Adapted Physical Education Specialists
25-2059.01Median wage $76,580/yr33,930 employed (US)Rank #670 of 923 scored · top 73% by substitution
Provide individualized physical education instruction or services to children, youth, or adults with exceptional physical needs due to gross motor developmental delays or other impairments.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution20
Exposure21
Augmentation48
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
20 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
5%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%21
panel mean rating 1.9/5 → substitution pressure 21/100
Technical feasibility todayw 20%20
panel mean rating 1.8/5 → substitution pressure 20/100
Cost vs. human wagew 15%21
panel mean rating 1.8/5 → substitution pressure 21/100
Adoption barriersw 20%inverted — strong barriers lower the score22
panel mean rating 4.1/5 (barrier strength) → substitution pressure 22/100
Sector adoption velocityw 10%11
panel mean rating 1.5/5 → substitution pressure 11/100
Task breakdown (20 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Request or order physical education equipment, following standard procedures.
75CI 65–85 · exposure 83 · augmentation 75 · importance 3.5/5 · click for rater detail
Request or order physical education equipment, following standard procedures.
75| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | School districts and educational institutions adopt procurement automation at moderate pace, with pilots common in larger systems but slower rollout in smaller districts. Broader enterprise adoption is faster, but the education sector lags. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education administrative functions, especially in physical education departments, tend to be slow adopters of AI-driven procurement automation compared to finance or corporate sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assistants can help an adapted PE specialist by auto-filling order forms, suggesting equipment based on student needs, checking compliance with accessibility standards, and tracking delivery status—substantially reducing manual administrative burden while a human reviews and approves. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by generating order lists, comparing vendors, tracking inventory, and auto-filling standard forms, though a human likely still finalizes and submits orders. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Requesting or ordering equipment following standard procedures is a highly structured, rule-based task with clear inputs (equipment needs, quantities) and outputs (purchase orders). Current AI systems can reliably classify equipment types, check inventory systems, generate orders, and route them through approval workflows without human intervention. |
| Task automatability | claude-sonnet-5 | 4/5 | Ordering equipment via standard procedures is a routine transactional task involving form-filling, catalog lookup, and procurement workflows that AI assistants and procurement software can largely handle end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Modest friction exists: organizations typically require human sign-off for budget authority, supplier relationships may be personalized, and some districts have legacy manual procedures. However, no legal or licensing barrier mandates human involvement in the ordering itself. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human perform equipment ordering; only mild organizational approval workflows or budget sign-off create friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI order processing costs pennies per transaction (API calls, inference, database lookups), while a human manually researching suppliers, filling forms, and routing approvals costs $15–50 per order. The cost advantage is at least an order of magnitude. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated procurement systems and AI-assisted ordering are far cheaper per transaction than having a specialist manually handle requisitions, though some setup and integration costs exist. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | E-procurement and inventory-management systems already integrate AI for purchase order generation and routing in many organizations. While production deployments exist in enterprise procurement, some variance in institutional procedures and legacy systems limits universal reliability to a 4 rather than 5. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Procurement and e-commerce automation tools, along with AI assistants integrated into purchasing systems, are already deployed in schools and districts for routine ordering tasks. |
Maintain inventory of instructional equipment, materials, or aids.
55CI 35–75 · exposure 55 · augmentation 63 · importance 3.3/5 · click for rater detail
Maintain inventory of instructional equipment, materials, or aids.
55| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 and specialized education settings show slower digitization and AI adoption than corporate or financial sectors; many facilities still rely on manual inventory sheets or basic spreadsheets rather than advanced automated systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education, especially specialized PE programs, is a slower-adopting sector for digital tools compared to information/finance industries, though basic inventory software use is common. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted inventory systems can help flag low stock levels, track maintenance schedules, and organize location data, meaningfully reducing clerical burden, though the specialist's judgment about equipment condition and student appropriateness remains essential. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted inventory systems can significantly reduce manual tracking effort, flag low stock, and generate reports, meaningfully boosting efficiency while a human oversees physical management. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While basic inventory tracking (counts, location logging, basic reordering) can be partially automated via barcode scanning or RFID systems, the task involves managing specialized adaptive equipment requiring domain knowledge about condition, suitability for different disabilities, and maintenance state—judgments that still require human oversight and cannot achieve 50% time savings end-to-end today. |
| Task automatability | claude-sonnet-5 | 4/5 | Inventory tracking is a structured data-entry and record-keeping task that AI-enabled inventory management systems can handle largely end-to-end, with humans only doing physical counts or scanning. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Schools and facilities must track equipment for liability, safety, and accountability reasons, and specialized adaptive equipment often requires expert judgment to ensure proper maintenance and allocation—creating organizational friction and oversight requirements that slow full automation. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There are no licensing, legal, or human-contact requirements tied to maintaining equipment inventories; it's a purely administrative task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI-augmented inventory systems (software + integration + ongoing human oversight of equipment decisions) cost roughly comparable to or slightly less than a part-time inventory specialist's time, but the specialized nature of adaptive equipment means substantial human labor remains unavoidable. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Off-the-shelf inventory software is inexpensive relative to staff time spent manually tracking equipment, offering substantial cost savings even after accounting for setup and maintenance. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Inventory management software and tools exist and are deployed in schools and facilities, but they typically require significant manual data entry, physical verification, and human judgment about equipment condition and appropriateness for specific student needs, limiting their reliability for this specialized domain. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Commercial inventory/asset management software with barcode/RFID scanning and automated reorder alerts is widely deployed in schools and similar organizations today, though full autonomy still requires some human input for physical counts. |
Write reports to summarize student performance, social growth, or physical development.
54CI 48–60 · exposure 58 · augmentation 75 · importance 4.0/5 · click for rater detail
Write reports to summarize student performance, social growth, or physical development.
54| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 education remains a laggard sector for automation; most schools use manual report writing or simple templates. Pilot projects exist but production AI-authored reports in adapted PE remain rare, with limited public evidence of deep displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education and adapted PE settings are generally slower-adopting sectors for AI tools compared to corporate or professional services environments, with pilots more common than production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist specialists by auto-generating report drafts, organizing observations from multiple data sources, and flagging trends—allowing the educator to focus on interpretation and personalized insights rather than transcription, thereby raising their productivity significantly. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up drafting, organizing observations into coherent narrative reports, letting the specialist focus on data accuracy and personalized judgment rather than writing from scratch. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can draft comprehensive reports on performance metrics, growth trends, and development observations with high fidelity by ingesting assessment data, attendance records, and notes. Minor human customization for nuance and personal context would likely still be required, but the core report generation easily achieves 50% time savings at equivalent quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft report narratives from structured performance data or notes with significant time savings, but requires human input of accurate observations and judgment calls about individual student progress that current systems cannot independently generate.of course. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Schools often require educator sign-off and assume human accountability for documented student progress; while there are no strict legal barriers preventing AI drafting, institutional inertia, liability concerns, and preference for human judgment slow adoption in K–12 environments. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement mandates a human write these reports, but special education documentation often carries compliance/legal obligations (IEP accuracy, FERPA) that create caution around fully automated content. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-generated reports cost pennies per output (inference + data integration), while a specialist writing a detailed report requires 30–60 minutes of billable time; the cost ratio favors automation substantially, especially at scale. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Drafting assistance is cheap per use, but the specialist's time to gather assessment data, verify accuracy, and personalize the report remains substantial, keeping overall cost savings moderate rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed AI systems (LLMs, report generation tools) can reliably produce narrative performance summaries from structured data, but most education sectors still rely on human authorship for legal/accountability reasons and customization beyond template outputs. Products exist but adoption in schools remains cautious. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | AI writing assistants and IEP/report-drafting tools are used in schools today to help teachers compose progress narratives, but adoption in adapted PE specifically is narrow and outputs still require substantial editing. |
Maintain thorough student records to document attendance, participation, or progress, ensuring confidentiality of all records.
36CI 30–43 · exposure 42 · augmentation 63 · importance 4.2/5 · click for rater detail
Maintain thorough student records to document attendance, participation, or progress, ensuring confidentiality of all records.
36| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 education and special education sectors adopt technology slowly; most adapted PE programs remain in traditional public schools with modest digitization. Adoption of autonomous documentation systems is laggard relative to professional services or finance, driven by budget constraints, regulatory caution, and educator resistance. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education administration is a historically slow-adopting sector for AI tools due to compliance concerns, budget constraints, and cautious IT policies around student data. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted forms, automated attendance logging, and draft progress summaries can meaningfully support a specialist's record-keeping workflow, reducing clerical time. However, the specialized judgment required in documenting adapted PE progress limits the transformative potential of augmentation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up drafting progress summaries, organizing attendance data, and flagging patterns, letting specialists focus more time on instruction while still reviewing and finalizing records. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Record-keeping can be partially automated (data entry, logging attendance), but maintaining thorough documentation of nuanced participation and progress in adapted PE requires human judgment about what constitutes meaningful progress for individualized students. Current AI cannot autonomously assess and document the qualitative aspects of student engagement and adaptation without significant human input. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft and organize records, auto-fill attendance logs, and summarize progress notes from structured inputs, but a human must still verify accuracy, input observations, and ensure confidentiality compliance, so only partial time savings are realized. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | FERPA and state education privacy laws impose strict requirements on student record handling; many jurisdictions legally require human educators to maintain and certify these records. Documentation of accommodations and progress also typically requires a licensed/certified specialist's professional judgment and sign-off for legal compliance. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Student records, especially those tied to special education (FERPA, IDEA), carry strict legal confidentiality and compliance requirements, requiring qualified staff oversight and limiting full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Automated record systems reduce clerical burden but require ongoing human oversight, validation, and judgment about what progress to document. The cost of a partial automation plus human review is comparable to or slightly less than having a specialist manage records, not dramatically cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Existing school administrative software is often already licensed and cheap per-use, so AI-driven documentation assistance offers moderate but not dramatic cost savings over current human/administrative workflows. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Learning management systems and record-keeping software exist and handle basic attendance and grade logging reliably, but comprehensive documentation of individual student progress in adaptive contexts requires human judgment that current products do not automate. Confidentiality infrastructure is mature, but the 'thorough documentation' element remains human-driven. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Ed-tech platforms (IEP software, LMS gradebooks) already automate attendance tracking and progress documentation, but confidentiality safeguards and nuanced progress narratives still require human oversight, limiting full reliability. |
Collaborate with other educational personnel to provide inclusive activities or programs for children with disabilities.
34CI 5–64 · exposure 41 · augmentation 63 · importance 4.3/5 · click for rater detail
Collaborate with other educational personnel to provide inclusive activities or programs for children with disabilities.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational sectors, especially special education, are relatively slow adopters of automation due to regulatory constraints, union presence, and strong institutional preference for human professional judgment in disability services; pilot programs exist but production displacement remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a low-digitization, highly regulated, human-centered sector with minimal AI adoption for interpersonal coordination tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist specialists by generating activity ideas adapted to specific disabilities, drafting inclusive program outlines, automating scheduling, and providing research-backed accommodation suggestions, which enhances specialist productivity while the human maintains critical judgment and oversight. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can help draft communication, organize schedules, or suggest adapted activity ideas, providing moderate support while humans handle the actual collaboration and decision-making. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | AI can generate inclusive activity designs, adapt curricula for disability categories, schedule programs, and coordinate logistics entirely autonomously, easily meeting 50% time savings at equal quality for the planning and administrative components of program development. |
| Task automatability | claude-sonnet-5 | 1/5 | This task centers on interpersonal collaboration, in-person coordination, and responsive program design for individual children with disabilities, which cannot be executed end-to-end by current AI systems. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal and ethical barriers are substantial: educational decisions for children with disabilities require licensed educators, individualized education plan (IEP) compliance, parental consent, and professional judgment on accommodation appropriateness; liability for injury or educational harm falls on credentialed staff, not AI systems. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education law (e.g., IDEA, IEP requirements) and professional certification standards require qualified human educators to collaborate on and be accountable for disability accommodations. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI inference and integration for program planning and activity generation cost substantially less than the specialist's loaded wage, especially when handling routine adaptation and scheduling tasks that would otherwise consume significant staff time. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the human collaboration involved, so there is no meaningful AI cost basis to compare against the human wage for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Current products exist for activity generation, curriculum adaptation, and scheduling, but they require significant human oversight to ensure educational appropriateness, disability-specific needs are met correctly, and participant safety; production deployment is limited to administrative aids rather than end-to-end program design. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs cross-staff collaboration or in-person inclusive program coordination for disabled students; this remains fundamentally a human relational and planning function. |
Communicate behavioral observations and student progress reports to students, parents, teachers, or administrators.
25CI 25–25 · exposure 25 · augmentation 63 · importance 4.1/5 · click for rater detail
Communicate behavioral observations and student progress reports to students, parents, teachers, or administrators.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 education remains a conservative, low-digitization sector with strong preference for human judgment in student communication. Early adopters are experimenting with AI-assisted drafting, but production deployment is rare and cautious; most districts remain in pilot or rejection phases. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education and adapted PE remain low-digitization, high-human-contact environments with slow AI adoption compared to sectors like finance or tech. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by drafting initial report structures, summarizing observation logs, or generating standard template text, thereby reducing specialist writing time. However, the specialist retains full responsibility for accuracy and tone, limiting the scope of productivity gain to roughly 20–30% time savings on clerical drafting work. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by helping draft, organize, and clarify progress reports and communications based on the specialist's notes and observations, saving time while the human remains central to observation and judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can draft progress reports and summarize behavioral observations at speed, but the task fundamentally requires nuanced, context-aware communication tailored to individual students and sensitive to family/organizational dynamics. Current systems cannot reliably capture the subjective professional judgment and relationship-building essential to effective behavioral communication, nor can they independently gather and validate the observational data required. |
| Task automatability | claude-sonnet-5 | 2/5 | Drafting progress reports can be assisted by AI, but accurately communicating nuanced behavioral observations from direct physical education sessions requires firsthand observation and judgment that AI cannot perform end-to-end today.5 |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | School districts, parents, and regulatory frameworks require that behavioral and progress communications be authored and accountable to a licensed educator. FERPA and local education policies typically mandate human professional responsibility for student records; AI-generated reports without specialist sign-off face legal and liability risks, creating substantial organizational friction. |
| Adoption barriers | claude-sonnet-5 | 4/5 | IEP-related communications and special education reporting often carry legal/regulatory requirements (e.g., IDEA compliance) requiring qualified professionals to observe, interpret, and sign off on student progress, creating strong barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference for report drafting is cheap, but the specialist must review, edit, and validate all output to ensure accuracy and appropriateness—consuming most of the time savings. Integration overhead and human oversight costs bring the effective cost per finished, deployable report close to or exceeding the direct labor cost of writing it from scratch. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI can cheaply help format or draft text, the human observation, data-gathering, and relationship-based communication component remains costly and irreplaceable, keeping overall cost savings modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While text-generation tools can produce draft communications, no deployed product reliably handles the full task of generating trustworthy behavioral reports that specialists can publish without substantial human review and customization. LLM-based report generators exist in early-stage education pilots but show high error rates in nuance and factual accuracy about specific students. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI writing tools exist to help draft reports, but no deployed product independently observes student behavior in adapted PE settings and generates reliable, contextually accurate progress communications without a human's direct input. |
Prepare lesson plans in accordance with individualized education plans (IEPs) and the functional abilities or needs of students.
25CI 25–25 · exposure 25 · augmentation 63 · importance 4.0/5 · click for rater detail
Prepare lesson plans in accordance with individualized education plans (IEPs) and the functional abilities or needs of students.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Special education remains heavily human-dependent and risk-averse; adoption of AI in IEP planning is minimal. Schools move cautiously on anything touching legally mandated special education, and most districts lack infrastructure or willingness to deploy AI for IEP-critical tasks. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a slower-adopting, human-intensive sector with limited AI tool penetration into IEP-linked instructional planning compared to corporate or tech sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by suggesting activity modifications, generating template language, or helping organize lesson structures, reducing the specialist's drafting time. However, the core judgment—matching activities to individual functional abilities and IEP goals—remains primarily human-driven. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up drafting of lesson plan structures, generate activity ideas adapted for various abilities, and organize goals, letting the specialist focus on individualization and compliance review. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft generic lesson plan templates and suggest activities, creating compliant IEP-aligned plans requires deep knowledge of each student's specific functional abilities, documented needs, and legal IEP requirements. Current AI systems cannot reliably integrate complex student data with regulatory compliance demands at the quality level required for special education. |
| Task automatability | claude-sonnet-5 | 2/5 | Drafting lesson plan text can be assisted by AI, but aligning content precisely to a specific student's IEP goals, functional abilities, and physical/motor needs requires nuanced professional judgment that current systems cannot reliably automate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | IEP compliance is federally mandated under IDEA, and specialists have legal responsibility for ensuring plans meet each student's documented needs. Schools face liability if plans fail to address IEP goals, and human specialists must ultimately sign off on and defend the instructional design in IEP meetings. |
| Adoption barriers | claude-sonnet-5 | 4/5 | IEPs are legal documents requiring input and sign-off from certified special education professionals, with compliance and liability requirements that constrain full automation of plan authorship. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted drafting of lesson plans is relatively cheap, but the specialist must still heavily review, customize, and validate against IEP documents and student needs. The human oversight burden remains substantial, limiting cost savings to perhaps 20–30% rather than approaching order-of-magnitude reduction. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply draft generic templates, but the specialist still must verify IEP compliance, adapt for functional limitations, and take liability, so net cost savings versus the human specialist's time are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some AI writing tools can generate lesson plan outlines, but no mature product reliably produces IEP-compliant, individualized adapted PE plans that meet legal standards. Existing systems lack the specialized domain knowledge and cannot access/interpret individual student IEPs and functional assessments at the required depth. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Generic AI lesson-plan generators exist but no deployed product reliably synthesizes IEP-specific legal/medical documentation with functional assessment data to produce compliant, individualized adapted PE plans at scale. |
Review adapted physical education programs or practices to ensure compliance with government or other regulations.
25CI 25–25 · exposure 25 · augmentation 50 · importance 3.5/5 · click for rater detail
Review adapted physical education programs or practices to ensure compliance with government or other regulations.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 education and adaptive PE programs remain relatively low-digitization sectors with limited AI adoption. While large districts may pilot compliance tools, production deployment of autonomous compliance systems in schools remains rare; adoption patterns lag far behind professional services or finance. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Special education and adapted PE are a niche, underfunded, and less digitized sector of education with limited AI tool adoption for compliance-specific tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by automatically flagging regulatory passages, summarizing policy changes, and cross-checking program materials against regulation templates, reducing the human specialist's manual reading and organization burden. The specialist retains judgment and sign-off, making this a moderate augmentation scenario. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist by summarizing regulations, drafting checklists, or flagging inconsistencies in documentation, meaningfully speeding up parts of the review process while the specialist retains final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Reviewing compliance involves parsing regulatory documents, cross-referencing program materials, and flagging deviations—partially automatable with document analysis AI. However, the judgment required to interpret nuanced regulatory language in context of specific educational environments, combined with the need for domain expertise in adapted PE practices, means AI cannot reliably perform end-to-end compliance review at 50% time savings without significant human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Compliance review requires interpreting regulations, applying them to specific student cases, and exercising professional judgment about individual needs, which current AI cannot fully replicate end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational compliance review carries legal and liability weight; school districts typically require a qualified specialist (often state-credentialed in adapted PE) to sign off on regulatory adherence. Additionally, accountability for compliance failures creates organizational and legal barriers that prevent full automation without human authority. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Compliance with government regulations (e.g., IDEA, Section 504) typically requires qualified, often certified professionals to attest to and be accountable for compliance, creating strong legal and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for document analysis and compliance checking are relatively inexpensive, but the need for skilled human compliance specialists to review findings, interpret ambiguities, and make final determinations means total cost per task remains comparable to or higher than direct human review. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI could cheaply assist with document review, the human oversight, contextual judgment, and liability required for compliance sign-off keep overall costs comparable to human-led review. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can extract and summarize regulatory text and identify some inconsistencies, no mature deployed product reliably performs comprehensive compliance auditing of educational programs across multiple regulatory frameworks. Existing tools are document-management aids rather than true autonomous compliance validators in educational contexts. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools can help search and summarize regulatory text or flag potential compliance gaps, but no deployed product reliably performs full compliance review of adapted PE programs in production. |
Assist in screening or placement of students in adapted physical education programs.
21CI 18–25 · exposure 20 · augmentation 50 · importance 4.3/5 · click for rater detail
Assist in screening or placement of students in adapted physical education programs.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | School districts, particularly those serving special education populations, are traditionally slow to adopt emerging technologies; adoption is heavily constrained by regulatory compliance requirements, liability concerns, and limited digitization of assessment workflows. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Special education and adapted PE is a low-digitization, highly individualized service sector with minimal AI adoption for placement decisions. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could moderately assist specialists by helping organize student data, flagging patterns in medical histories, or scoring standardized questionnaire responses, allowing the specialist to focus on direct observation and clinical judgment without fully automating the screening decision. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help organize assessment data, flag information from records, or draft reports, providing moderate assistance while the specialist retains decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with processing questionnaires and medical history forms, adapted PE placement requires nuanced assessment of individual student abilities, disabilities, and learning styles through direct observation and multi-factor judgment that current systems cannot reliably perform end-to-end at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Screening and placement require synthesizing medical records, physical assessments, and observed motor abilities into individualized judgments, which current AI cannot reliably perform end-to-end without heavy human oversight.dns |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal and regulatory barriers are substantial: special education placement decisions are heavily regulated under IDEA and Section 504, require documented assessment by qualified professionals, parental involvement, and individualized education plans (IEPs) that must be signed by licensed educators, limiting full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Placement decisions often tie into IEP/IDEA compliance and require credentialed professionals to assess and sign off, creating strong regulatory and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Implementation would require custom integration with school systems, medical records, and compliance documentation, making the total cost of an AI solution likely comparable to or exceed the cost of a specialist's time for this screening task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Any AI use would still require expert review of assessments and legal documentation (IEP-related), so cost savings versus a qualified specialist are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs student screening and placement for adapted PE programs in production; assessment tools exist but require human specialist oversight and cannot replace the individualized, multi-modal evaluation process. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs adapted PE screening or placement decisions in real school settings; this remains a specialized clinical/educational judgment task. |
Write or modify individualized education plans (IEPs) for students with intellectual or physical disabilities.
19CI 18–20 · exposure 25 · augmentation 63 · importance 4.1/5 · click for rater detail
Write or modify individualized education plans (IEPs) for students with intellectual or physical disabilities.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools are conservative sectors with low digital sophistication and strong compliance requirements. Adoption of AI in IEP writing is minimal; most district practices remain human-driven, and regulatory risk deters rapid deployment of automated or semi-automated IEP generation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Special education is a slow-adopting, highly regulated, relationship-driven sector with limited AI integration into IEP workflows beyond isolated pilot tools. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by organizing assessment data, generating template language, checking for completeness, and drafting boilerplate sections, helping specialists work faster and more systematically. However, the augmentation is bounded because the core work—interpreting student needs and making appropriate placements—remains fundamentally human. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up drafting of goal language, progress summaries, and boilerplate sections, letting specialists focus on individualized clinical judgment and compliance review. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Writing IEPs requires understanding complex legal requirements, individual student needs, disability assessment data, and collaborative input from parents and specialists. While AI could assist in drafting sections or organizing information, the task demands nuanced judgment about appropriateness, legality, and personalization that current systems cannot reliably provide end-to-end without substantial human revision. |
| Task automatability | claude-sonnet-5 | 2/5 | IEP writing requires synthesizing student-specific assessment data, clinical judgment about physical/cognitive needs, and legally binding goal-setting that current AI cannot reliably perform end-to-end; AI can draft templates but cannot independently determine appropriate individualized goals with equal quality.6 |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | IEPs are legally mandated documents under IDEA; federal law requires qualified professionals (special educators, sometimes licensed clinicians) to author or sign off on them. Schools face liability for inadequate IEPs, and parent involvement is legally required, creating hard institutional and regulatory barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | IEPs are legally mandated documents under IDEA requiring qualified professional authorship/sign-off, with strict compliance and due-process implications, making unsupervised AI authorship legally prohibited. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI draft assistance may reduce some clerical time, but IEP writing demands specialized human judgment (special educators, parents, medical input). The loaded cost of the specialist performing or heavily reviewing the work remains higher than marginal AI inference, making the ratio unfavorable for full displacement. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI drafting tools are cheap per use, but the mandatory specialist review, compliance checking, and liability oversight required keep all-in cost close to human-only costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably generates legally compliant, educationally sound IEPs independently. Template-based systems exist, but producing a complete, defensible IEP that meets IDEA requirements and reflects genuine student assessment requires human expertise; products in use are primarily drafting aids, not autonomous IEP writers. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some ed-tech products offer IEP drafting assistance and goal-bank suggestions, but no deployed product reliably generates compliant, individualized, legally defensible IEPs without extensive specialist review. |
Attend in-service training, workshops, or meetings to keep abreast of current practices or trends in adapted physical education.
16CI 7–25 · exposure 13 · augmentation 50 · importance 4.0/5 · click for rater detail
Attend in-service training, workshops, or meetings to keep abreast of current practices or trends in adapted physical education.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adapted physical education is a specialized, field-based profession with moderate digitization; while some sectors use AI for training content curation, the human attendance requirement limits broad displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education sector, especially special/adapted PE, has slower AI adoption for compliance-driven professional development activities. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by summarizing conference materials, flagging relevant trends, or organizing learning resources, meaningfully boosting a specialist's ability to stay informed without replacing the attendance itself. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help identify relevant workshops, summarize research/trends, and prepare notes, aiding preparation and follow-up around the training but not replacing attendance. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Attending training and staying current requires human presence, engagement, and contextual judgment about relevance to one's practice. AI cannot meaningfully attend meetings or workshops in the required sense. |
| Task automatability | claude-sonnet-5 | 2/5 | AI could help find/summarize training content but cannot itself physically attend or fulfill the professional development requirement, which involves real-time interaction and credentialing.dumo Most of the task's value lies in attendance and engagement, not information retrieval alone.The core act remains human. . |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Professional development and staying current in specialized practice areas are often mandated by licensing bodies, employer requirements, or certification boards, creating regulatory and organizational friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Continuing education and in-service training requirements are often tied to certification/licensure maintenance, requiring documented personal attendance and participation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for summarizing training content or trend monitoring cost less than human time, but the core task of actual attendance and engagement remains human-dependent, limiting overall cost displacement. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task, so cost comparison favors the human doing the attendance themselves. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can summarize training materials or curate information about trends, no deployed product can genuinely attend or participate in interactive workshops; AI assistance is limited to pre- or post-processing content. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product attends training or workshops on a professional's behalf; this is inherently a human participation requirement. |
Assess students' physical progress or needs.
16CI 9–23 · exposure 13 · augmentation 50 · importance 4.3/5 · click for rater detail
Assess students' physical progress or needs.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools are laggard sectors with limited digitization budgets, fragmented IT infrastructure, and strong cultural reliance on human specialists for student assessment. Adoption of AI in special education contexts is minimal and restricted by resource constraints and regulatory caution. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Adapted physical education is a small, highly specialized, in-person educational field with minimal AI tool adoption or digitization of core assessment practices. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted video review or automated baseline tracking could help specialists organize and compare measurement data over time, reducing administrative burden. However, the interpretive core—judgment about student needs and progress—remains human-centered, limiting augmentation impact. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by organizing assessment data, tracking progress over time, generating reports, or suggesting adaptive exercise plans, but the core physical assessment remains human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Assessing students' physical progress requires observing nuanced movement patterns, understanding individual developmental context, and making clinical judgments about functional capacity. Current AI systems lack the embodied perception and real-time interactive assessment capability needed to evaluate physical needs reliably without human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Assessing physical progress requires hands-on observation of motor skills, coordination, and adaptive needs for students with disabilities, which current AI cannot perform end-to-end without human observation and clinical judgment.irical. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Adapted physical education assessment involves licensed or credentialed specialists in many jurisdictions, and involves duty of care for potentially vulnerable students with disabilities. Liability and safety considerations create substantial organizational and legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education law (e.g., IDEA) requires qualified professionals to conduct and certify assessments for individualized education plans, creating strong legal and professional barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Setting up video analysis systems, integrating them with student records, and requiring human review of AI outputs makes the total cost comparable to or exceeding direct specialist assessment, especially given the low volume per school and customization needed. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Since AI cannot substitute for the physical observation core of this task, any AI use is supplementary (e.g., data logging) rather than a cost-replacing solution, so cost comparison favors the human-led process. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While computer vision can detect gross movement patterns from video, no deployed product reliably assesses adapted physical education progress independently. Existing systems require significant human interpretation and cannot adapt assessment in real time based on student response or safety concerns. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product independently conducts physical/motor assessments of students with disabilities; this remains a human specialist function requiring direct physical observation and interaction. |
Evaluate the motor needs of individual students to determine their need for adapted physical education services.
14CI 5–23 · exposure 13 · augmentation 25 · importance 4.3/5 · click for rater detail
Evaluate the motor needs of individual students to determine their need for adapted physical education services.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational sectors, especially special education, remain digitization laggards with slow AI adoption; budget constraints, regulatory conservatism, and the high-touch nature of individualized education planning create minimal production-level AI displacement in this domain. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Adapted physical education and special education services are a highly specialized, low-digitization niche within education, a sector generally slow to adopt AI for hands-on assessment tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist by automating video tagging or test scoring if a specialist provides raw data, but the core task—clinical judgment about individual student motor needs—is not substantially enhanced by current AI tools. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with documentation, tracking progress data, or suggesting assessment frameworks, but it offers limited assistance for the core physical observation and clinical judgment involved. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Evaluating motor needs requires individualized clinical judgment, observation of student movement patterns, understanding developmental baselines, and assessment of complex factors like cognitive ability, motivation, and learning environment—tasks requiring human expertise that current AI systems cannot perform end-to-end with comparable quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires hands-on observation of a child's physical movement, motor skills, and functional abilities in real time, which current AI cannot perform directly; some data analysis or documentation support is possible but the core assessment is physical and interactive.time saving is limited. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: adapted PE evaluation often requires licensed physical educators or special educators; decisions directly affect educational placement and services, creating high liability; regulations in special education (IDEA) typically mandate human professional judgment; and parent/guardian preference for human evaluation of their child's needs is substantial. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education law (IDEA) requires qualified professionals to conduct and certify assessments determining eligibility for adapted services, creating strong legal and procedural barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for motion capture or assessment support exist but require human administration, interpretation, and oversight; the total cost (infrastructure, personnel, liability) approaches or exceeds the cost of a specialist conducting the evaluation directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical evaluation itself, so cost comparison favors the human specialist entirely; any AI use would be a minor supplement, not a replacement. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with data collection (video analysis of movement) or scoring standardized motor tests if performed by humans, no deployed product reliably evaluates motor needs independently or makes clinical referral decisions for adapted PE services in production use. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products conduct autonomous motor skill evaluations of students for adapted PE placement; this remains a specialized, hands-on clinical assessment task performed by trained specialists. |
Adapt instructional techniques to the age and skill levels of students.
12CI 5–19 · exposure 13 · augmentation 50 · importance 4.9/5 · click for rater detail
Adapt instructional techniques to the age and skill levels of students.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | School districts, especially special education programs, remain low-digitization sectors with slow AI adoption; physical education and student safety remain areas where human professionals are strongly preferred and protected by regulation and institutional practice. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 physical education and adaptive/special education is a low-digitization, highly physical, hands-on sector with minimal AI agent deployment for instructional delivery. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist a specialist by generating adaptation suggestions, tracking student progress data, or recommending evidence-based modifications—useful support tools—but the core task of real-time observation, judgment, and hands-on instruction remains fundamentally human-centered. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help specialists plan lessons, suggest adapted activities based on skill level, and generate individualized education materials, though the live instructional adaptation itself remains human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Adapting instructional techniques to individual students requires real-time assessment of physical capabilities, learning styles, and psychological factors that demand human judgment and direct observation—something current AI cannot do end-to-end in a physical education setting. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires real-time observation of individual students' physical abilities, motor skills, and behavior to adjust teaching on the fly, which current AI cannot perceive or execute in a physical classroom/gym setting.}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Adapted physical education specialists are typically licensed educators bound by state certification requirements; schools have legal duties to provide individualized instruction to students with disabilities, and direct human-instructor presence is often mandated for safety and legal liability reasons. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Adapted PE for students with disabilities typically requires certified/licensed specialists under IEP and special education law, with human judgment and legal accountability for individualized instruction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task requires specialized professional expertise (adapted PE certification, knowledge of disabilities and motor development); the cost of AI systems plus required human oversight and integration would exceed the wage of the specialist performing the work directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute delivering this in-person adaptive instruction, so cost comparison favors the human specialist by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can suggest generic differentiation strategies or generate adaptation frameworks based on text descriptions, no deployed system reliably assesses a student's live performance and adjusts physical education instruction in real time without extensive human oversight. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs live physical education instruction adaptation for students with disabilities; this remains an in-person, embodied teaching task. |
Instruct students, using adapted physical education techniques, to improve physical fitness, gross motor skills, perceptual motor skills, or sports and game achievement.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.8/5 · click for rater detail
Instruct students, using adapted physical education techniques, to improve physical fitness, gross motor skills, perceptual motor skills, or sports and game achievement.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools and special education programs are laggard sectors on AI adoption; PE instruction is inherently physical and hands-on. No meaningful automation or agent adoption is occurring in production educational settings for this task. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Special education and physical education sectors show minimal AI adoption for direct instruction, especially for physically supervised, disability-specific interventions. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While video-based movement analysis or workout planning tools might offer minor support, they cannot materially assist the core teaching act of live instruction, hands-on correction, safety monitoring, and interpersonal motivation that specialists provide to students with disabilities. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with tracking progress, generating individualized exercise plans, or suggesting adapted activities, but it offers limited direct assistance during the hands-on instructional process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time physical demonstration, correction of body positioning, safety monitoring of students with disabilities, and dynamic adaptation to individual needs—none of which AI can perform end-to-end today. The core work is hands-on coaching and personalized movement instruction that demands human presence and physical interaction. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires live, hands-on physical instruction, spotting, physical adjustments, and real-time adaptation to students with diverse disabilities, none of which current AI systems can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Adapted PE instruction for students with disabilities is legally governed (IEP requirements under IDEA), requires credentialed specialists, involves duty of care and physical safety, and mandates human-to-student contact for demonstration, spotting, and behavioral support. These are hard legal and organizational barriers. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Many jurisdictions require certified/licensed adapted PE specialists, IEP compliance, and direct supervision for student safety and legal accountability, creating strong barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems cannot perform this task, so cost comparison is moot. The task requires a licensed specialist whose loaded wage is the baseline; automation is not feasible at any price point today. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the physical presence, safety supervision, and hands-on adaptation required, so there is no viable AI cost comparison—human specialists remain the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously deliver adapted PE instruction to students. While video analysis tools exist, they cannot replace the live observation, one-on-one feedback, physical spotting, and safety oversight that this role demands. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product delivers adapted physical education instruction to students with disabilities; this remains firmly in the domain of trained human specialists working in person. |
Provide students positive feedback to encourage them and help them develop an appreciation for physical education.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.8/5 · click for rater detail
Provide students positive feedback to encourage them and help them develop an appreciation for physical education.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Physical education departments operate in traditional school settings with strong cultural and regulatory expectations that teachers, not systems, build student confidence and motivation. Adoption of automation for this explicitly relational task is extremely limited and not part of mainstream institutional adoption patterns. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 adapted physical education is a low-digitization, physically-embodied service sector with minimal AI agent adoption in direct instructional delivery. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AI could potentially draft generic encouragement prompts or flag patterns in student engagement for a human specialist to review, current systems offer minimal augmentation value for a task that depends on authentic, real-time human presence, observation, and relationship-building during physical activity. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help specialists plan lessons, track progress notes, or suggest encouragement phrasing, but it offers little direct assistance during the actual real-time feedback delivery to students. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Providing meaningful, personalized positive feedback that genuinely encourages individual students and fosters appreciation for physical education requires understanding each student's emotional state, learning history, and intrinsic motivation—capabilities that current AI systems cannot reliably execute without direct human observation and relationship. This task is fundamentally about human connection and motivational psychology in real-time classroom contexts, which AI cannot replicate end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time, physically present interaction with students during movement activities, building rapport and motivational relationships that current AI cannot perform end-to-end.RelativeLayout AI cannot physically observe and coach students in a gym setting. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Schools and families expect and legally require qualified, licensed educators to be responsible for student motivation and social-emotional development. Automated feedback systems cannot assume the accountability, duty of care, and professional judgment that a licensed specialist provides; human sign-off and direct educator involvement remain hard requirements. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Working with students with disabilities typically requires specialized certification/licensure, physical presence, and duty-of-care obligations that create strong barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of developing, deploying, and overseeing an AI system to deliver personalized encouragement to students would substantially exceed the loaded cost of a specialist educator already present in the classroom performing this task naturally as part of their role. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this in-person, embodied task, so cost comparison favors the human by default; any AI tool would only supplement, not replace, at added cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably provide personalized, emotionally-calibrated positive feedback to students in physical education settings that would meaningfully encourage them or build appreciation for the subject. AI-generated generic praise lacks the authenticity and relational trust required for the task's core outcome. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product provides in-person physical education coaching and motivational feedback to students with disabilities during live physical activity. |
Advise education professionals of students' physical abilities or disabilities and the accommodations required to enhance their school performance.
3CI 0–5 · exposure 0 · augmentation 38 · importance 4.2/5 · click for rater detail
Advise education professionals of students' physical abilities or disabilities and the accommodations required to enhance their school performance.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Education, particularly special education services, remains a laggard sector for AI adoption; school districts are heavily regulated, budget-constrained, and resistant to automating decisions about student accommodations that carry legal and developmental implications. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Special education and adapted PE are low-digitization, highly interpersonal fields with minimal AI agent adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could modestly assist by retrieving reference information about disabilities or accommodation examples, but the core task of assessing individual students and advising professionals remains fundamentally dependent on human professional judgment and direct interaction. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help specialists draft accommodation reports, summarize assessment data, or research best practices, but the core advising and assessment remain human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires nuanced clinical judgment, one-on-one assessment of individual student needs, and professional decision-making about accommodations. No current AI system can independently evaluate a student's physical abilities, disabilities, and educational context to generate reliable, personalized accommodation advice without human expertise. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires in-person clinical assessment of a specific student's physical abilities, professional judgment, and interpersonal advising to teachers, none of which current AI can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task is protected by licensing requirements for special educators/specialists, legal liability for accommodation decisions affecting student safety and educational access, regulatory requirements under IDEA and Section 504, and the requirement that qualified professionals sign off on individualized education plans. |
| Adoption barriers | claude-sonnet-5 | 4/5 | This role often requires specialized certification/licensure and legal compliance (e.g., IEP/504 processes), creating strong regulatory and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The specialized expertise, liability, and one-on-one nature of assessment and advice means an adapted PE specialist's cost cannot be matched by AI systems, which would still require human oversight, verification, and final responsibility for recommendations. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the physical assessment and professional consultation involved, so there is no comparable AI cost baseline; the human expert remains necessary. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | While AI can retrieve general information about disabilities and accommodations, no deployed product reliably performs the core task of independently assessing individual students and advising education professionals on specific accommodations. This requires direct observation, clinical reasoning, and professional accountability. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product conducts individualized physical/disability assessments and advises school staff on accommodations; this remains a human specialist function. |
Provide individual or small groups of students with adapted physical education instruction that meets desired physical needs or goals.
0CI 0–0 · exposure 0 · augmentation 38 · importance 4.8/5 · click for rater detail
Provide individual or small groups of students with adapted physical education instruction that meets desired physical needs or goals.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | School districts, especially special education departments, adopt technology slowly and face budget constraints. The requirement for certified specialists and the safety/liability risks mean production AI adoption in this space remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Physical education and special education services are a low-digitization, high-human-contact sector with minimal AI adoption for direct instructional delivery. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could modestly assist by analyzing video recordings of student movement to suggest form corrections or track progress metrics, but the core task—live instruction, physical demonstration, and real-time adjustment—remains human-dependent. Augmentation potential is limited to back-office analysis, not transformative productivity gain. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help with lesson planning, tracking progress data, and suggesting individualized adaptive exercises, but the physical instruction itself is not augmented by AI in real time. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time physical demonstration, hands-on correction of movement patterns, and dynamic adaptation to individual students' evolving abilities and disabilities. AI cannot currently perform these core elements—demonstrating exercises, physically adjusting posture, or responding to live performance in a gym setting. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires live, hands-on physical instruction, real-time observation of movement and adaptation to disability-specific needs, which current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Adapted PE instruction is typically mandated under IDEA (Individualized Education Program) and Section 504 plans, requiring a licensed or certified specialist to assess, plan, and deliver instruction. Liability and legal requirements for students with disabilities create hard barriers to full automation or unsupervised AI deployment. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Special education law (e.g., IDEA/IEP requirements) mandates qualified, often licensed, human specialists to assess and deliver adapted PE, creating strong legal and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot replace the in-person, hands-on nature of adapted PE instruction. The cost of any AI assistant (video monitoring, analysis, integration) would add to rather than replace the specialist's labor, making the total cost higher than human-only provision. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute delivering physical instruction, so any AI cost comparison is moot; the human specialist remains the only cost-effective option for actual delivery. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably delivers adapted PE instruction to students in person. While video-based fitness coaching exists, it lacks the personalized assessment and physical intervention critical to adapted PE, and no production system addresses the individualized accommodation needs of students with disabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product provides physical, in-person adapted PE instruction to students with disabilities; this remains entirely a human physical presence task. |
Establish and maintain standards of behavior to create safe, orderly, and effective environments for learning.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.7/5 · click for rater detail
Establish and maintain standards of behavior to create safe, orderly, and effective environments for learning.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Education remains a low-automation sector for instructional and behavioral management tasks; adoption of AI in classroom behavior management is negligible in production, with only isolated pilots. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Special education and adapted physical education is a low-digitization, high-touch physical service sector with minimal AI deployment for behavior management tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might assist with minor elements such as logging behavioral incidents or flagging patterns in recorded data, but it does not meaningfully augment the core task of real-time standard-setting and enforcement, which depends on human authority and presence. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help draft behavior plans or track incident patterns for later analysis, but it offers minimal real-time assistance during active supervision and instruction. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Establishing and maintaining behavioral standards requires real-time judgment about individual students, contextual awareness of classroom dynamics, and adaptive responses to complex social-emotional situations—all dependent on human presence and authority. No current AI system can manage this end-to-end without continuous human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | Establishing behavioral standards and maintaining classroom order requires real-time physical presence, authority, and relationship-building with students with disabilities, which AI cannot perform. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task is legally and professionally gatekept: only a licensed educator can establish behavioral standards and maintain a safe learning environment; liability, duty of care, and state credentialing requirements prevent delegation to or replacement by AI. |
| Adoption barriers | claude-sonnet-5 | 5/5 | This requires licensed/certified educators physically present with legal responsibility for student safety and behavior management, an unavoidable human-contact and supervisory requirement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task requires a credentialed human professional (adapted PE specialist) onsite; there is no AI deployment that substitutes for or reduces the cost of this presence, and oversight alone would add cost. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this function, so the comparison defaults to the human being the only viable and thus cheaper option in practice. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system reliably manages behavioral standards in live educational settings; this task fundamentally requires a licensed educator present to observe, intervene, and take responsibility for student safety and conduct. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product manages live classroom behavior management or physical safety enforcement in adapted PE settings; this remains entirely human-executed. |
Provide adapted physical education services to students with intellectual disabilities, autism, traumatic brain injury, orthopedic impairments, or other disabling condition.
0CI 0–0 · exposure 0 · augmentation 38 · importance 4.7/5 · click for rater detail
Provide adapted physical education services to students with intellectual disabilities, autism, traumatic brain injury, orthopedic impairments, or other disabling condition.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational institutions, particularly in special education, are laggard adopters of automation technology, and there is strong cultural and legal preference for licensed professionals to deliver direct services to vulnerable student populations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education and physical instruction is a low-digitization, high-touch sector with minimal AI deployment for direct instructional delivery. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could offer limited assistance through exercise video libraries, progress tracking, or adaptive activity suggestions, but the core task of in-person instruction, safety management, and real-time response to student needs remains human-dependent. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help specialists with individualized lesson planning, tracking progress data, generating adapted exercise ideas, and documentation, but cannot assist during actual physical instruction significantly. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time physical interaction, individual assessment, safety monitoring, and personalized adaptation of exercises—all core to human-led adapted PE. Current AI systems cannot autonomously manage physical activity sessions, assess physical capabilities, or ensure participant safety in a classroom setting. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on, physically supervised instructional task requiring real-time assessment of a student's motor abilities, safety monitoring, and physical assistance, which current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task is heavily protected by licensing requirements (many states require certification or licensure for adapted PE specialists), mandatory human contact with vulnerable populations (students with disabilities), and liability concerns around physical activity and injury risk. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Adapted PE requires certified/licensed specialists, IEP compliance, legal accountability for student safety and disability accommodation, and direct physical supervision—strong regulatory and liability barriers block automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI infrastructure, integration, and required human oversight would not be competitive with a single adapted PE specialist's wage, especially given the need for constant human monitoring and liability coverage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical, supervisory task, so cost comparison favors the human specialist entirely; any AI role is only supplementary (e.g., planning tools). |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably delivers adapted physical education services to students with disabilities. AI lacks the embodied presence, real-time responsiveness to individual needs, and liability tolerance required for direct service delivery in this context. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product delivers physical education instruction or physical support to students with disabilities in gyms or therapy settings; this remains firmly in the human domain. |
Related occupations — Educational Instruction & Library
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.