Special Education Teachers, Middle School
25-2057.00Median wage $66,810/yr95,200 employed (US)Rank #738 of 923 scored · top 80% by substitution
Teach academic, social, and life skills to middle school students with learning, emotional, or physical disabilities. Includes teachers who specialize and work with students who are blind or have visual impairments; students who are deaf or have hearing impairments; and students with intellectual disabilities.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution17
Exposure17
Augmentation51
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
40 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
0%
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%17
panel mean rating 1.7/5 → substitution pressure 17/100
Technical feasibility todayw 20%16
panel mean rating 1.7/5 → substitution pressure 16/100
Cost vs. human wagew 15%17
panel mean rating 1.7/5 → substitution pressure 17/100
Adoption barriersw 20%inverted — strong barriers lower the score20
panel mean rating 4.2/5 (barrier strength) → substitution pressure 20/100
Sector adoption velocityw 10%14
panel mean rating 1.5/5 → substitution pressure 14/100
Task breakdown (40 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.
Select, store, order, issue, and inventory classroom equipment, materials, and supplies.
59CI 52–66 · exposure 55 · augmentation 75 · importance 2.9/5 · click for rater detail
Select, store, order, issue, and inventory classroom equipment, materials, and supplies.
59| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Schools increasingly adopt centralized inventory management and procurement platforms; many districts now use cloud-based systems for ordering and tracking. This is a high-digitization, information-centric task in education, and adoption in public schools and especially well-resourced districts is accelerating. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education, especially special education administrative tasks, is a slow-adopting sector for AI tools compared to finance or professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | Automated inventory dashboards, smart reordering alerts, and AI-suggested material recommendations based on student IEPs and classroom usage patterns substantially raise teacher productivity by reducing time spent on manual counts and low-stakes ordering decisions, while teachers remain responsible for final selection and student-specific customization. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-driven inventory and ordering systems can meaningfully reduce time teachers spend tracking and reordering supplies, freeing time for instructional planning. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Roughly half of this task—inventory tracking, reordering based on thresholds, and data entry—could be automated with current warehouse/inventory management systems and AI-driven demand forecasting. However, physical selection and issuance of items require human judgment about classroom context and student needs, which limits full end-to-end automation to around 40–50% time savings. |
| Task automatability | claude-sonnet-5 | 3/5 | Inventory tracking, ordering, and reordering can be substantially automated with inventory management software and AI-assisted procurement, though physical storage and selection of specialized materials for students' IEP needs require human judgment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers prevent automation of inventory tasks; schools face only moderate organizational friction (staff retraining, system integration). Special education compliance does not legally mandate human selection of routine supplies—only appropriate individualized education planning, which is separate from logistics. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier restricts using software for ordering/inventory, though schools' procurement approval chains and budget systems create some organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Cloud-based inventory platforms (e.g., Skyware, Infinite Campus modules) cost substantially less per transaction than a teacher's loaded wage ($60k–80k annually), especially once amortized across a school's inventory operations. The cost ratio heavily favors automation for the routine portions. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Software-based inventory tools are cheap relative to teacher time spent on this administrative subtask, but integration and teacher oversight still require nontrivial ongoing cost given the specialized nature of materials. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Inventory management software, barcode scanning, and automated reordering systems are mature, widely deployed products in schools and enterprises. These reliably handle the data-driven portions (tracking, ordering) at scale, though human oversight for special education contexts remains necessary for decision-making on which specific materials suit individual students. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Off-the-shelf inventory and procurement management tools are widely deployed in schools and businesses, but not specifically tailored or adopted for special education classroom material management at scale. |
Modify the general education curriculum for students with disabilities, based upon a variety of instructional techniques and instructional technology.
55CI 25–85 · exposure 58 · augmentation 88 · importance 4.3/5 · click for rater detail
Modify the general education curriculum for students with disabilities, based upon a variety of instructional techniques and instructional technology.
55| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Schools are piloting AI curriculum tools and adaptive platforms, but most remain in early adoption; full production deployment is growing but still below majority penetration, particularly in under-resourced districts. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a slow-adopting, resource-constrained sector with limited AI tool penetration into individualized instructional planning, despite growing ed-tech pilots. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically accelerates and improves a teacher's ability to generate multiple curriculum variants, identify student-specific accommodations, and integrate assistive tech recommendations—strongly augmenting human expertise while keeping the teacher in charge of final approval and implementation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up drafting of adapted materials, alternate assessments, and differentiated resources, letting teachers focus on individualization and compliance. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | AI can fully automate curriculum modification by analyzing student disability profiles, generating adapted lesson plans, recommending assistive technologies, and producing differentiated materials at scale—well exceeding 50% time savings while maintaining quality comparable to specialized teachers. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft modified materials but the task requires ongoing judgment about individual student IEP goals, disability-specific accommodations, and legal compliance that current systems cannot fully own end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While IEP laws require documented individualization, the law does not mandate human creation of materials—only that a qualified educator review and sign off; oversight is required but not prohibitive to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | IEP-driven modifications are legally mandated and typically require certified special education teacher involvement and documentation, creating strong regulatory and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI curriculum modification costs pennies per student per task after setup, while a special education teacher's loaded hourly wage (with benefits and overhead) is $40–60+; automation delivers orders of magnitude savings. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI drafting tools are cheap per output, but the human curation, compliance review, and individualization required keep total cost comparable to or only modestly less than teacher labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products (Learning Management Systems with AI-driven adaptation, curriculum generation tools, accessibility checkers) perform aspects of this task reliably in schools; however, no single integrated system handles the full end-to-end workflow with consistent production reliability across diverse disability types. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some ed-tech tools offer differentiated content generation and accessibility adaptations, but no deployed product reliably performs full curriculum modification aligned to individualized special education needs at scale. |
Maintain accurate and complete student records, and prepare reports on children and activities, as required by laws, district policies, and administrative regulations.
52CI 37–67 · exposure 58 · augmentation 75 · importance 4.2/5 · click for rater detail
Maintain accurate and complete student records, and prepare reports on children and activities, as required by laws, district policies, and administrative regulations.
52| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Public K–12 education shows moderate digital adoption and pilot AI use, but adoption is slower and more fragmented than finance or tech sectors. Many districts still rely on manual or semi-manual processes; widespread production deployment of AI record-keeping remains partial. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a slow-adopting, resource-constrained sector where AI use for compliance documentation remains in early pilot stages rather than widespread deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists teachers by auto-populating templates, drafting narrative sections from assessment data, and organizing information for compliance reports. Teachers retain judgment on interpretation and sign-off, while AI dramatically reduces manual data entry and formatting burden. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI writing assistants and data-summarization tools can meaningfully speed up drafting of progress notes and reports, letting teachers focus on verification and compliance rather than initial composition. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can substantially automate record entry, data aggregation, and routine report generation from structured student data, school systems, and assessment results. While some narrative judgment calls and legal compliance verification may require human review, the core documentation and report-writing work meets the 50% time-saving threshold with current systems. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft IEP progress reports, summarize data, and populate templates from notes, but compliance-sensitive recordkeeping still needs human verification and legally accurate entry, limiting full end-to-end automation.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | FERPA and state education laws require accurate, auditable records and may mandate human sign-off on official reports; liability for misclassification or missing documentation creates compliance friction. However, no law requires a teacher personally type records—delegation to AI-assisted systems is legally permissible if properly overseen. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education records are governed by IDEA, FERPA, and district policy requiring certified staff to verify and sign off on accuracy, creating strong legal and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Once configured, AI-driven record management and report generation cost pennies per task versus tens of dollars in loaded teacher labor. The cost delta strongly favors automation, especially at scale across a school or district. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting tools reduce time spent writing narrative sections, but human review, data entry from multiple sources, and compliance checks keep overall costs only moderately lower than fully manual work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (learning management systems with AI-assisted reporting, document automation tools, and form-filling engines) already perform routine record-keeping and report generation in school districts. Mature systems exist, though integration with legacy district systems and compliance verification can create friction. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some ed-tech platforms offer AI-assisted report drafting and data summarization, but no mature product reliably manages full compliant student records and legal reporting autonomously in production. |
Prepare, administer, and grade tests and assignments to evaluate students' progress.
43CI 39–48 · exposure 50 · augmentation 75 · importance 3.9/5 · click for rater detail
Prepare, administer, and grade tests and assignments to evaluate students' progress.
43| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Special education departments are slower adopters of automation than general education, owing to regulatory complexity, small team sizes, and the need for highly individualized assessment. Pilots of AI grading exist, but production deployment remains limited and cautious in this sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a slower-adopting sector with limited AI tooling penetration in daily classroom evaluation workflows compared to fields like finance or general software development. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist by auto-scoring objective items, suggesting rubric-based feedback, generating differentiated assignments, and flagging progress trends, allowing teachers to focus human effort on interpreting results and adjusting individualized instruction. This assistive role—preserving teacher judgment while automating drudgery—is well-suited to special education contexts. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully help teachers draft tests, generate differentiated materials, and pre-grade objective items, freeing time for individualized assessment and feedback while the teacher retains oversight. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automatically grade objective tests (multiple choice, short answer with rubrics) and generate assignments with good speed gains, but assessing progress in special education requires understanding individual IEPs, accommodations, and nuanced judgment about student growth that AI cannot reliably do end-to-end. Roughly half the workflow (grading objective items, generating draft assessments) is automatable; the interpretive half (evaluating progress against individualized goals) remains human-dependent. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can generate and grade many assignments, especially objective or short-answer formats, but grading for IEP-specific goals and nuanced student progress on individualized accommodations still requires human judgment., so only partial time savings at equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Special education assessments are legally mandated under IDEA and must align with documented IEPs; teachers are required by law to personally evaluate progress and make instructional decisions based on that assessment. Liability for assessment errors is high, and automated grading without human review can expose schools to compliance risk, creating a strong legal barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | IEP-related evaluations often require certified special education teacher sign-off and documentation for compliance, creating moderate regulatory and liability friction, though not an absolute licensing requirement for the mechanics of grading itself. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for grading and assignment generation reduce time on routine tasks, but special education teachers must still review and interpret results, and licensing (LMS, specialized assessment software) plus human oversight can offset savings. The cost per reliably graded special-education assessment is not yet substantially below the teacher's hourly rate for the full workflow. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools for test creation and objective grading are cheap, but the need for teacher review of accommodations and progress interpretation keeps overall cost comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist for automated grading and test generation (e.g., learning management systems with AI-assisted grading, platforms like Gradescope), but they work reliably only on well-structured, objective content. Special education contexts demand alignment with IEPs and adaptive assessment, where current systems have narrow scope and material error rates. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Gradescope, AI-based quiz generators, and adaptive assessment tools exist and are used in schools, but reliability for special-education-specific evaluation (IEP goal tracking, accommodations) is narrower and less proven in production. |
Prepare objectives and outlines for courses of study, following curriculum guidelines or requirements of states and schools.
38CI 25–51 · exposure 38 · augmentation 63 · importance 4.0/5 · click for rater detail
Prepare objectives and outlines for courses of study, following curriculum guidelines or requirements of states and schools.
38| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Special education and K–12 curriculum design remain relatively low in AI adoption compared to higher-education or corporate sectors. Schools move slowly on pedagogical changes, pilot programs are common, and adoption of AI-generated curricula in production remains rare and cautious. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education, especially special education, is a sector with slower AI adoption due to compliance concerns, limited budgets, and lower digitization of curriculum planning workflows compared to corporate sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist teachers by generating initial outline drafts, suggesting objectives aligned with standards, and organizing templates, helping them structure and accelerate planning. However, the human teacher must validate compliance, customize for student populations, and ensure pedagogical appropriateness, limiting transformative potential. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools substantially speed up drafting of course objectives and outlines, letting teachers focus on tailoring to individual student IEPs and state requirements, a clear productivity boost while keeping humans in the loop. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate draft course outlines and objectives quickly, the task requires deep understanding of student needs, state standards, and school-specific context that demands substantial human review and customization. Current AI cannot reliably produce compliant, pedagogically sound, and individualized IEPs/curricula without extensive human oversight, falling short of 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft curriculum outlines and objectives aligned to standards quickly, but adapting to specific IEP requirements, state guidelines, and individual student needs still requires substantial teacher review and customization.time savings are meaningful but not full end-to-end automation without oversight. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | State education departments and school districts impose strict curriculum standards, legal compliance requirements for special education (IDEA, 504), and accountability mandates. Teachers must sign off on curricula, and deviations from approved frameworks carry legal and liability risk, creating significant regulatory barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no law requires a human to draft the outline itself, special education teachers are legally responsible for IEP compliance and curriculum alignment, creating oversight and liability-driven friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for curriculum design are available but require integration, professional expertise to evaluate outputs, and human curriculum specialists to validate compliance. The loaded cost of a special education teacher plus AI tooling is still higher than the AI alternative alone, given limited autonomous capability. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating draft outlines and objectives via AI is very cheap compared to teacher time spent from scratch, though some human review time is still needed, moderating the savings slightly. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some educational platforms offer curriculum-building tools and template generators, but these typically require heavy human direction and lack reliable compliance with special education regulations. No deployed product independently generates state-compliant special education course objectives at scale without material human revision. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like lesson-planning AI tools and generative assistants are used by teachers today to draft objectives and outlines, but they require significant editing for compliance with IEPs and state-specific special education requirements. |
Confer with other staff members to plan and schedule lessons promoting learning, following approved curricula.
37CI 30–44 · exposure 38 · augmentation 75 · importance 4.0/5 · click for rater detail
Confer with other staff members to plan and schedule lessons promoting learning, following approved curricula.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Public K-12 education adopts technology slowly; special education programs are particularly conservative due to compliance and liability. Current adoption is mostly pilots and early experiments rather than production deployment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a low-digitization, human-intensive sector where AI adoption for collaborative planning remains largely experimental rather than embedded in practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at augmenting this task: generating lesson-plan drafts, identifying scheduling conflicts, suggesting differentiated materials, and cross-referencing curricula standards—all while teachers make final decisions. This assistive role is already demonstrable and valuable. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can help draft lesson plans, summarize curricula, suggest schedules, and generate materials aligned to IEP goals, meaningfully speeding up the human planning and conferring process. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can assist substantially with lesson planning, scheduling, and curricula alignment through document analysis and calendar optimization, but human judgment about student needs, teacher availability, and pedagogical priorities remains essential. The task likely reaches ~50% time savings with current systems handling drafting and logistical coordination. |
| Task automatability | claude-sonnet-5 | 2/5 | Coordinating and scheduling with colleagues involves interpersonal negotiation, contextual judgment about student IEP needs, and real-time collaborative decision-making that current AI cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Teachers retain legal responsibility for curricula and individualized education plans (IEPs); AI cannot sign off on them unilaterally. Organizational friction (multiple stakeholders, union considerations) and institutional preference for human deliberation create material adoption friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | IEP compliance, legal requirements for qualified staff involvement in special education planning, and required human interaction with colleagues and specialists create moderate structural barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference for planning assistance is cheap, but integration with school systems, oversight by teachers, and quality review add significant cost. The total falls short of an order-of-magnitude advantage over staff meeting time. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human coordination remains necessary for nuanced student-specific accommodations, so AI tools only supplement rather than replace the labor cost of this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products like ChatGPT and specialized educational planning tools can draft lesson plans and suggest schedules, but deployed systems show material limitations in handling special education nuances, cross-staff dependencies, and real constraints. Pilot adoption is common; production reliability remains mixed. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI scheduling/planning assistants exist but are not deployed to autonomously confer with staff or make collaborative curriculum-pacing decisions in special education settings today. |
Establish clear objectives for all lessons, units, and projects, and communicate those objectives to students.
34CI 30–39 · exposure 34 · augmentation 75 · importance 4.3/5 · click for rater detail
Establish clear objectives for all lessons, units, and projects, and communicate those objectives to students.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Special education operates in highly regulated, compliance-heavy environments with strong institutional resistance to full automation of IEP-related tasks. Adoption of AI for objective-setting remains limited; most sectors are still in pilot or cautious adoption phases, particularly for tasks with legal implications. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a lower-digitization, human-intensive sector where AI planning tools are used experimentally but not yet deeply embedded in daily practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist teachers by generating objective drafts, organizing standards-aligned learning outcomes, and suggesting language for clarity, enabling teachers to focus on customization and compliance review. This augmentation meaningfully raises educator productivity without replacing human decision-making on student-specific objectives. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI is genuinely useful for brainstorming, drafting, and aligning objectives to standards or IEP goals, meaningfully speeding up a teacher's planning process while they retain final control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can draft lesson objectives and learning outcomes rapidly, but establishing truly clear, developmentally appropriate, and individualized objectives for students with diverse special needs requires human judgment about specific student abilities, IEPs, and pedagogical context. The task involves significant customization that goes beyond template generation. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can draft lesson objectives aligned to standards, but tailoring them for individual IEPs, developmental levels, and classroom context still requires substantial teacher judgment and adaptation.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Special education law (IDEA) mandates that IEPs must contain specific, measurable goals tailored to each student, and only qualified educators can legally establish and sign off on these objectives. There is both a legal requirement for licensed educator involvement and high error cost if objectives fail to meet compliance standards. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically for writing objectives, but IEP compliance, legal accountability for special education plans, and required teacher sign-off create meaningful oversight friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for objective generation are inexpensive, but the required human review, customization for IEPs, and verification against compliance standards mean the all-in cost remains comparable to or potentially higher than having a teacher perform the task directly with modest AI assistance. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting tools are cheap per use, but the human time needed to review, adapt to IEP goals, and communicate objectives to students keeps overall cost savings moderate rather than dramatic. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products like learning management systems and curriculum design tools can generate and organize learning objectives, but they operate narrowly and require substantial educator review and customization. No mature system reliably establishes objectives that meet special education compliance and individual student accommodation requirements without human oversight. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like lesson-planning assistants and standards-aligned generators exist and are used by teachers, but reliable individualized adaptation for special education students remains limited in production tools. |
Prepare for assigned classes, and show written evidence of preparation upon request of immediate supervisors.
34CI 25–43 · exposure 30 · augmentation 75 · importance 3.9/5 · click for rater detail
Prepare for assigned classes, and show written evidence of preparation upon request of immediate supervisors.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Special education is a highly regulated, human-contact-intensive sector with mixed digitization. Adoption of AI for lesson planning exists in pockets but remains tentative due to liability concerns, IEP compliance scrutiny, and workforce reliance on experienced judgment rather than tools. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education, especially special education, has been slower and more cautious in AI adoption compared to sectors like finance or tech, with pilots more common than full integration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI drafting tools substantially assist teachers by generating initial lesson structures, accommodation strategies, and documentation scaffolds, allowing them to focus on individualization and refinement rather than blank-page creation. This raises productivity while keeping the teacher in full control of compliance decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully speed up drafting lesson plans, generating differentiated materials, and organizing documentation, significantly aiding teacher productivity while the teacher remains responsible for final content. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can draft lesson plans and generate written materials, but special education requires individualized planning tailored to each student's IEP, accommodations, and learning profile—decisions that demand human judgment and legal compliance review. AI could accelerate documentation but cannot autonomously ensure the individualized, compliant preparation this role demands. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft lesson plans and materials, but tailoring preparation to specific students' IEPs, classroom dynamics, and compliance documentation requires human judgment and cannot be fully automated end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Special education preparation is legally mandated (IDEA, IEP compliance) and supervisors must verify it meets regulatory standards. Schools have governance structures and accountability requirements around IEP-aligned instruction, creating significant barriers to full automation without human sign-off. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier for lesson planning itself, but special education requires certified teacher accountability and compliance documentation tied to legal IEP requirements, creating moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-generated drafts cost pennies per preparation versus the loaded wage (~$50–70/hour) of a teacher investing 5–10 hours weekly in planning. Even accounting for human review time, AI-assisted preparation is substantially cheaper than unassisted human labor. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools are cheap per use, but the teacher must still review, adapt for individual IEP goals, and produce verifiable evidence, so net cost savings are modest given oversight needs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI tools (GPT-4, Claude) can generate lesson outlines, materials, and planning documents today, and some schools use them for drafting. However, these outputs require substantial human review and customization to meet IEP requirements and district standards, limiting reliable end-to-end performance. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Lesson-planning AI tools exist and are used by teachers, but no deployed product reliably produces compliant, individualized special-education lesson prep with the documentation rigor supervisors require. |
Develop or write Individualized Education Programs (IEPs) for students.
30CI 23–37 · exposure 33 · augmentation 63 · importance 4.5/5 · click for rater detail
Develop or write Individualized Education Programs (IEPs) for students.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Special education departments operate in heavily regulated, traditionally low-tech environments with strong reliance on certified staff and face-to-face collaboration. Adoption of AI automation in this space is minimal; most schools use basic templates and word processors, not AI agents. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a slow-adopting, heavily regulated public-sector environment with limited AI tool penetration and cautious rollout due to compliance concerns. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist teachers by drafting sections, organizing assessment results, suggesting evidence-based accommodations, and automating formatting—moderately raising productivity during the composition phase. However, the human teacher must always validate content, ensure legal compliance, and drive the collaborative process. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up drafting of goals, objectives, and progress notes, letting teachers focus on individualization and compliance review, offering strong augmentation value. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft template text and organize standard accommodations, IEP development requires synthesizing assessment data, understanding individual student needs, legal compliance, and collaborative judgment with parents and specialists. Current AI systems lack the contextual depth and accountability to produce legally adequate IEPs autonomously, falling well short of the 50% time-saving threshold for end-to-end automation. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft goals, accommodations, and language based on assessment data, but requires human review of student-specific context, legal compliance, and judgment, so it can save significant drafting time without fully automating the task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | IEPs are legally mandated documents under IDEA (Individuals with Disabilities Education Act) that often require signatures from certified special education teachers and must meet strict regulatory standards. Parental input and multi-disciplinary team consensus are also required, creating hard organizational and legal barriers to full substitution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | IEPs are legally mandated documents requiring input and sign-off from certified special education professionals and multidisciplinary teams, creating strong regulatory and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tools (if any are genuinely deployed for this task) still require significant human review, legal oversight, and integration time. The cost of errors—non-compliant IEPs trigger liability and re-work—is high, making all-in AI cost comparable to or exceeding human authorship when oversight is factored in. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting tools are cheap per document, but the need for teacher review, data gathering, and legal accuracy checks keeps overall cost roughly comparable to current practice rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably generates compliant, legally defensible IEPs in production. Some tools assist with form-filling and template suggestions, but educators still perform the substantive work of diagnosis, goal-setting, and legal review. Products exist only in narrow, experimental, or advisory-only roles. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some ed-tech products offer IEP-writing assistance and templated goal banks, but no mature deployed product independently produces compliant, individualized IEPs at scale without heavy teacher input and verification. |
Prepare materials and classrooms for class activities.
28CI 25–30 · exposure 25 · augmentation 63 · importance 4.4/5 · click for rater detail
Prepare materials and classrooms for class activities.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Schools remain low-digitization environments with limited adoption of automation for classroom operations. Special education settings, in particular, emphasize human judgment and adaptive, individualized preparation that organizations have not prioritized automating. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a moderately slow-adopting sector for physical task automation, though AI use for lesson/material drafting is growing among teachers generally. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by suggesting material checklists, adaptive strategies for specific disabilities, or organizing digital lesson files, helping teachers work more efficiently. However, the physical and judgment-intensive nature of the work limits the transformative upside. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully help generate differentiated worksheets, visual aids, and activity plans tailored to individual student needs, saving significant prep time even though physical setup remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could help generate lesson material lists or organize digital content, the physical preparation of classrooms—arranging furniture, setting up learning stations, organizing adaptive equipment—requires hands-on work that current AI cannot perform. AI might assist in planning but cannot execute the bulk of classroom setup. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical setup of classrooms and preparation of tactile/adapted materials for students with disabilities requires hands-on manipulation and customization AI cannot perform end-to-end, though content generation for materials can be assisted. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teachers are legally and professionally required to ensure classroom safety, accessibility, and proper adaptation for students with disabilities. This hands-on responsibility creates a strong barrier to full automation, and teachers' accountability for student welfare limits substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically for room/material prep, but special education contexts often require individualized accommodations tied to IEPs that necessitate teacher judgment and accountability. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of integrating AI-assisted planning plus human labor to execute physical setup likely exceeds the cost of the teacher doing it directly, given the modest time savings and the need for human oversight and customization. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | The physical labor component still requires a paid human, while AI-assisted content creation is cheap but only covers part of the task, keeping overall cost savings modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI system can autonomously prepare a physical classroom or organize materials at the reliability needed for daily use. Document-generation tools exist but fall short of managing the diverse, adaptive, and context-specific preparation needs of special education classrooms. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product physically arranges classrooms or assembles adapted physical materials; AI tools exist mainly for generating printable worksheets or lesson content, a narrow subset of this task. |
Use computers, audio-visual aids, and other equipment and materials to supplement presentations.
28CI 25–30 · exposure 25 · augmentation 75 · importance 4.0/5 · click for rater detail
Use computers, audio-visual aids, and other equipment and materials to supplement presentations.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational institutions, especially those serving special populations, tend to adopt classroom technology more slowly than corporate sectors. Teachers remain highly skeptical of ceding instructional decisions to automation, and budget constraints in education limit early adoption of AI-powered AV selection tools. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a sector with historically slow AI adoption due to funding constraints, specialized needs, and cautious rollout of edtech tools. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI tools for identifying accessible media formats, auto-captioning, generating visuals from lesson plans, or curating content by learning level could substantially assist teachers in preparing richer presentations without replacing their decision-making role. Such augmentation aligns well with the supplementary nature of the task. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully help teachers prepare multimedia content, generate accessible materials, and personalize visual aids, enhancing lesson quality and preparation efficiency. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Using computers and AV equipment to supplement presentations has some automatable elements (e.g., selecting or generating appropriate media), but selecting, sequencing, and tailoring materials to specific student needs and learning objectives requires ongoing human judgment that current AI cannot reliably replicate. The task is primarily about augmenting human instruction rather than being performed end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help create or select materials, but the physical act of operating equipment and integrating aids into live classroom instruction with special-needs students requires human presence and adaptive judgment, limiting full automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teachers are required by law (IDEA) to provide individualized education and maintain instructional oversight; using purely automated AV supplementation without teacher selection and evaluation could violate legal obligations. Additionally, institutional policies, parental expectations, and safeguarding requirements around student-facing technology create significant adoption friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier specifically for using AV equipment, but special education requires certified teachers present with students, creating organizational and legal constraints on full delegation to AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI services for media curation, accessibility adaptation, and educational content selection add non-trivial costs (API calls, platform subscriptions, integration overhead) that approach or exceed the cost of a teacher spending time manually selecting existing resources, particularly for small-scale classroom use. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI-generated content creation can be cheap, but the task still requires a teacher present to operate equipment and adapt to student needs, so overall cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can generate or retrieve media assets and some educational software exists, no mature deployed product reliably handles the full task of selecting and integrating appropriate supplementary materials for individualized special education contexts. Most existing tools are narrowly scoped or require significant human oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Tools exist for generating slides, videos, and adaptive materials, but no deployed product autonomously operates classroom AV equipment or manages real-time supplementation for special education students. |
Develop and implement strategies to meet the needs of students with a variety of handicapping conditions.
25CI 25–25 · exposure 25 · augmentation 63 · importance 4.5/5 · click for rater detail
Develop and implement strategies to meet the needs of students with a variety of handicapping conditions.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Special education is under-digitized relative to other sectors, with adoption concentrated in larger districts. Pilot adoption of AI-assisted planning tools is emerging but far from mainstream production use; most teachers rely on established workflows and institutional practices. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a low-digitization, human-intensive sector with slow AI adoption due to compliance concerns, student safety, and lack of mature classroom-deployed agents. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by surfacing evidence-based strategies, drafting accommodation suggestions, and organizing student data, helping teachers work more efficiently. However, the augmentation is partial and tool-like; human judgment on implementation remains essential and irreplaceable. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully help teachers brainstorm differentiated strategies, generate accommodation ideas, and draft materials tailored to specific conditions, boosting planning efficiency significantly. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help generate draft strategies, lesson plans, and accommodation suggestions, the core task—understanding individual student needs, behavioral dynamics, and implementing real-time adjustments in a classroom—requires sustained human judgment and presence. AI lacks the relational and observational capacity to develop truly personalized strategies without extensive human oversight and iteration. |
| Task automatability | claude-sonnet-5 | 2/5 | Developing individualized strategies for diverse disabilities requires clinical judgment, ongoing observation, and relationship-building that current AI cannot execute end-to-end; AI can draft suggestions but not implement or adapt them in the classroom.dostupn |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: teachers must hold special education certification and licensure; legal responsibility for IEPs and disability accommodations is non-delegable; educators have professional judgment and liability obligations; and direct student contact and relationship-building are core to effectiveness and legally mandated. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education is heavily regulated (IDEA, IEP compliance, licensure requirements), and legal responsibility for implementing accommodations must rest with a qualified, often licensed, educator. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted tools reduce planning time modestly, but the teacher's salary dominates the total cost, and current AI tools still require significant oversight and customization by the teacher, limiting cost advantage to marginal reductions per task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI drafting assistance is cheap, the actual implementation and adaptive teaching still requires a paid, credentialed teacher, so overall cost savings are limited to the planning portion only. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Products exist for generating IEP suggestions, accommodation frameworks, and evidence-based strategy templates, but they operate with narrow scope and require substantial human filtering. No deployed system reliably develops and implements (rather than merely suggests) strategies across the heterogeneous conditions and individual contexts special educators encounter daily. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools (e.g., IEP drafting assistants) exist to suggest accommodations, but no deployed product reliably designs and executes individualized behavioral/instructional strategies across handicapping conditions in real classrooms. |
Administer standardized ability and achievement tests, and interpret results to determine students' strengths and needs.
25CI 25–25 · exposure 25 · augmentation 50 · importance 3.9/5 · click for rater detail
Administer standardized ability and achievement tests, and interpret results to determine students' strengths and needs.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Schools remain traditionally organized with teacher-led assessment; while some digital scoring tools exist, adoption of autonomous AI interpretation in special education assessment is slow due to regulatory constraints, professional licensing requirements, and educator skepticism about algorithm validity for high-stakes IEP decisions. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a historically slow-adopting, human-intensive sector with limited AI deployment for high-stakes assessment tasks, despite growing edtech pilots. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by automating scoring, flagging patterns in test data, and generating initial summary reports, which frees teachers to focus on deeper interpretation and student interviews; however, the core interpretive work still requires the teacher's clinical expertise and professional judgment. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help score tests, generate draft reports, and flag patterns in data to support the teacher's interpretation, but the core judgment and administration remain human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can score standardized tests and generate basic interpretation reports, the task requires nuanced understanding of individual student contexts, learning disabilities, and professional judgment to connect test results to educational planning—elements that current AI cannot reliably perform end-to-end at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Test administration requires physical presence, behavior management, and accommodation adjustments for students with disabilities, while interpretation demands clinical judgment tied to IEP context that current AI cannot fully replicate end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Special education assessment is heavily regulated under IDEA and state education codes, requiring licensed special education teachers or certified specialists to administer tests and interpret results; liability and legal requirements to ensure culturally responsive, valid assessment create strong adoption barriers. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Standardized testing for special education often requires certified/licensed professionals under IDEA and state regulations, with legal accountability for interpretation feeding into IEPs, creating strong regulatory and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI can reduce some administrative scoring costs, but special education assessment requires licensed professionals who must oversee interpretation and integrate findings into IEPs; the human expert remains necessary and costly, making the all-in cost savings minimal. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human specialists remain necessary for administration and clinical interpretation; AI tools only reduce some scoring/reporting time, so overall cost savings versus a special education teacher's loaded wage are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Scoring and basic data analysis of standardized tests are technically feasible with current products, but interpreting results to determine strengths and needs in special education contexts requires contextual clinical judgment that AI systems cannot yet deploy reliably in production educational settings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some scoring/reporting software exists for standardized tests, but no deployed product reliably administers tests to special-needs students or autonomously interprets results for individualized educational planning. |
Instruct through lectures, discussions, and demonstrations in one or more subjects, such as English, mathematics, or social studies.
21CI 18–25 · exposure 25 · augmentation 63 · importance 4.2/5 · click for rater detail
Instruct through lectures, discussions, and demonstrations in one or more subjects, such as English, mathematics, or social studies.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational sectors have been slow to adopt AI for core instruction despite years of edtech investment; most adoption remains in supplementary roles (tutoring, content generation). Schools face budget constraints, regulatory inertia, and cultural resistance to replacing live instruction, particularly in special education where one-on-one and small-group interaction is central. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a highly regulated, in-person, low-digitization sector with minimal production AI adoption for actual instruction delivery. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist special education teachers by generating lesson plans, creating adapted materials for diverse learning needs, and providing content suggestions, meaningfully raising their productivity in preparation. However, the real-time instructional delivery itself—where student engagement and differentiation happen—remains primarily human-driven. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist teachers by generating differentiated lesson plans, adaptive materials, and practice exercises tailored to individual IEP goals, improving instructional prep productivity. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate lecture content and demonstrations, synchronous instruction requires real-time classroom management, adaptive response to student confusion, and relationship-building that current AI systems cannot reliably replicate. The task also involves monitoring and modulating instruction for diverse learning needs in special education, which demands human judgment and presence. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can generate lecture content and demonstrations, but live in-person instruction, real-time adaptation to special-needs students, and classroom management require human presence and judgment that current AI cannot replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong regulatory and institutional barriers exist: teachers must hold state licensure and certification, special education instruction is legally mandated to be delivered by qualified professionals, and liability for student outcomes rests on the licensed educator. Parents and school systems strongly prefer human instructors for direct student interaction. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Special education teaching requires state licensure, IEP compliance, legal accountability for student outcomes, and mandated human interaction with vulnerable populations, making substitution essentially barred by law and policy. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Generating lecture content and basic demonstrations is inexpensive, but integrating these into a functioning classroom, managing platform costs, and ensuring oversight makes the all-in cost approach human wages for continuous instruction. The savings on content creation alone do not offset the need for human classroom presence. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply produce supplementary materials, but the core task still requires a paid, credentialed human teacher present in the classroom, so overall cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably delivers full classroom instruction independently; educational AI tools exist for content generation and tutoring but require substantial human oversight and cannot fully replace the instructor role. Existing systems lack the embodied presence and adaptive responsiveness required for classroom instruction at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tutoring tools and content generators exist and are used to support lesson planning, but no deployed product independently delivers live classroom instruction to special education students reliably at scale. |
Plan and conduct activities for a balanced program of instruction, demonstration, and work time that provides students with opportunities to observe, question, and investigate.
21CI 18–25 · exposure 25 · augmentation 63 · importance 4.1/5 · click for rater detail
Plan and conduct activities for a balanced program of instruction, demonstration, and work time that provides students with opportunities to observe, question, and investigate.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | K–12 education, especially special education, is a low-digitization, human-contact-intensive sector with strong regulatory and cultural resistance to automation. Adoption of AI tools is nascent and limited to narrow planning aids, not displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a relatively low-digitization, human-intensive sector where AI adoption for instructional delivery remains in early pilot stages. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist teachers by generating differentiated activity ideas, drafting lesson plans, or suggesting adaptations for specific disabilities—raising planning productivity. However, the core of conducting and adapting activities in real time remains primarily human-driven. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully help teachers generate differentiated activities, materials, and assessments tailored to IEP goals, improving planning efficiency while the teacher still delivers instruction. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Planning activities requires understanding individual student needs, disabilities, and learning styles—nuanced judgment that AI struggles with reliably. While AI can help generate activity ideas or draft lesson outlines, conducting activities and responding to student questions in real time demands adaptive human expertise that current systems cannot replicate end-to-end at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft lesson plans and activity ideas, but conducting a live, adaptive balance of instruction, demonstration, and hands-on work with special-needs middle schoolers requires in-person judgment, behavior management, and real-time adaptation that current AI cannot execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Special education teaching is legally protected: individualized education programs (IEPs) require licensed special education teachers to plan and deliver instruction, and parental consent and oversight are mandatory. Liability for student safety and outcomes is substantial, creating hard barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education teaching requires certification, IEP compliance, legal accountability for student progress, and direct human interaction with children, creating strong regulatory and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted planning tools are inexpensive, but the task includes conducting activities and real-time adaptation—the core of a teacher's role. A special education teacher's salary is high, and AI cannot yet replace that labor, making the cost ratio unfavorable for automation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI planning tools are cheap for drafting materials, but the execution portion still requires a paid, trained special education teacher present, keeping overall cost comparable to human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI can assist with drafting lesson plans and generating activity suggestions, but no deployed system reliably handles the full task of planning *and* conducting a balanced program tailored to special education students' diverse needs. Teacher-facing tools exist but require heavy human oversight and customization. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Lesson-planning assistants and adaptive learning tools exist and are used by teachers, but no deployed product independently plans and conducts full classroom activity cycles for special education students reliably. |
Organize and label materials and display students' work.
19CI 14–24 · exposure 16 · augmentation 38 · importance 3.6/5 · click for rater detail
Organize and label materials and display students' work.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools operate in laggard sectors for automation, with limited digitization, physical plant constraints, and emphasis on human judgment; adoption of AI for classroom organization tasks is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education classrooms are low-digitization environments with minimal adoption of AI for physical classroom management tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by generating digital catalogs, suggesting organization schemes, or producing printable labels and display designs, helping teachers plan and visualize classroom layout more efficiently. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help generate label text or organize digital records of student work, but offers little assistance with the physical organizing and display itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical organization and labeling of materials requires spatial manipulation and judgment about student work display that current AI cannot perform end-to-end; AI could assist with digital cataloging or label design, but not the physical assembly and arrangement in a classroom environment. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical organizing and displaying of materials requires manual handling in a classroom, which AI cannot perform; only labeling/text-generation portions could be assisted digitally.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: special education teachers have legal responsibility for student work, individualized material accommodations, and classroom environment—tasks requiring direct human oversight and decision-making about student needs and dignity. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but the physical, in-person nature of arranging a classroom environment creates practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task involves physical manipulation in a specific classroom environment; AI would require expensive robotic systems and integration to match the cost-effectiveness of a teacher or aide spending time on this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI has no viable role in physically arranging classroom materials, so there is no cost-saving substitution available; human labor remains necessary. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs the physical organization, spatial arrangement, and aesthetic judgment required for organizing classroom materials and displaying student work in a classroom setting. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product manages physical classroom organization or display of student work; this remains a hands-on task performed by teachers or aides. |
Confer with parents, administrators, testing specialists, social workers, and professionals to develop individual educational plans (IEPs) for students' educational, physical, and social development.
18CI 11–25 · exposure 17 · augmentation 63 · importance 4.3/5 · click for rater detail
Confer with parents, administrators, testing specialists, social workers, and professionals to develop individual educational plans (IEPs) for students' educational, physical, and social development.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Special education remains a heavily regulated, human-intensive field with strong legal and cultural emphasis on parent involvement and professional accountability. Adoption of AI for core IEP work remains minimal; most pilots focus on data summarization, not process replacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a highly regulated, relationship-driven, moderately digitized sector where AI adoption for compliance documents is emerging slowly and pilots for meeting support are still uncommon. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by pre-populating IEP templates with assessment data, suggesting evidence-based interventions, or organizing prior records, thereby reducing administrative burden. However, the human educator retains full decision-making authority, limiting the transformation potential. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully help teachers draft IEP goals, summarize assessment data, and prepare talking points ahead of meetings, improving efficiency while professionals remain the ones conferring and deciding. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time negotiation, relationship-building, and synthesis of diverse stakeholder perspectives into legal documents tailored to individual students. Current AI cannot meaningfully replace the interpersonal judgment, contextual understanding, and consensus-building that constitute the core of IEP conferencing. |
| Task automatability | claude-sonnet-5 | 2/5 | This task centers on multi-party human conferencing, relationship-building, and negotiated professional judgment about a child's needs, which AI cannot conduct end-to-end; AI can assist with drafting and data synthesis but not replace the actual conferring and consensus-building process. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | IEPs are legally mandated documents that must be developed through federal-requirement conferences with parent participation; federal law (IDEA) requires qualified professionals to develop and sign IEPs. No AI system can legally substitute for the human-led, parent-inclusive conferencing process. |
| Adoption barriers | claude-sonnet-5 | 4/5 | IEPs are legally mandated documents under IDEA requiring signatures and participation from qualified, often licensed professionals (special education teachers, related service providers), creating strong regulatory and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI drafting assistance might reduce clerical time modestly, but the human specialist conducting the conference remains essential and expensive. The cost of integrating AI tools, oversight, and potential legal liability likely approaches or exceeds savings on documentation alone. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply generate draft language or summarize data, but the core deliverable—live collaborative meetings and judgment calls—still requires paid professional time, so overall cost savings are modest, not order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can draft IEP templates or summarize assessment data, no deployed product reliably conducts multi-stakeholder conferences or produces legally defensible, individualized IEPs independently. Existing tools require heavy human oversight and cannot replace the conferencing process itself. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some IEP-drafting and documentation tools exist (e.g., AI-assisted goal writing, progress summarization), but no deployed product conducts the actual multi-stakeholder meetings or replaces the collaborative decision-making required. |
Supervise, evaluate, and plan assignments for teacher assistants and volunteers.
13CI 5–21 · exposure 13 · augmentation 50 · importance 3.7/5 · click for rater detail
Supervise, evaluate, and plan assignments for teacher assistants and volunteers.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Special education in K–12 has lagged digitization and AI adoption overall; schools remain conservative about automating personnel decisions, and volunteer/paraprofessional management remains highly localized and human-centric. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially special education administrative/supervisory functions, shows minimal AI adoption for personnel management tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could provide useful assistance by drafting evaluation templates, summarizing observation notes, and suggesting assignment matches based on volunteer skills—meaningfully aiding a teacher's workflow while the teacher retains decision authority. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help draft assignment schedules, generate evaluation templates, or organize documentation, offering moderate assistance while the teacher retains all supervisory judgment and interaction. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could help draft evaluation frameworks and assignment suggestions, the task fundamentally requires human judgment about personnel performance, individualized coaching, and delegation decisions that depend on understanding interpersonal dynamics and student needs—areas where AI cannot reliably perform end-to-end at 50% time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | Supervising and evaluating people, planning their assignments, and giving feedback requires interpersonal judgment, relationship management, and accountability that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teachers hold professional responsibility and potential legal liability for staff evaluations and assignment decisions; school districts typically require a human educator (not an AI system) to formally evaluate employees and volunteers, creating a strong legal and regulatory barrier. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Personnel supervision and performance evaluation typically require accountable, often certified staff (e.g., lead teacher) with legal/administrative responsibility, creating strong organizational and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI infrastructure, prompting, oversight, and potential errors in managing staff performance would likely exceed the incremental human time saved, especially given the need for a teacher to review and validate AI-generated evaluations. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the human supervisory role, so cost comparison favors the human by default; AI cannot produce the same output. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably evaluates or supervises staff performance in real special education settings; while AI tools exist for task planning and documentation, none demonstrably handle the relational and contextual judgment this task requires in production school environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product manages or evaluates human staff/volunteers autonomously in a classroom setting; this remains entirely a human management function. |
Plan and supervise class projects, field trips, visits by guest speakers, or other experiential activities, and guide students in learning from those activities.
13CI 9–18 · exposure 20 · augmentation 50 · importance 3.4/5 · click for rater detail
Plan and supervise class projects, field trips, visits by guest speakers, or other experiential activities, and guide students in learning from those activities.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task is performed in K–12 schools, a sector with low AI adoption for core instructional and supervisory functions. Adoption remains limited to planning tools; actual activity supervision and guidance is not being automated. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a low-digitization, highly regulated, in-person sector with minimal AI deployment for supervisory or experiential-activity tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating activity ideas, drafting itineraries, and organizing logistics, moderately easing planning burden. However, assistance on the supervisory and learning-guidance components is limited without direct real-time presence and judgment. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can meaningfully help teachers plan projects, draft trip itineraries, generate discussion prompts, and design follow-up learning activities, improving efficiency in the planning phase. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with planning logistics (scheduling, itineraries, speaker coordination) but cannot meaningfully supervise activities, manage student behavior, or guide learning in real time. The core supervisory and pedagogical elements require human judgment and presence. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help brainstorm project ideas, logistics checklists, and speaker outreach templates, but supervising students physically, managing IEP-specific accommodations, and guiding real-time learning during trips requires human presence and judgment that cannot be automated. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal and regulatory requirements mandate that licensed special education teachers directly supervise students, particularly during field trips and outside-classroom activities. Liability and duty of care create hard barriers to substitution. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Special education law (IDEA) and school district policy require certified teachers to supervise students and ensure IEP-mandated accommodations during activities, making human responsibility and liability essentially non-negotiable. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI's assistance with planning (e.g., draft itineraries) cannot eliminate the need for teacher salary and supervision. The cost of oversight and human presence during activities far outweighs any savings in planning automation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI planning assistance is cheap, but the core supervisory and instructional-guidance labor still requires paid staff, so overall cost savings are minimal relative to the full task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI tools can draft plans and schedules, but no deployed product reliably supervises groups, manages special education student needs during activities, or adapts guidance in response to real-time student learning. Supervision and adaptation remain human-dependent. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product supervises field trips or in-person experiential learning for special education students; this remains entirely human-executed with only planning support tools available. |
Employ special educational strategies and techniques during instruction to improve the development of sensory- and perceptual-motor skills, language, cognition, and memory.
13CI 0–25 · exposure 13 · augmentation 50 · importance 4.2/5 · click for rater detail
Employ special educational strategies and techniques during instruction to improve the development of sensory- and perceptual-motor skills, language, cognition, and memory.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Special education remains predominantly human-delivered due to regulatory requirements, low sector digitization, and the heterogeneous nature of disabilities; adoption of AI tools for autonomous instruction is negligible in production today, with pilot interest limited to narrow supplementary tasks. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a highly regulated, low-digitization, human-contact-intensive sector with minimal AI deployment for direct instructional delivery. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by suggesting strategy variations, analyzing video of student performance to flag patterns, and helping plan lesson sequences; however, the assistive value is moderate because teachers rely heavily on tacit, embodied judgment and real-time sensory observation that AI cannot yet meaningfully augment in situ. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers plan individualized strategies, generate practice materials, or suggest techniques for specific disabilities, but cannot replace the hands-on instructional delivery itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate instructional content and suggest evidence-based strategies, the task fundamentally requires real-time observation, adaptive responsiveness to individual sensory and motor feedback, and iterative adjustment during live instruction—capabilities current systems cannot reliably execute in unstructured classroom environments with diverse special needs. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires live, physical, adaptive interaction with students with disabilities—observing motor movements, providing hands-on prompting, and adjusting in real time—none of which AI can perform end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Special education is heavily regulated (IDEA, IEPs), requires licensed special education teachers for legal accountability, and involves vulnerable populations where error costs (injury, developmental harm) are high; parental trust and individualized legal obligations create strong friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Special education law (IDEA, IEP requirements) mandates qualified, licensed special education teachers to deliver individualized instruction and document progress, creating hard legal and professional barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Deployment of AI assistants for special education instruction would require substantial integration, customization per student profile, human oversight, and validation—likely making per-student costs comparable to or exceeding the cost of a paraprofessional or special educator's time. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI has no viable substitute delivery mechanism for physical instructional strategies, so cost comparison favors the human teacher entirely. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs dynamic, in-the-moment instructional adaptation for special education populations. AI can support planning and suggest techniques, but production systems today cannot replace the teacher's real-time clinical judgment in modifying perceptual-motor or sensory activities for individual students. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product delivers in-person sensory-motor or perceptual-motor instruction to students; this remains firmly a human physical/interpersonal task. |
Observe and evaluate students' performance, behavior, social development, and physical health.
13CI 0–25 · exposure 13 · augmentation 38 · importance 4.1/5 · click for rater detail
Observe and evaluate students' performance, behavior, social development, and physical health.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI for student observation remains experimental and pilot-stage in most districts; concerns around privacy, legal compliance, and trust in AI judgment over teacher expertise slow meaningful deployment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a low-digitization, high-touch sector with minimal AI adoption for direct student observation and evaluation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered tools can assist by flagging behavioral patterns in video, summarizing attendance/engagement data, and prompting observation of specific metrics, moderately enhancing a teacher's ability to document and organize observations while the teacher retains judgment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI tools can help organize behavioral data logs or flag patterns from teacher-entered notes, but they contribute minimally to the core observational and evaluative judgment involved. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can analyze limited video/behavior data and flag patterns, the task requires nuanced, in-person observation of complex behavioral, social, and physical health indicators that demand human contextual judgment and real-time adaptation. Current AI systems cannot reliably replace this holistic, dynamic assessment. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires sustained in-person observation, relational judgment, and clinical understanding of individual students that current AI cannot perform end-to-end.RM AI cannot physically observe classroom behavior or build the contextual rapport needed for valid evaluation.rating |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Federal law (IDEA) and state special education regulations mandate that teachers conduct these observations and that decisions be based on documented professional judgment. Privacy regulations (FERPA) and liability concerns around health/behavioral findings create significant legal and organizational friction. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Special education law (IDEA, IEP requirements) mandates certified professional judgment and documentation by qualified educators, creating strong legal and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of video analysis, AI oversight, and validation by human educators often costs as much or more than direct teacher observation, especially given the need for human review of alerts and contextualization of findings. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since no AI system can perform this task, there is no viable cost comparison; human teacher labor remains the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some AI tools exist for classroom video analysis and behavioral flagging, but they operate at low accuracy for nuanced social-emotional and developmental assessment in real classroom settings. No mature product reliably performs the full scope of observation across behavior, social development, and physical health. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product observes and evaluates special-needs students' behavior, social development, and physical health in real classrooms; this remains far outside current AI product scope. |
Establish and enforce rules for behavior and policies and procedures to maintain order among students.
9CI 0–18 · exposure 8 · augmentation 38 · importance 4.5/5 · click for rater detail
Establish and enforce rules for behavior and policies and procedures to maintain order among students.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of AI for autonomous rule enforcement in K–12 education is minimal; schools remain highly conservative on delegating student management to machines, and regulatory/liability concerns keep this area largely human-led despite digitization elsewhere. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a low-digitization, highly regulated, in-person sector with minimal AI deployment for behavior management and classroom order. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by analyzing attendance, flagging repeated infractions, suggesting interventions, or helping teachers document incidents—supporting the teacher's rule-enforcement decision-making without removing human authority. This is useful but modest compared to tasks with deeper AI leverage. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help draft behavior management plans or suggest strategies based on IEP goals, but it offers little real-time assistance for enforcing rules and maintaining order in the classroom. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI cannot meaningfully establish and enforce behavioral rules autonomously; this requires real-time presence, judgment about context and individual student needs, and authority that only humans possess. Limited automation could support flagging policy violations from logs or video, but the core enforcement task is irreducibly human. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time, in-person authority, relationship-building, and behavioral judgment with students, especially those with special needs, which current AI cannot execute or enforce directly. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: teachers hold legal and professional authority to establish and enforce classroom rules, schools have liability for delegating discipline to non-human systems, and most jurisdictions require a licensed educator to manage student behavior. Parents and regulators expect human judgment in discipline. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Special education law, IEP compliance, in-person supervisory duties, and legal/safety responsibilities require a certified human teacher to establish and enforce behavioral rules. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI systems that monitor behavior or assist with documentation are relatively cheap, but they cannot replace the human teacher's wage for this task; the human must still enforce rules, make judgment calls, and maintain order, so the cost ratio remains unfavorable for full substitution. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task, so cost comparison favors the human by default since AI cannot deliver the output at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs autonomous classroom behavior management and rule enforcement in production today. Some surveillance or logging tools exist, but they do not enforce rules or maintain order—they merely record. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product manages classroom discipline or enforces behavioral policies with special education students; this remains entirely a human function. |
Coordinate placement of students with special needs into mainstream classes.
8CI 0–16 · exposure 5 · augmentation 50 · importance 4.3/5 · click for rater detail
Coordinate placement of students with special needs into mainstream classes.
8| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Special education administration remains largely in legacy systems with limited digitization. Schools are early in adopting AI tools; adoption of AI for high-stakes placement decisions is minimal outside pilot programs, reflecting organizational conservatism and regulatory caution. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education administration is a slow-adopting, highly regulated, relationship-driven sector with minimal AI deployment in placement decisions. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by organizing student data, matching class schedules, and flagging compatibility issues, helping coordinators work more efficiently. However, the core decision-making responsibility remains with the educator, limiting augmentation scope. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help organize student records, summarize IEP data, and flag scheduling conflicts, aiding coordination even though it cannot make placement decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Coordinating student placement requires nuanced understanding of individual learning profiles, mainstream class dynamics, teacher capacity, and legal requirements. Current AI systems cannot reliably make discretionary decisions about student integration that balance pedagogical, social, and legal factors end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires interpersonal coordination among teachers, parents, administrators, and IEP teams, plus contextual judgment about student and classroom fit that current AI cannot execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Federal law (IDEA) and state special education regulations typically require licensed educators and often documented collaboration with classroom teachers and parents. Legal accountability for placement decisions rests with qualified personnel, creating a hard barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Placement decisions are governed by IDEA and require IEP team consensus, including licensed educators, parents, and often legal documentation, making this a hard-barrier task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI could help with document processing and scheduling suggestions, but the core coordination task—evaluating fit, consulting with mainstream teachers, and making placement decisions—still requires a human coordinator. The savings from automation would be modest relative to the loaded teacher wage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the human decision-making and relationship management involved, so there is no viable cost comparison for full task replacement. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in data aggregation and flagging relevant information, no deployed product reliably performs the full coordination task independently. This requires human judgment on complex trade-offs and stakeholder negotiation that current systems cannot do reliably at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product manages the multi-stakeholder negotiation, scheduling, and legal compliance involved in special education placement decisions. |
Provide additional instruction in vocational areas.
7CI 0–14 · exposure 8 · augmentation 38 · importance 3.7/5 · click for rater detail
Provide additional instruction in vocational areas.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Special education is heavily regulated, relies on credentialed professionals, and serves vulnerable populations; adoption of AI-driven vocational instruction remains negligible, with no evidence of production deployment in schools. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a low-digitization, human-intensive sector with minimal AI deployment for direct instructional delivery, especially in physical/vocational skill-building contexts. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with some preparatory materials (e.g., generating instructional videos or safety checklists), but the core task of live vocational skill coaching and adaptive support for special learners requires continuous human presence and judgment. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers plan vocational lesson content, generate individualized materials, or suggest adaptive strategies, providing moderate assistance while the teacher remains essential for delivery and hands-on support. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Providing vocational instruction to special education students requires hands-on demonstration, individualized pacing adjustments, real-time behavior management, and emotional support that current AI systems cannot deliver end-to-end. This task is inherently relational and embodied, falling well outside automation feasibility. |
| Task automatability | claude-sonnet-5 | 2/5 | Vocational instruction for special education middle schoolers requires hands-on demonstration, physical guidance, and adaptive behavior management that current AI cannot replicate end-to-end.So only limited portions (e.g., generating supplementary materials) could be offloaded to AI. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Federal law (IDEA) mandates individualized, in-person special education services with certified teachers; liability for student safety in vocational training is substantial; and parent/guardian expectations and trust require human contact and accountability. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education instruction is governed by IEP legal requirements, certification mandates for special education teachers, and requires direct human supervision and relationship-building with students with disabilities. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying AI systems to simulate vocational hands-on training (robotics, custom content, oversight infrastructure) would far exceed the loaded wage of a special education teacher providing this instruction. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since AI cannot perform the core hands-on instructional task, there is no viable cost comparison for full task substitution; any AI use is supplementary to, not a replacement for, the teacher's labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system reliably performs vocational instruction for special education students. This requires live skill coaching, safety oversight, adaptive teaching techniques, and interpersonal rapport that no current product addresses in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously deliver vocational instruction to students with disabilities; existing AI tools are limited to content generation or scheduling support, not classroom instruction delivery. |
Collaborate with other teachers that provide instruction to special education students to ensure that the students receive appropriate support.
6CI 5–7 · exposure 0 · augmentation 38 · importance 4.2/5 · click for rater detail
Collaborate with other teachers that provide instruction to special education students to ensure that the students receive appropriate support.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational institutions, particularly special education departments, move slowly on AI adoption, rely on established practices, and prioritize human relationships and compliance. Meaningful displacement in this domain remains negligible. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education, especially special education, is a sector with slow, cautious AI adoption focused on administrative support rather than collaborative decision-making tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with organizing IEP information, flagging resource conflicts, or consolidating student progress data, but these are marginal aids to the core collaborative problem-solving. The task is fundamentally about human judgment and interpersonal alignment that AI cannot substantially enhance. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can help by drafting shared notes, summarizing student progress data, or organizing communication logs to support teacher collaboration, though the core collaborative act remains human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task is fundamentally interpersonal and requires real-time coordination, judgment about individual student needs, and adaptive decision-making among humans. AI cannot meaningfully substitute for the collaborative discussion, negotiation, and consensus-building required across multiple educators. |
| Task automatability | claude-sonnet-5 | 1/5 | This task is fundamentally interpersonal coordination and relationship-based collaboration among educators, which requires human judgment, trust-building, and situational awareness that AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal and regulatory barriers are substantial: special education is heavily regulated (IDEA, IEPs), requires licensed educators to develop and execute individualized plans, and mandates parent involvement. Schools face liability for educational decisions, and human educator sign-off is typically legally required. |
| Adoption barriers | claude-sonnet-5 | 4/5 | IEP compliance, legal accountability for special education services, and required professional collaboration among certified staff create strong organizational and regulatory barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems capable of supporting this task (if they existed at scale) would require significant infrastructure, human oversight, and integration costs that would exceed the cost of the collaborative time already built into teacher workflows. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this collaborative task, so cost comparison favors the human entirely; any AI cost would be additive, not substitutive. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs end-to-end educational collaboration and cross-teacher coordination. While AI can assist with scheduling or document sharing, it cannot autonomously ensure appropriate student support decisions or replace the human deliberation required. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product independently conducts teacher-to-teacher collaboration on student support plans; this remains a human-led interpersonal process. |
Instruct and monitor students in the use and care of equipment and materials to prevent injuries and damage.
4CI 0–9 · exposure 8 · augmentation 25 · importance 4.1/5 · click for rater detail
Instruct and monitor students in the use and care of equipment and materials to prevent injuries and damage.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Special education contexts involve vulnerable populations with heightened legal and duty-of-care requirements; adoption of autonomous monitoring remains minimal and faces strong institutional and regulatory resistance. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a low-digitization, high-human-contact sector with minimal AI deployment for physical safety supervision tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by generating pre-use checklists or post-incident analysis, but the task's core—live supervision and immediate safety response—offers limited room for meaningful augmentation while a human remains the accountable observer. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help generate instructional materials or safety checklists in advance, but offers little real-time assistance during the actual monitoring and hands-on instruction itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI systems could generate safety instructions and monitoring scripts, the task inherently requires real-time physical supervision, direct observation of student behavior, and immediate intervention to prevent injuries—capabilities current AI cannot perform in embodied, live classroom settings. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires live, physical supervision of students with equipment in a classroom, including hands-on demonstration and real-time safety intervention, which AI cannot perform end-to-end today.mise |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal duty of care, liability for student injuries, mandatory in-person oversight requirements, and strong institutional/parental expectations that a licensed educator must be physically present and responsible create hard regulatory and liability barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Special education teachers are licensed professionals with legal duty-of-care obligations for student safety and IEP compliance, and physical supervision cannot be legally or practically delegated to AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | An AI system capable of live physical monitoring and intervention in a classroom would require extensive infrastructure (cameras, sensors, robotic systems) far exceeding the cost of a teacher's direct supervision. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the physical presence and supervisory liability role, so any comparison would require a human anyway, making AI an add-on cost rather than a replacement. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs real-time monitoring and intervention of student safety with equipment in physical classrooms; this requires embodied presence and adaptive response to unpredictable student actions that current AI systems cannot deliver at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product provides physical classroom supervision or in-person instruction on equipment safety for special education students; this remains entirely research-stage or nonexistent for physical presence tasks. |
Confer with parents or guardians, other teachers, counselors, and administrators to resolve students' behavioral and academic problems.
4CI 0–7 · exposure 0 · augmentation 38 · importance 4.3/5 · click for rater detail
Confer with parents or guardians, other teachers, counselors, and administrators to resolve students' behavioral and academic problems.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | School districts are heavily regulated, union-protected environments with strong norms for human-led family engagement. Adoption of AI for core conference facilitation remains virtually nonexistent in practice. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education, especially special education administrative/relational work, adopts AI slowly relative to sectors like finance or tech; use is mostly limited to drafting or scheduling support. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with pre-meeting preparation (summarizing prior communications, drafting agendas) or post-meeting documentation, but provides limited augmentation during the actual conferencing task, which demands real-time relationship management and trust-building. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers prepare talking points, summarize academic/behavioral data, draft follow-up communications, or translate for non-English-speaking parents, meaningfully aiding preparation even though the core conference remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time interpersonal negotiation, emotional intelligence, and contextual judgment about sensitive behavioral and academic issues. Current AI systems cannot conduct autonomous conferences with multiple stakeholders or resolve conflicts requiring human authority and accountability. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a live, multi-party interpersonal negotiation requiring trust-building, emotional sensitivity, and real-time judgment about a specific child; no current AI can conduct or substitute for these conferences end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Special education law (IDEA, Section 504) requires documented conferences with parents/guardians and qualified educators; regulatory frameworks mandate human professionals sign and take responsibility for accommodations and behavior plans. Liability and legal accountability create hard barriers. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education involves legal obligations (IEP compliance, FERPA, due process), requiring a credentialed teacher's direct engagement and accountability, creating strong institutional and legal barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Even minimal AI assistance in pre-conferencing or note-taking would require human oversight and validation; the core conferencing work remains firmly human, making AI cost higher relative to the teacher performing it directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this function, so cost comparison favors the human by default; any AI role is supportive infrastructure, not replacement. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can independently facilitate multi-party meetings to resolve student behavioral and academic problems. While chatbots can provide information, they cannot serve as a substitute for the legal authority and trust required in these conferences. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products conduct parent-teacher-administrator conferences autonomously; this remains squarely a human relational task with no production analog. |
Guide and counsel students with adjustments, academic problems, or special academic interests.
4CI 0–7 · exposure 5 · augmentation 50 · importance 4.3/5 · click for rater detail
Guide and counsel students with adjustments, academic problems, or special academic interests.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 special education remains highly human-intensive and has been slow to adopt AI agents for core counseling and guidance roles; most AI uptake is limited to administrative tasks or supplementary tutoring, not replacement of the counselor role itself. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a highly regulated, relationship-driven, low-digitization sector with minimal AI deployment for direct student counseling tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist teachers by drafting progress notes, organizing student data, suggesting evidence-based interventions, or generating template guidance materials, moderately improving teacher productivity without removing the human from the counseling relationship. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers track student progress, flag academic issues, and suggest personalized resources, but the core counseling interaction remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Guiding and counseling students requires adaptive human judgment, emotional intelligence, and individualized responses to personal and academic challenges that current AI systems cannot reliably perform end-to-end. The task involves building trust, understanding nuanced student needs, and making real-time adjustments based on complex interpersonal dynamics that AI cannot replicate at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | Guiding and counseling students with individualized emotional, behavioral, and academic adjustment needs requires deep relational trust, real-time judgment, and legal accountability that current AI cannot replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Special education is heavily regulated; guidance and counseling typically require a licensed educator and often a certified special education teacher by law. Legal and ethical liability, duty-of-care obligations, and mandatory human involvement in decisions affecting students' educational plans create hard barriers to substitution. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Special education law (e.g., IEP requirements, IDEA) mandates qualified, credentialed personnel for student support and counseling, creating strong legal and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI tools for tutoring or information delivery remain expensive relative to their value when oversight and integration costs are included, and a qualified special education teacher's loaded wage is competitive or cheaper for the quality of personalized guidance required. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Because AI cannot substitute for the core counseling relationship, there is no viable cost comparison—human specialized teachers remain necessary regardless of AI cost efficiencies elsewhere. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI chatbots can provide general academic information or draft guidance frameworks, no deployed product reliably performs actual counseling and personalized academic guidance at the quality expected in special education contexts. Existing systems lack the contextual understanding and relational competence needed for this sensitive task in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs student counseling and adjustment guidance autonomously in special education settings; existing tools are limited to narrow tutoring or content support, not holistic counseling. |
Teach students personal development skills, such as goal setting, independence, and self-advocacy.
4CI 0–7 · exposure 0 · augmentation 50 · importance 4.3/5 · click for rater detail
Teach students personal development skills, such as goal setting, independence, and self-advocacy.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Special education teaching remains a highly human-intensive field with strong professional licensure, regulatory oversight, and institutional reluctance to automate instruction in social-emotional domains. Adoption of AI for replacement is minimal and slow. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a slower-adopting sector for AI, with usage concentrated in administrative or content-support tools rather than core skill instruction, especially for social-emotional development. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by generating sample goal-setting templates, organizing student progress data, or suggesting discussion prompts, but the human teacher must interpret each student's needs and deliver the teaching interactively. Useful on task components, not transformative. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help generate personalized goal-tracking materials, social stories, or self-advocacy scripts that teachers then use and adapt during instruction, providing moderate productivity support. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Teaching personal development skills requires real-time adaptive response to individual students' emotional and social needs, sustained relationship-building, and modeling behavior—capabilities far beyond current AI. No end-to-end automation path exists that achieves 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | Teaching personal development skills to middle schoolers with disabilities requires relational trust, real-time behavioral judgment, and adaptive modeling of independence and self-advocacy that current AI cannot deliver end-to-end in a classroom setting. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Special education teachers are licensed professionals, and individualized instruction in self-advocacy and personal development is typically a legally mandated IEP requirement. Liability and duty-of-care norms create hard barriers to substitution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | IEP compliance, certification requirements for special education teachers, and legal accountability for student progress on individualized goals create strong barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems (content generation, basic chatbots) cost little but cannot substitute for the human teaching work; any meaningful performance requires a qualified teacher to deliver and adapt the instruction. The human remains irreplaceable for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since AI cannot perform this task independently, the comparison defaults to human cost being necessary regardless of any AI tool costs layered on top. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | While AI can provide generic scaffolding or example content about goal-setting, no deployed product reliably teaches personal development to middle-school special education students. The task fundamentally requires human judgment, empathy, and ongoing relationship to be effective. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously teaches self-advocacy or independence skills to special-needs students; existing tools are limited to supplementary content generation, not delivery of this interpersonal instruction. |
Meet with parents and guardians to discuss their children's progress and to determine priorities for their children and their resource needs.
4CI 0–7 · exposure 0 · augmentation 38 · importance 4.1/5 · click for rater detail
Meet with parents and guardians to discuss their children's progress and to determine priorities for their children and their resource needs.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools have shown no meaningful adoption of AI to replace parent-teacher conferences; these meetings remain a core, legally-mandated human function in special education practice. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a relatively low-digitization, human-contact-heavy sector with slow AI adoption for direct parent engagement tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with preparing summaries of a student's progress data or documenting meeting notes after the fact, but provides limited value during the actual conference where human judgment, responsiveness, and relationship matter most. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help prepare progress summaries, draft talking points, or organize resource recommendations ahead of the meeting, aiding preparation though not the meeting itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires genuine human relationship-building, nuanced communication about sensitive topics, and collaborative problem-solving with parents. AI cannot meaningfully conduct parent-teacher conferences or establish the trust necessary for these conversations. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires live, relational, emotionally sensitive conversation with parents about a specific child's individualized needs, which AI cannot conduct end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Educational law and district policy typically require a certificated teacher to conduct parent conferences and make decisions about special education services. Parents expect and are legally entitled to speak with a qualified human educator. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education involves legal IEP processes, parental consent, and professional accountability requiring a certified teacher's direct involvement, creating strong regulatory and trust barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task involves irreplaceable human presence and relationship-building; AI has no meaningful cost advantage since a licensed educator must attend and lead the meeting regardless of any AI involvement. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this meeting, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably conducts parent-teacher meetings or replaces the human judgment and empathy required to discuss a child's educational needs and family priorities. This remains beyond current AI capabilities in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously conducts parent-teacher conferences or determines IEP priorities; this remains a human-led interpersonal task. |
Meet with parents and guardians to provide guidance in using community resources and to teach skills for dealing with students' impairments.
4CI 0–7 · exposure 0 · augmentation 50 · importance 3.8/5 · click for rater detail
Meet with parents and guardians to provide guidance in using community resources and to teach skills for dealing with students' impairments.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Special education is a heavily regulated, relationship-intensive sector with strong institutional and legal constraints. Schools have shown slow AI adoption in sensitive student-facing roles, and parent engagement is a core accountability metric that districts will not algorithmically delegate. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a slow-adopting, relationship-driven sector with limited AI deployment in direct parent counseling contexts. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by summarizing community resources, drafting talking points on disability accommodations, or preparing resource lists beforehand. However, the core interpersonal work—listening, responding to parent concerns, and teaching adaptive strategies—remains fundamentally human-driven and only moderately benefited by assistive tools. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers prepare resource lists, draft talking points, translate materials, or summarize strategies before/after meetings, aiding but not replacing the interaction. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires genuine human interaction, relationship-building, and individualized guidance tailored to each family's circumstances and emotional state. AI cannot authentically conduct parent-teacher conferences or establish the trust needed to guide families through sensitive matters involving their children's disabilities. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires live, empathetic, individualized interpersonal counseling with parents about their child's specific impairments and community resources, which AI cannot conduct end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and professional barriers exist: teachers must be licensed educators, parent-teacher conferences are often mandated by special education law (IEP meetings), and schools face direct liability for guidance quality. The human-contact and trust requirements are fundamentally embedded in federal special education regulations. |
| Adoption barriers | claude-sonnet-5 | 4/5 | IEP-related parent communication is often legally mandated and tied to a certified special education teacher's role, and trust/liability concerns strongly favor human involvement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Even if partial automation were conceivable, the cost of AI oversight, integration, and potential liability for guidance failures on disability-related parenting would likely exceed the loaded wage of a special education teacher conducting these meetings directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task, so any AI cost comparison is moot; the human teacher remains the only functional option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs parent-teacher guidance meetings for special education independently. While AI can draft communication templates or provide information, the core task—meeting with parents to provide personalized guidance and teach coping skills—requires human presence and responsiveness that current systems cannot replicate in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product independently runs parent meetings to teach disability-management skills and connect families to community resources; this remains a human relational task. |
Instruct students in daily living skills required for independent maintenance and self-sufficiency, such as hygiene, safety, and food preparation.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail
Instruct students in daily living skills required for independent maintenance and self-sufficiency, such as hygiene, safety, and food preparation.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Special education instruction is a highly regulated, relationship-intensive, and human-contact-mandatory service in public and private schools with very limited AI adoption and no incentive to replace human instructors in daily living skills training. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a low-digitization, high human-contact sector with minimal AI deployment for hands-on skill instruction. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by providing reference materials or video demonstrations of hygiene or food-prep steps, but the core work—demonstrating, correcting form, reinforcing behavior, ensuring safety—requires human presence and cannot be meaningfully augmented by existing AI tools. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help generate lesson plans, visual schedules, or social stories to support instruction, but offers little assistance for the hands-on teaching itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time physical demonstration, hands-on guidance, behavioral reinforcement, and adaptive responsiveness to individual student needs and safety concerns that current AI cannot reliably deliver. AI cannot safely supervise food preparation, hygiene practices, or safety-critical activities with students who may have cognitive or physical disabilities. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires hands-on demonstration, physical supervision, and real-time behavioral coaching with students who have disabilities, which current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Hard legal and duty-of-care barriers exist: special education teachers must maintain direct in-person supervision of vulnerable students, school liability and child safety requirements mandate human oversight, and many jurisdictions legally require qualified educators to deliver special education instruction. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education requires certified teachers, IEP compliance, and direct human supervision for safety-critical activities like food preparation, creating strong regulatory and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems cannot perform the core safety-critical and physically-supervisory aspects of this task, so cost comparison is inapplicable; a human instructor remains mandatory, making AI cheaper-than-human impossible when substitution is infeasible. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI has no viable standalone offering for this task, so it cannot be cheaper than a human teacher who must physically supervise and adapt instruction. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably instructs students in practical daily living skills requiring physical demonstration, supervision, and personalized behavioral guidance. This task is fundamentally hands-on and context-dependent in ways current systems cannot meet in a classroom or school setting. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product independently teaches daily living skills like hygiene or food preparation to special-needs students in a classroom setting. |
Teach socially acceptable behavior, employing techniques such as behavior modification and positive reinforcement.
3CI 0–5 · exposure 0 · augmentation 38 · importance 4.3/5 · click for rater detail
Teach socially acceptable behavior, employing techniques such as behavior modification and positive reinforcement.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Special education remains a highly human-centered, regulated field with low digitization and minimal AI adoption in instructional roles. Schools move cautiously on automation in sensitive domains like student behavior management, and no measurable displacement of special education teachers by AI is evident. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a low-digitization, high human-contact sector with minimal AI deployment for direct behavioral instruction. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can offer limited assistance—such as suggesting behavior modification strategies or tracking reinforcement logs—but the core task of *teaching* social behavior through modeling, demonstration, and real-time relationship management remains primarily human-dependent with minimal augmentation potential. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers track behavior data, suggest reinforcement strategies, or draft behavior intervention plans, providing moderate assistance while the teacher remains the primary agent of instruction. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Teaching socially acceptable behavior requires real-time interpersonal judgment, emotional attunement, and adaptive responsiveness to individual student needs that current AI systems cannot reliably replicate. The task fundamentally depends on ongoing human relationship-building and contextual behavioral assessment that extends far beyond what automation can achieve today. |
| Task automatability | claude-sonnet-5 | 1/5 | Teaching socially acceptable behavior to middle schoolers with disabilities requires real-time relational presence, behavior observation, and adaptive intervention that current AI cannot execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Significant legal and professional barriers exist: special education is heavily regulated under IDEA, teachers must hold state licensure and certification, and behavioral instruction typically requires a credentialed educator to conduct assessments and implement individualized behavior plans. Liability for behavioral outcomes is substantial. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education involves IEP compliance, legal accountability, and requires certified educators to implement behavior plans, creating strong regulatory and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI systems capable of classroom deployment, integration, and liability oversight would exceed the loaded wage of a teacher for this task, particularly given the low error tolerance and need for human oversight in behavioral instruction. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task, so cost comparison favors the human by default; any AI use is only a minor supplement, not a replacement of labor cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably delivers end-to-end behavior modification and positive reinforcement instruction in classroom settings. While AI can draft behavioral protocols or suggest interventions, actual teaching of social behavior requires human presence, model-demonstration, and real-time behavioral feedback that remains research-stage. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously delivers behavior modification instruction to students; this remains squarely a human interpersonal function. |
Attend staff meetings and serve on committees, as required.
3CI 0–5 · exposure 0 · augmentation 38 · importance 3.5/5 · click for rater detail
Attend staff meetings and serve on committees, as required.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task involves mandatory human participation in school governance structures; no sector-wide AI adoption pattern exists because the task is inherently interpersonal and institutionally bound to human roles. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially special education administrative functions, shows slow AI adoption for governance and interpersonal collaboration tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by preparing summaries of prior meeting minutes or drafting agenda items for review, but such assistance is marginal; the core task of attending and participating remains entirely human-dependent. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with meeting summarization, agenda preparation, and note-taking, moderately boosting efficiency around the task without replacing attendance itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Attending staff meetings and serving on committees requires real-time presence, active participation in group dialogue, and interpersonal negotiation that AI cannot perform end-to-end today. No AI system can substitute for a human participant in a live meeting. |
| Task automatability | claude-sonnet-5 | 1/5 | Attending meetings and serving on committees inherently requires human presence, participation, and real-time judgment in group deliberation; AI cannot substitute for physical/social attendance and representation.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Staff meetings and committee service are mandatory institutional duties tied to employment contracts and organizational governance; a human employee is legally and organizationally required to attend and participate. A teacher cannot be replaced by AI in these governance roles. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Institutional and professional norms require the actual staff member's presence and voice in decision-making bodies, especially in special education where legal compliance (IEP-related committees) often mandates specific personnel. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI systems capable of real-time meeting participation would far exceed the time cost of a teacher's attendance, since this task cannot meaningfully be automated anyway. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since AI cannot perform the core task of attending and representing on committees, there is no viable cost comparison—human presence is mandatory. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs the task of attending and participating in meetings as a committee member. AI may summarize or transcribe meetings post-hoc, but it cannot genuinely participate, vote, or represent a role in real time. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product attends meetings or serves as a committee member on a teacher's behalf; AI note-taking tools exist but do not perform the task itself. |
Attend professional meetings, educational conferences, and teacher training workshops to maintain and improve professional competence.
3CI 0–5 · exposure 0 · augmentation 38 · importance 3.5/5 · click for rater detail
Attend professional meetings, educational conferences, and teacher training workshops to maintain and improve professional competence.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools operate in regulated environments with explicit professional development mandates. There is no trajectory toward AI substitution for teacher conference attendance because the requirement is statutorily tied to individual human professionals. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Education, especially K-12 special education, shows slow AI adoption generally, and this specific task of attending PD events has no substitution trend at all. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist marginally by summarizing conference materials or helping organize notes after attendance, but it cannot participate in the core activity of engaging with live instruction and professional networking that defines this task. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers find relevant conferences, summarize workshop content, generate notes, or suggest follow-up learning resources, moderately supporting the professional development process. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires human participation in professional development activities that involve networking, live instruction, and interactive engagement. AI cannot meaningfully attend meetings or conferences in place of a teacher or achieve the professional growth objectives. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically or virtually attending meetings, conferences, and workshops to build professional competence is an inherently human developmental and social activity that AI cannot perform on a teacher's behalf. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Significant legal, organizational, and professional licensing barriers exist: teachers are individually responsible for maintaining their own professional credentials and meeting state-mandated continuing education requirements. Professional development attendance is tied to individual accountability and licensure. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Many jurisdictions require licensed teachers to complete continuing education credits and specific training hours to maintain certification, making personal attendance a professional/regulatory requirement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Attempting to substitute AI for human attendance at professional development would not reduce costs; a teacher must still attend to meet professional requirements and maintain competence. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI equivalent product performing this task, so cost comparison is moot; the human must personally participate. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product exists that can autonomously attend professional meetings or conferences on behalf of a human. This task inherently requires human presence and active participation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product attends professional development events or substitutes for a teacher's participation in training; this remains entirely a human activity. |
Perform administrative duties, such as school library assistance, hall and cafeteria monitoring, and bus loading and unloading.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.3/5 · click for rater detail
Perform administrative duties, such as school library assistance, hall and cafeteria monitoring, and bus loading and unloading.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational institutions remain among the slowest to adopt automation, and these specific supervisory duties involve child safety—a domain where human accountability is mandated and resistance to substitution is strongest. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 school administrative/physical supervision functions show minimal AI adoption; this is a low-digitization, in-person task domain. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist minimally through automated duty schedules or incident logging, but cannot augment the core supervisory presence required. The task fundamentally depends on human accountability rather than augmentation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with scheduling or library cataloging support, but offers little to no meaningful help with live physical monitoring duties like hallway or bus supervision. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | These duties require physical presence, real-time situational awareness, and direct supervision of students in uncontrolled environments. Current AI cannot monitor hallways, load buses, or supervise cafeterias with the safety and accountability standards required. |
| Task automatability | claude-sonnet-5 | 1/5 | These are physical presence and supervision duties (monitoring hallways, cafeterias, bus loading) that require a human body on-site for safety and behavioral oversight; AI cannot perform them end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Schools have duty-of-care legal obligations and must have responsible adults physically present during student supervision. Negligence liability, state education codes, and child safety regulations strictly require human oversight for these duties. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Legal duty-of-care and child-safety supervision requirements mean a responsible, often certified staff member must be physically present; liability and safeguarding regulations create strong barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The infrastructure, legal liability, and continuous monitoring required would far exceed the cost of a teacher or aide performing these tasks. Human presence remains the cost-effective solution. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute delivering equivalent supervisory presence, so AI cost is effectively irrelevant/non-comparable and the human remains the only option, making AI not cheaper in any practical sense. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs end-to-end hall monitoring, cafeteria supervision, or bus loading/unloading. Video-based monitoring systems exist but cannot replace human judgment, intervention, and duty-of-care responsibilities in real school settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical student supervision or bus-loading monitoring; this remains entirely human-performed in schools. |
Monitor teachers and teacher assistants to ensure that they adhere to inclusive special education program requirements.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Monitor teachers and teacher assistants to ensure that they adhere to inclusive special education program requirements.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Education, particularly special education, remains low in AI adoption due to regulatory requirements, need for human expertise, and resistance to tech-driven compliance monitoring. Schools lack digitization infrastructure and funding for such systems, and adoption is virtually nonexistent in production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education administration is a low-digitization, highly regulated, human-contact-intensive sector with minimal AI adoption for supervisory compliance functions. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist by flagging video clips or generating compliance checklists, but the core task—monitoring and ensuring adherence through professional judgment—requires human expertise. Augmentation possibilities are limited by the judgment-heavy nature of the work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help track documentation, flag missing paperwork, or summarize compliance checklists, but it offers little assistance for the actual observational and judgment-based monitoring of staff practice. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time observation of classroom behavior, judgment about adherence to complex, context-dependent program requirements, and assessment of teaching quality—areas where current AI lacks reliable capability. Monitoring adherence to inclusive practices requires understanding nuanced interactions and program-specific regulations that exceed what automated systems can consistently evaluate. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires in-person observation, judgment about compliance with IEPs and legal requirements, and interpersonal supervision of staff—none of which current AI can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Special education oversight is heavily regulated (IDEA, Section 504) and typically requires licensed educators with formal authority and accountability. Schools have legal liability for compliance failures, and parents expect human judgment and accountability from qualified professionals—creating strong legal and institutional barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Special education compliance monitoring is tied to legal mandates (IDEA, IEPs) and requires a credentialed, authorized supervisor to evaluate and enforce staff adherence, creating hard regulatory and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Building and maintaining a capable AI system for this task—including video capture, analysis, and integration into school workflows—would be substantially more expensive than the salary cost of a special education teacher performing ongoing monitoring as part of their role. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this supervisory task, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs end-to-end classroom monitoring and compliance verification for special education programs. While video analysis tools exist in limited form, they cannot credibly assess adherence to individualized program requirements or substitute for a trained educator's judgment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product monitors classroom staff for adherence to inclusive special education requirements; this is an administrative/supervisory function requiring human presence and authority. |
Provide assistive devices, supportive technology, and assistance accessing facilities, such as restrooms.
0CI 0–0 · exposure 0 · augmentation 25 · importance 3.8/5 · click for rater detail
Provide assistive devices, supportive technology, and assistance accessing facilities, such as restrooms.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task is deeply embedded in legal requirements for special education staffing. Schools operate under high regulatory scrutiny and cannot substitute AI for the mandated human support students with disabilities require. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education support services are a low-digitization, physically embedded sector with minimal AI adoption for hands-on care tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist with scheduling facility access, tracking device inventory, or recommending assistive technology types, but the core task of physically providing assistance and accompanying students cannot be augmented by AI systems. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-enabled assistive technology (e.g., communication devices, adaptive software) can support the broader goal of accessibility, but the core physical assistance task itself sees little augmentation. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical presence, manual handling of devices, and real-time responsiveness to individual student needs. Current AI systems cannot physically provide devices, accompany students to facilities, or adapt support in real-time based on individual disabilities. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires direct physical assistance, hands-on device setup, and in-person support for mobility/accessibility needs, none of which current AI can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Schools have legal and duty-of-care obligations to provide direct assistance to students with disabilities; staff must be physically present to ensure student safety and accessibility. Regulatory compliance under the ADA and IDEA creates hard barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Special education law (IDEA/504 plans), safeguarding requirements, and the need for trained, credentialed staff to physically assist students create hard legal and safety barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI has no role in the direct delivery of this service; a human staff member must be present. The cost of any AI overlay would be purely additive to the necessary human labor cost. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the physical labor and supervision involved, so there is no meaningful AI cost comparison—human presence is mandatory. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can physically provide assistive devices or accompany students to access facilities. This task is fundamentally dependent on human presence and physical intervention. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical assistance or facility access support for students; this remains entirely a human/physical-caregiving function. |
Organize and supervise games and other recreational activities to promote physical, mental, and social development.
0CI 0–0 · exposure 0 · augmentation 25 · importance 3.7/5 · click for rater detail
Organize and supervise games and other recreational activities to promote physical, mental, and social development.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | There is no adoption of AI for this task in production settings because it is legally and practically infeasible. Schools and districts recognize that supervision of recreational activities for vulnerable students cannot be delegated to automated systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Special education and physical supervisory roles in schools are a low-digitization, high-physical-presence sector with minimal AI adoption for direct activity supervision. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might assist marginally with scheduling or documenting activities post-hoc, but offers minimal assistance during actual supervision. The task's core value—responsive human oversight and relationship-building—cannot be meaningfully augmented by current AI. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help plan activity curricula or suggest games suited to developmental goals, but it offers little assistance during the actual supervision and execution of the task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time supervision, dynamic adaptation to individual student needs, and direct physical presence. AI cannot meaningfully organize and supervise in-person recreational activities or provide the immediate behavioral management and interpersonal responsiveness that students with special needs require. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence, real-time supervision of students with special needs, and hands-on safety management during recreational activities—none of which AI can perform end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and duty-of-care barriers exist: schools have liability obligations to supervise students directly, especially those with special needs. A licensed educator must be physically present and accountable for student safety and welfare during recreational activities. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Special education students often require certified, trained personnel for safety, IEP compliance, and legal supervision requirements, creating hard regulatory and liability barriers to non-human substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI has no practical role in this task, making any cost comparison moot; a human supervisor is irreplaceable. The loaded wage of a special education teacher cannot be offset by AI tools for direct supervision of recreational activities. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical supervisory task, so the human cost is the only viable option, making AI comparatively far more expensive or simply inapplicable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously supervise recreational activities, manage group dynamics, or ensure student safety in physical play settings. This task fundamentally depends on human presence and judgment in an unpredictable, embodied environment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product organizes and physically supervises recreational activities for special education students; this is entirely outside current product capabilities. |
Visit schools to tutor students with sensory impairments and to consult with teachers regarding students' special needs.
0CI 0–0 · exposure 0 · augmentation 38 · importance 3.3/5 · click for rater detail
Visit schools to tutor students with sensory impairments and to consult with teachers regarding students' special needs.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Special education remains a human-intensive, regulated sector with strong organizational and legal expectations that qualified teachers deliver personalized services; automation pressure is minimal and adoption of AI for core tutoring/consultation roles is virtually absent. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a low-digitization, high-physical-presence sector with minimal AI agent deployment for direct student services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with administrative prep (scheduling, generating lesson outlines) or basic information retrieval, but it offers limited value during the core activities of visiting schools and consulting on individual student sensory needs, where human expertise dominates. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers prepare materials, draft IEP documentation, or suggest accommodation strategies, but the core in-person tutoring and consultation remains human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires in-person visitation, real-time interaction with students having sensory impairments, and nuanced consultation with teachers about individualized special needs—all fundamentally dependent on human presence, empathy, and adaptive interpersonal judgment that current AI cannot replicate at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physically visiting schools, in-person tutoring of students with sensory impairments, and interpersonal consultation with teachers—none of which current AI can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Special education services are heavily regulated (IDEA, IEPs), require licensed special education teachers in many jurisdictions, demand legal accountability for student progress, and necessitate in-person human judgment and relationship-building with vulnerable student populations. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Special education services for students with disabilities are governed by IDEA and require certified/licensed special education professionals, with legal accountability for IEP compliance and accommodations. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | An AI system capable of even a fraction of this task (e.g., remote tutoring support) would require significant infrastructure and human oversight, making it more expensive than direct human tutoring for specialized sensory-impairment populations. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the physical and relational components of this task, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously visit schools, conduct tutoring sessions with sensory-impaired students, or engage in collaborative consultation with educators; these require physical presence and human-to-human trust-building. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs in-person travel, hands-on tutoring for sensory-impaired students, or real-time collegial consultation in physical school settings. |
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.