statistics Canada's official SOC 2018 → NOC 2016 → NOC 2021 correspondence tables. TEER is the second digit of the official NOC code, so it cannot disagree with it.
What this job actually involves
These are the O*NET task statements for Instructional coordinators— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 12 of 30, core tasks first.
The exposure figures above are for the occupation as a whole. We do not have a per-task measurement: no published dataset tells us which of these specific tasks AI is doing. Read this as what the job is made of, not as a ranking of what is at risk.
4 of these 12statements do carry one measured signal: Anthropic publishes, per O*NET task, how much of the observed Claude usage on it looked like automation rather than iteration. Those are marked below. It still says how people use AI on that task, not how much of it AI can do — and the unmarked statements carry nothing rather than inheriting the occupation's figure.
Microsoft's applicability score was itself produced by classifying Copilot conversations against the O*NET work-activity catalogue — the same catalogue these groups come from. The published file gives one number per occupation, not one per activity, so we can tell you that 31% of this occupation's work activities are covered by observed AI usage, but not which ones.
Documenting/Recording Information · 3 tasks
Update the content of educational programs to ensure that students are being trained with equipment and processes that are technologically current.
Core task · Importance 3.7/5 (O*NET)
Prepare grant proposals, budgets, and program policies and goals or assist in their preparation.
Supplemental task · Importance 3.9/5 (O*NET) · 30% of observed AI use on this task is automation-pattern
Prepare or approve manuals, guidelines, and reports on state educational policies and practices for distribution to school districts.
Supplemental task · Importance 3.5/5 (O*NET)
Providing Consultation and Advice to Others · 3 tasks
Advise teaching and administrative staff in curriculum development, use of materials and equipment, and implementation of state and federal programs and procedures.
Core task · Importance 4.0/5 (O*NET) · 26% of observed AI use on this task is automation-pattern
Advise and teach students.
Core task · Importance 3.9/5 (O*NET) · 23% of observed AI use on this task is automation-pattern
Recommend, order, or authorize purchase of instructional materials, supplies, equipment, and visual aids designed to meet student educational needs and district standards.
Core task · Importance 3.9/5 (O*NET)
Judging the Qualities of Objects, Services, or People · 2 tasks
Observe work of teaching staff to evaluate performance and to recommend changes that could strengthen teaching skills.
Core task · Importance 4.5/5 (O*NET)
Research, evaluate, and prepare recommendations on curricula, instructional methods, and materials for school systems.
Core task · Importance 3.7/5 (O*NET) · 35% of observed AI use on this task is automation-pattern
Monitoring Processes, Materials, or Surroundings · 1 task
Interpret and enforce provisions of state education codes and rules and regulations of state education boards.
Core task · Importance 4.1/5 (O*NET)
Selling or Influencing Others · 1 task
Address public audiences to explain program objectives and to elicit support.
Core task · Importance 3.7/5 (O*NET)
Training and Teaching Others · 1 task
Plan and conduct teacher training programs and conferences dealing with new classroom procedures, instructional materials and equipment, and teaching aids.
Core task · Importance 4.1/5 (O*NET)
Guiding, Directing, and Motivating Subordinates · 1 task
Conduct or participate in workshops, committees, and conferences designed to promote the intellectual, social, and physical welfare of students.
Core task · Importance 4.0/5 (O*NET)
Task statements and work activities from the O*NET Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under CC BY 4.0. O*NET® is a trademark of USDOL/ETA. USDOL/ETA has not reviewed or approved this material.
That's the average. Is this you?
Everything above describes Instructional coordinators as a whole. Three questions, and nothing is stored or sent.
Where are you in your career?
What does your week mostly look like?
Is your employer deploying AI?
Where the leverage is
Transition to high-value advisory, complex case analysis, or AI system oversight. The pivot is to move from routine execution to AI orchestration and quality assurance.
Cognitive, information-based tasks are highly exposed. Entry-level displacement pressure is high, necessitating transition to advisory and governance roles.
Master workflow automation platforms (e.g., Make, Zapier, CRM integrations) to double your output and position for fractional leadership roles.
This guidance is written per exposure band — three texts across all 342 occupations, framed on the ILO's transformation-versus-displacement distinction. It is editorial guidance for the high risk band, not an occupation-specific research finding about instructional coordinators.
Sources for this occupation
Only the sources that hold data for instructional coordinators are listed. A study that does not cover this occupation is not cited here.
- Microsoft Research — Working with AI (Tomlinson et al., arXiv:2507.07935): Measured AI applicability, from 200,000 anonymised Copilot conversations classified against O*NET work activities.
- Anthropic Economic Index: Measured observed AI usage per occupation, from Anthropic's open per-SOC dataset (CC-BY 4.0).
- Felten, Raj & Seamans — AI Occupational Exposure index: Measured academic exposure percentile; the index underlying Statistics Canada's Canadian AI-exposure estimates (Mehdi & Morissette, 2024).
- O*NET Database 31.0 (USDOL/ETA), CC BY 4.0: The occupation's task statements and work activities, verbatim. Joined on SOC 2018 with no crosswalk — O*NET-SOC is built on SOC — and carrying no claim about which tasks AI touches.
- Anthropic Economic Index — collaboration split (release 2026-06-26, CC-BY 4.0): Measured share of observed Claude usage following an automation rather than an augmentation pattern. This is the second axis of the grouping above.
- Statistics Canada NOC 2021 concordance: Official SOC 2018 → NOC 2016 V1.3 → NOC 2021 V1.0 correspondence tables. TEER is the second digit of the resolved code, so we report it only where the concordance gave a single candidate.
- ESDC COPS 2024–2033: Projected labour-market assessment per NOC 2021 unit group, from the Canadian Occupational Projection System open dataset.
- BCG — AI Will Reshape More Jobs Than It Replaces (April 3, 2026): Published aggregate reshaping rates, used to calibrate our modelled percentages. The per-occupation figures here are our estimates, not BCG's data.
Related occupations
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