Special Education Teachers, Secondary School
25-2058.00Median wage $74,260/yr163,930 employed (US)Rank #755 of 923 scored · top 82% by substitution
Teach academic, social, and life skills to secondary 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
Substitution16
Exposure16
Augmentation49
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%16
panel mean rating 1.6/5 → substitution pressure 16/100
Technical feasibility todayw 20%15
panel mean rating 1.6/5 → substitution pressure 15/100
Cost vs. human wagew 15%16
panel mean rating 1.6/5 → substitution pressure 16/100
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 1.6/5 → 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.
Establish clear objectives for all lessons, units, and projects, and communicate those objectives to students.
66CI 48–85 · exposure 70 · augmentation 88 · importance 4.4/5 · click for rater detail
Establish clear objectives for all lessons, units, and projects, and communicate those objectives to students.
66| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Schools and EdTech platforms are piloting AI for lesson planning and objective generation, but adoption remains uneven and often experimental. Most districts lack systematic, production-grade deployment of AI planning assistants; adoption is faster in tech-forward or wealthy districts but remains middling sector-wide. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education, especially special education, is a sector with slower AI adoption compared to information/finance industries, with pilots more common than full production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI strongly augments teacher productivity by rapidly drafting standards-aligned objectives, accommodating diverse learner needs, and generating multiple communication formats. Teachers maintain control over adaptation to their students' disabilities, IEPs, and contexts, making this a high-leverage assistive application. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully speed up drafting of lesson objectives and align them to standards, letting teachers focus more time on individualized adaptation and delivery to students. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | AI can generate lesson objectives, unit plans, and project frameworks that meet curriculum standards and learning theory benchmarks. Current LLMs can produce clear, specific, measurable objectives and communicate them effectively in multiple formats (syllabi, emails, presentations) with minimal human revision, easily achieving 50% time savings on this routine instructional planning task. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft lesson objectives aligned to standards and IEP goals quickly, but tailoring to individual special-needs students and communicating them effectively in class still requires human judgment and delivery. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal legal or licensing barriers exist; teachers retain pedagogical authority and judgment over alignment with IEPs and student needs. Light organizational friction occurs (adoption of new tools, teacher preference for autonomy), but nothing prevents schools from deploying AI-generated objective templates. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement blocks AI-assisted planning, but special education involves IEP compliance and legal documentation requirements that create moderate institutional friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI inference cost for generating objectives and communications is negligible (cents per lesson) compared to teacher labor (loaded wage of $30–50/hour for planning time). The cost advantage is orders of magnitude in favor of AI, even accounting for oversight and editing. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting tools are cheap per use, but the teacher's time reviewing, customizing for IEPs, and delivering objectives to students is not eliminated, keeping overall cost comparable to current practice. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products (Claude, ChatGPT, specialized EdTech platforms) reliably generate lesson objectives and communicate them to students at scale. Production use exists in schools, though teachers typically do light editing for context-specificity and grade-level appropriateness, so full end-to-end autonomy has minor friction. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like lesson-planning AI tools and LMS objective generators exist and are used by teachers, but adaptation for special education populations and in-classroom communication remains manually handled. |
Prepare, administer, and grade tests and assignments to evaluate students' progress.
45CI 43–48 · exposure 50 · augmentation 75 · importance 3.9/5 · click for rater detail
Prepare, administer, and grade tests and assignments to evaluate students' progress.
45| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Special education contexts are lower-adoption environments due to individualization requirements, smaller scale, and regulatory compliance burden; most schools still rely on teacher-created or district-standardized assessments rather than AI-driven testing pipelines. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a historically slow-adopting sector for AI, with pilots emerging but limited large-scale deployment in individualized instructional contexts. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist by auto-generating test drafts, providing rapid feedback analytics on student progress, and highlighting performance trends, allowing teachers to focus human effort on interpreting results and adjusting individualized instruction rather than routine grading. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI is already useful for generating differentiated test items, rubrics, and first-pass grading, significantly aiding teachers who still finalize grades and accommodations. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate significant portions of test creation, administration via digital platforms, and objective grading of multiple-choice or short-answer questions, but designing assessments tailored to individual IEPs and grading subjective work (essays, projects) with special education considerations requires human expertise, preventing full end-to-end automation at equal quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft assignments, generate quizzes, and grade objective or short-answer items well, but grading for special-education students often requires interpreting IEP-specific accommodations and nuanced judgment that current systems cannot fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Special education teachers must follow IEPs mandated by law, and assessment decisions carry legal and liability weight under IDEA; educators are required to document progress and make individualized judgments, creating substantial regulatory and professional barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | IEP compliance, grading accountability, and special education regulations create moderate oversight requirements, though no strict licensing law mandates a human perform grading itself. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI tools for test generation and grading are moderately affordable, but the infrastructure, integration, and human verification needed to maintain quality for special education contexts results in costs roughly comparable to traditional teacher labor for this task. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools reduce time on test creation and objective grading cheaply, but the need for human review of accommodations and subjective grading keeps blended costs only moderately lower than fully manual work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed products (learning management systems, automated grading tools) handle routine assessment components reliably in production, but reliable end-to-end performance on adaptive, IEP-aligned testing and qualitative feedback generation remains limited and typically requires manual oversight. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like AI-based quiz generators and automated essay/short-answer graders are deployed in schools, but reliability drops for individualized special-education criteria and non-standard responses. |
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 4.1/5 · 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 | Educational institutions, especially in special education contexts, adopt technology slowly due to regulatory requirements, professional norms, and the high cost of errors. While some schools pilot AI writing tools, actual production replacement of curriculum design remains rare and cautious. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education, especially special education, has been slower to adopt AI tools broadly compared to sectors like finance or tech, with pilots more common than full-scale integration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by drafting initial outlines, suggesting evidence-based objectives, and checking alignment with state standards, allowing teachers to focus on customization and IEP integration. However, the augmentation is moderate because curriculum planning for special education remains heavily dependent on teacher expertise and judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly speed up drafting of course objectives and outlines by generating templates and content aligned with guidelines, letting teachers focus on individualized adaptation and review. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can generate initial course outlines and objectives by processing curriculum guidelines and requirements, but special education contexts require extensive customization for individual student IEPs, disability accommodations, and state-specific compliance that demand human judgment and expertise. The task includes legally-binding special education considerations that AI cannot fully own end-to-end. |
| Task automatability | claude-sonnet-5 | 3/5 | LLMs can draft curriculum objectives and outlines aligned to standards quickly, but adapting them to specific students' IEPs and school requirements still needs teacher review and customization, limiting full automation.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Special education curriculum development is subject to IDEA, ADA, and state-specific legal requirements; teachers must have proper certification and accountability for individualized education plans. The requirement that a licensed special education teacher oversee and sign off on curriculum objectives creates a hard legal barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no license is legally required to draft curriculum outlines, special education plans often require alignment with legally mandated IEPs and state curriculum standards, creating moderate compliance and oversight requirements. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for curriculum drafting are inexpensive, but the human oversight required to ensure legal compliance, appropriate differentiation, and IEP alignment means total cost remains comparable to or exceeds hiring a teacher or curriculum specialist to do it correctly from the start. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating a draft outline via AI costs a fraction of a cent compared to the teacher-hours needed to build objectives from scratch, though human review time still adds cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While general-purpose curriculum generation tools exist and LLMs can draft outlines, no mature product reliably produces special education course objectives that meet state compliance requirements and individualized accommodation needs in production settings. Current systems require substantial human review and modification. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like lesson-planning AI tools (e.g., Curriculum-aligned generators, ChatGPT-based planners) exist and are used by teachers, but they are not yet reliable enough for full IEP-integrated special education planning without heavy editing. |
Select, store, order, issue, and inventory classroom equipment, materials, and supplies.
37CI 23–52 · exposure 38 · augmentation 50 · importance 2.8/5 · click for rater detail
Select, store, order, issue, and inventory classroom equipment, materials, and supplies.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Special education departments in schools are among the slowest adopters of automation; most inventory and supply management remains manual or uses generic, decades-old systems. Budget constraints and resistance to automation in public education limit adoption velocity significantly. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a slower-adopting sector for AI-driven administrative tools compared to finance or tech, with budget and infrastructure constraints slowing rollout. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by suggesting reorder points, flagging missing inventory, or flagging inventory from past purchases, which would help teachers manage stock more efficiently. However, the core task of selecting appropriate materials for diverse student needs remains fundamentally human-driven. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based inventory and procurement apps can meaningfully reduce time spent tracking and ordering supplies, letting teachers focus more on instructional duties. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with inventory tracking and automated ordering via APIs, the task requires physical selection and storage of materials, judgment about classroom-specific needs, and decisions about equipment suitability for students with diverse disabilities. Current systems cannot perform these discretionary choices end-to-end without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 3/5 | Inventory tracking, reordering, and supply-list generation can largely be handled by software/AI tools, but physical selection, storage, and issuing of materials still require human action.the digital portion is automatable while physical handling is not. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Schools operate under procurement regulations, budget authorization requirements, and often need human sign-off on equipment purchases and storage decisions. Additionally, special education equipment selection requires professional judgment tied to individual student IEPs, creating a de facto requirement for teacher involvement. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement ties this specifically to a certified teacher; it's largely administrative, though budget accountability and school district purchasing rules add some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Implementing and maintaining a specialized AI-driven inventory system for a school's classroom supplies likely costs more than the teacher time it saves, especially in smaller special education departments. Integration with existing school systems and oversight requirements increase costs significantly. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Digital inventory tools are cheap relative to teacher time spent on paperwork, but physical organization and handling still require paid staff time, keeping overall cost roughly comparable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Basic inventory management software exists, but integrated systems that handle selection logic, storage location optimization, and issue-tracking for educational materials are not mature in deployment. Most schools still rely on manual processes or generic inventory systems not tailored to special education equipment needs. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Inventory and procurement management software (some AI-enhanced) is widely deployed in schools and businesses, but few systems are tailored to classroom-specific special education material tracking, so adoption is partial. |
Maintain accurate and complete student records, and prepare reports on children and activities, as required by laws, district policies, and administrative regulations.
36CI 30–43 · exposure 42 · augmentation 75 · importance 4.5/5 · 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.
36| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 special education operates in heavily regulated, lower-digitization environments with strong legal conservatism; while some districts use AI-assisted note-taking tools, widespread adoption of AI for autonomous record maintenance and compliance reporting is minimal due to risk aversion and continued reliance on human-verified workflows. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a slow-adopting sector with limited AI deployment for compliance documentation due to legal sensitivity, budget constraints, and administrative caution. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully augment teachers' record-keeping by auto-generating observation summaries, flagging missing data fields, formatting compliance templates, and drafting routine sections of reports—substantially reducing clerical burden while the teacher remains responsible for accuracy and legal compliance. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly speed up drafting of progress notes, summarizing data, and organizing records, meaningfully boosting teacher productivity while they remain responsible for accuracy and compliance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with drafting reports and organizing structured data, maintaining accurate and complete student records requires nuanced judgment about what constitutes accurate documentation under varying legal and district requirements. The task involves compliance interpretation and discretionary decisions about what to record that today's systems cannot reliably handle end-to-end without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft IEP progress summaries and compile records from structured data, but final compliance-sensitive reports require human verification of accuracy and legal specifics, limiting full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Federal IDEA regulations, state special education laws, and district policies explicitly require teachers and administrators to maintain legally defensible records; liability exposure for incorrect or incomplete documentation is high, and in many jurisdictions a licensed educator must certify the accuracy and completeness of special education records. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education records are governed by IDEA, FERPA, and district regulations requiring a credentialed teacher's certification and signature, creating strong legal and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Administrative tools and AI-assisted documentation can reduce some data-entry burden, but the cost of reliable compliance oversight, error-checking, and human review to ensure legal accuracy means the total cost per fully compliant student record still approaches or exceeds the cost of a teacher's time dedicated to the task. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting can cut time spent on report writing, but integration with district-specific compliance systems and required human review keeps overall costs only moderately below fully manual work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Document management and report-generation tools with AI assistance exist in production (e.g., learning management systems with auto-generated progress notes), but they still require significant human verification and manual entry for special education law compliance, IEP specifics, and individualized documentation that varies by student and jurisdiction. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Some ed-tech and IEP software platforms include AI-assisted report drafting, but they are not yet reliably handling full compliance documentation without teacher review and correction. |
Use computers, audio-visual aids, and other equipment and materials to supplement presentations.
32CI 25–39 · exposure 30 · augmentation 75 · importance 4.0/5 · click for rater detail
Use computers, audio-visual aids, and other equipment and materials to supplement presentations.
32| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational institutions, especially special education departments, show slow adoption of AI for instructional design; budget constraints, resistance to technology in special ed, and emphasis on personalized human oversight keep velocity low. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education, especially special education, has been slower to adopt AI tools compared to information/finance sectors, with pilots more common than full integration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can powerfully assist by suggesting multimedia formats, organizing digital libraries, auto-generating captions and transcripts, and recommending equipment—all of which accelerate teacher preparation while the teacher retains full decision-making authority over appropriateness for each student. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully help teachers prepare tailored visual aids, transcripts, translations, and interactive content that enhances lesson delivery for students with diverse needs. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can help generate or organize multimedia content and suggest equipment use, but selecting appropriate supplements for individual students' disabilities and learning needs requires human judgment about pedagogical fit and student-specific accommodation strategies. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help generate or select multimedia materials, but the actual task of using and integrating equipment during live classroom instruction requires physical presence and real-time adaptation that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teachers must assess individual student needs and select accommodations that comply with IEP requirements; liability concerns around automated accommodation choices and institutional preference for human professional judgment in special education create substantial adoption friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing barrier for using AV tools, but special education students often need certified teacher oversight and individualized in-person adaptation, creating moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI content generation and media organization tools are becoming cost-competitive with teacher time spent on presentation prep, but oversight and customization still require significant human involvement, keeping costs roughly comparable to direct human effort. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cheaply assist in generating supplementary materials, but the human teacher must still operate equipment and adapt delivery in real time, limiting overall cost savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Tools exist to generate presentations, organize media libraries, and recommend equipment (e.g., AI-assisted slide design, content curation), but no product reliably handles the full task of selecting contextualized supplements for diverse special education populations in production settings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for creating slides, videos, and adaptive materials, but no deployed system autonomously operates classroom AV equipment or delivers presentations to students with disabilities reliably. |
Prepare for assigned classes, and show written evidence of preparation upon request of immediate supervisors.
29CI 25–34 · exposure 25 · augmentation 63 · importance 3.7/5 · click for rater detail
Prepare for assigned classes, and show written evidence of preparation upon request of immediate supervisors.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Education sectors lag in AI adoption overall, and special education departments are particularly cautious due to regulatory requirements and the vulnerability of the student population. Adoption remains at pilot stage with minimal production-level displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a moderately digitized but resource-constrained sector where AI tool adoption for lesson planning is emerging but not yet deeply embedded in daily practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by drafting lesson scaffolds, suggesting accommodations, and generating documentation templates, which would accelerate teacher preparation. However, the human teacher must validate all adaptations against student needs and legal requirements. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up drafting of lesson plans, differentiation strategies, and documentation, giving teachers a strong productivity boost while they retain responsibility for final content and compliance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with generating lesson plans, materials, and documentation, but special education requires individualized assessment, student-specific accommodation design, and compliance knowledge that demands human judgment. Only partial automation is feasible; significant human oversight remains essential. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft lesson plans and materials, but preparing for specific classes involves tailoring to individual IEPs, student needs, and classroom dynamics that require ongoing human judgment, so full end-to-end automation with equal quality is not yet achievable. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Special education is heavily regulated (IDEA, Section 504) and requires documented human expertise and accountability. Supervisors must verify written evidence of preparation; schools face legal liability for inadequate accommodations, creating strong barriers to full automation of this task. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no license is strictly required to use planning tools, professional accountability, IEP compliance obligations, and supervisor sign-off create moderate organizational and regulatory friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for lesson prep have low inference costs but require substantial human review and customization for special education contexts. The total cost (tool + teacher oversight) remains comparable to or higher than direct human preparation, particularly given liability and compliance constraints. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting tools are cheap relative to teacher time spent planning, but the need for human review, individualization, and compliance with IEP requirements narrows the net cost savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI tools exist for lesson planning and document generation, they cannot reliably produce the specialized, legally-compliant individualized education plans (IEPs) and accessible materials that special education demands. Products fail to meet the consistency and accuracy required in regulated educational settings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Lesson-planning AI tools exist and are used by teachers, but no deployed product reliably prepares special-education-specific, individualized class plans with documented evidence acceptable to supervisors without substantial teacher review and customization. |
Confer with other staff members to plan and schedule lessons promoting learning, following approved curricula.
28CI 25–30 · exposure 25 · augmentation 63 · importance 4.2/5 · click for rater detail
Confer with other staff members to plan and schedule lessons promoting learning, following approved curricula.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 education is a laggard sector in AI adoption, with budget constraints, regulatory caution, and strong preferences for human teacher involvement in curriculum design. Pilot use of scheduling or planning assistants is emerging, but production adoption remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education, especially special education, is a slower-adopting sector for AI tools compared to information or finance industries, with pilots more common than full production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating lesson plan templates, suggesting scheduling options, or flagging conflicts, reducing routine administrative burden on teachers. However, the human educator remains the decision-maker on curriculum fit and student accommodation, so augmentation is meaningful but not transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist by drafting lesson outlines, suggesting curriculum-aligned activities, and organizing scheduling information, helping teachers and staff collaborate more efficiently. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help draft lesson outlines or suggest scheduling options, the task requires human judgment about student needs, teacher availability, and curriculum alignment that must be vetted by educators. The human approval and coordination loop is essential and irreplaceable, limiting end-to-end automation to well below the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft lesson plans and schedules aligned to curricula, but the actual conferring, negotiating priorities, and adapting to specific students' IEPs requires human interaction and judgment that current AI cannot autonomously perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Special education teachers must legally comply with individualized education plans (IEPs) and federal requirements (IDEA), and lesson coordination requires licensed educator judgment and sign-off. Schools also face institutional inertia and professional norms favoring human-led curriculum planning. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing barrier prevents AI-assisted planning, but special education requires certified teacher judgment and compliance with IEP/legal requirements, creating moderate organizational and regulatory friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools (LLM APIs, scheduling software) may reduce planning overhead slightly, but the human coordination and validation time dominates cost. The all-in cost of AI assistance plus human oversight remains comparable to or higher than the value of incremental time savings. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply generate draft materials, but the interpersonal coordination portion still requires paid staff time, so overall cost savings are limited relative to the full task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed system reliably performs the full collaborative planning and scheduling task with consistent quality. LLMs can generate draft plans, but they lack real-time access to institutional constraints, student IEPs, and multi-party buy-in, making production-scale deployment rare and problematic. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for lesson planning assistance and scheduling tools, but no deployed system reliably conducts the collaborative staff conferencing element of this task in production. |
Collaborate with other teachers and administrators in the development, evaluation, and revision of secondary school programs.
28CI 25–30 · exposure 25 · augmentation 63 · importance 3.9/5 · click for rater detail
Collaborate with other teachers and administrators in the development, evaluation, and revision of secondary school programs.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Education technology adoption is moderate; districts are testing AI tools for grading and content generation, but core program development remains a human-governed, deliberative process with slow institutional change cycles and limited evidence of AI displacement in this specific role. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a moderately digitized but slow-adopting sector for AI in collaborative/administrative decision-making, with pilots more common than production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by summarizing stakeholder feedback, generating draft program revisions, analyzing evaluation data, or suggesting evidence-based practices—but teachers and administrators must remain the primary decision-makers. Augmentation is real but partial, not transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by drafting program proposals, summarizing student outcome data, and generating evaluation reports, augmenting teachers' and administrators' collaborative work significantly. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with drafting program materials and evaluation summaries, but cannot replicate the collaborative judgment, stakeholder negotiation, and institutional knowledge required to develop and revise secondary school programs. The human-dependent deliberative process prevents end-to-end automation. |
| Task automatability | claude-sonnet-5 | 2/5 | This involves interpersonal collaboration, negotiation, and consensus-building around institutional programs, which AI cannot conduct end-to-end; at best it can draft materials or summarize data to support the process. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Secondary school program development involves legal/regulatory oversight (special education law, curriculum standards), requires sign-off by licensed administrators and teachers, and carries high error costs if implemented without proper expertise. Professional judgment and accountability are legally embedded. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement bars AI involvement, but institutional norms, accreditation processes, and stakeholder trust favor human-led collaborative decision-making, creating moderate organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI assistance (drafting, analysis tools) costs little, but cannot replace the loaded wages of special education teachers, administrators, and meeting time required for actual collaborative work. The task fundamentally requires human decision-making and presence. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human collaboration and relationship-building costs cannot be meaningfully replaced by AI; any AI use is supplementary, so cost savings are modest relative to the labor still required from teachers/administrators. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs collaborative curriculum development and institutional program revision. AI can support document drafting and analysis, but the core task—facilitating consensus among educators with different pedagogical views and institutional constraints—remains research-stage in practice. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for drafting curriculum documents or analyzing IEP/program data, but no deployed system actually participates in collaborative program development and revision meetings with staff. |
Develop and implement strategies to meet the needs of students with a variety of handicapping conditions.
25CI 25–25 · exposure 25 · augmentation 63 · importance 4.3/5 · 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 | Adoption of AI in special education is nascent; schools are primarily piloting assistive tools and data dashboards, not agent-based strategy design systems, due to regulatory, liability, and relationship-intensive nature of the work. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a slow-adopting, highly regulated public sector niche with limited AI deployment beyond pilot programs for content generation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by recommending evidence-based interventions, organizing student data, flagging patterns in behavior or progress, and generating draft strategy components, but the teacher must synthesize and adapt these for each student's unique profile. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by suggesting differentiated strategies, generating accommodations ideas, and drafting materials, notably speeding up teacher planning while the teacher retains decision-making. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in generating generic intervention templates and identifying evidence-based strategies from literature, developing individualized strategies requires real-time assessment of each student's unique disabilities, learning patterns, and classroom dynamics—something current AI cannot do reliably end-to-end without substantial human oversight and adaptation. |
| Task automatability | claude-sonnet-5 | 2/5 | Designing individualized behavioral and instructional strategies for diverse disabilities requires ongoing clinical judgment, relationship-building, and real-time adaptation that current AI cannot autonomously perform end-to-end., |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal mandates (IDEA, IEPs) require a licensed special education teacher to develop and take responsibility for individualized education plans; liability, accountability, and regulatory requirements create hard barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | IEP development and specialized instruction implementation are legally regulated (IDEA) and generally require certified special education staff, creating strong authorization and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of deploying AI advisory systems plus required teacher review and customization does not yet undercut the wage of a special education teacher who must remain accountable for student outcomes; the human remains essential. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply generate draft materials, but the human oversight, assessment, and implementation needed for compliance and efficacy keep overall costs comparable to or only modestly below a teacher's time. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs this task autonomously; existing AI can support strategy selection from databases or suggest frameworks, but schools do not replace special education teachers with AI systems for this core responsibility, and clinical judgment of student needs remains human-driven. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product independently designs and implements comprehensive special-education intervention strategies; existing tools only support drafting or resource suggestions within a teacher-led process. |
Modify the general education curriculum for students with disabilities, based upon a variety of instructional techniques and technologies.
25CI 25–25 · exposure 25 · augmentation 63 · importance 4.2/5 · click for rater detail
Modify the general education curriculum for students with disabilities, based upon a variety of instructional techniques and technologies.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Special education remains a conservative, compliance-heavy sector where teachers are scarce and irreplaceable in practice. Adoption of AI for curriculum design is still in pilot phases; most districts rely on teacher expertise and existing frameworks rather than automated tools. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a slower-adopting sector with limited AI tooling penetration, constrained budgets, and cautious rollout due to compliance concerns. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist teachers by suggesting accessibility modifications, identifying resource libraries, or auto-generating first drafts of adapted materials, moderately raising productivity. However, the human teacher's expertise in understanding the individual student's disability and learning needs remains central. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist teachers by generating differentiated materials, adapting reading levels, suggesting instructional strategies, and saving prep time, while the teacher retains responsibility for individualized decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate curriculum suggestions and identify accessibility modifications using templates, the task requires nuanced judgment about individual student disabilities, learning profiles, and instructional fit that current systems cannot reliably perform end-to-end. Meaningful automation would need to be combined with substantial human oversight, falling short of the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft modified materials and suggest accommodations, but determining appropriate modifications requires deep knowledge of individual student IEPs, disability-specific needs, and pedagogical judgment that AI cannot reliably perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Special education curriculum modification is governed by IEPs (Individualized Education Programs) and IDEA, requiring documented human professional judgment and accountability. Teachers and districts face legal and liability exposure if curriculum modifications are solely AI-generated without qualified educator review and certification. |
| Adoption barriers | claude-sonnet-5 | 4/5 | IEP-driven curriculum modifications are legally mandated under special education law (IDEA) and require certified teacher/specialist involvement and documentation, creating strong regulatory and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI curriculum tools require significant integration, personalization, and oversight by qualified educators; the all-in cost per modified curriculum unit likely exceeds the cost of a teacher doing the work, especially when quality assurance is factored in. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI drafting tools are cheap per output, the required human review, IEP compliance checking, and individualization mean overall cost savings are modest rather than transformative. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some curriculum design tools and accessibility checkers exist, but no deployed product reliably modifies curricula for diverse disability profiles at the quality required for educational use. Most solutions are narrow (e.g., text-to-speech accessibility) rather than comprehensive curriculum adaptation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some edtech products offer differentiated content generation and accessibility adaptations, but no deployed product reliably performs individualized curriculum modification aligned to specific IEP goals at scale. |
Administer standardized ability and achievement tests, and interpret results to determine students' strengths and needs.
23CI 20–25 · exposure 25 · augmentation 63 · importance 3.8/5 · click for rater detail
Administer standardized ability and achievement tests, and interpret results to determine students' strengths and needs.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | School districts adopt assessment software slowly due to budget constraints, regulatory caution, and union/cultural resistance to automation in special education; pilots exist but widespread production deployment of AI-driven interpretation remains limited in this sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a highly regulated, relatively low-digitization sector where AI adoption for formal assessment remains in pilot stages, not production-scale replacement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted test scoring, data visualization, and flagging of unusual patterns can meaningfully assist teachers in the administrative and analytical portions of the task, but the interpretive and clinical judgment core remains human-led and relies on teacher expertise rather than being transformed by AI. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully speed up scoring, generate draft interpretive summaries, and highlight patterns in test data, materially aiding teachers who remain responsible for professional judgment and reporting. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can score multiple-choice test items and generate summary statistics automatically, interpreting results to determine individual student strengths and needs requires contextual judgment, knowledge of the student's history, and understanding of educational and psychological nuance that current systems cannot reliably perform end-to-end without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help interpret score reports and suggest patterns, but administering standardized tests requires physical presence, direct student observation, and professional judgment about testing conditions and accommodations that current systems cannot handle end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Special education assessment is heavily regulated under IDEA and Section 504; interpretations inform legally binding IEP decisions; liability and error costs are high if an automated assessment leads to misidentification; schools typically require a licensed educator (teacher or psychologist) to sign off on results and drive instructional decisions. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Special education law (IDEA) and licensing standards require qualified professionals to administer and interpret assessments and to certify results for legal IEP documentation, creating a hard regulatory barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Automated test administration and scoring reduce some administrative costs, but the AI solutions currently deployed still require substantial human oversight, interpretation, and clinical judgment, keeping total cost (system + human time + liability management) comparable to or higher than traditional teacher-led assessment. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Test administration still requires a proctor's time and specialized materials; AI can cut some scoring/report-writing time but doesn't eliminate the core labor cost, so savings are modest relative to human wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Scoring engines exist and are deployed in some educational platforms, but end-to-end interpretation and needs assessment at the level a special education teacher performs (incorporating clinical judgment, IEP implications, and legal/compliance considerations) is not yet reliably available in production systems; most implementations require human review and decision-making. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some scoring/interpretation software exists and is widely used, but full administration and clinically valid interpretation for IEP purposes still requires certified professionals; no deployed product performs the whole task. |
Prepare materials and classrooms for class activities.
20CI 5–35 · exposure 13 · augmentation 38 · importance 4.4/5 · click for rater detail
Prepare materials and classrooms for class activities.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Schools, especially special education departments, are among the slowest sectors to adopt AI due to funding constraints, regulatory oversight, and the irreplaceable role of human judgment in customizing learning environments for students with disabilities. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a low-digitization, human-intensive physical environment with minimal AI/robotics adoption for tasks like classroom setup. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by generating customized worksheets, organizing digital resources, and drafting lesson-aligned materials, but the teacher remains the primary decision-maker for classroom setup and accessibility accommodations. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help generate checklists, lesson plans, or material lists to inform preparation, but it offers little direct assistance with the physical act of setting up the classroom. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with creating some classroom materials (e.g., generating worksheets or organizing digital content), the physical setup of classrooms for students with diverse, individualized needs—arranging adaptive equipment, safety modifications, and sensory accommodations—requires in-person judgment and cannot be automated end-to-end to achieve 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical task involving arranging rooms, setting up materials, and organizing physical space, which current AI systems cannot perform without robotic embodiment.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Special education teachers must assess individual student needs and ensure classroom safety and accessibility; these responsibilities carry legal and liability weight under IDEA and ADA, and many physical setup tasks require human presence and judgment, creating organizational and regulatory friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically bars automation of physical setup, but practical barriers like lack of embodied AI capability and need for adaptive, individualized special-ed material staging create friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI can reduce costs for generating some written materials, but the physical labor of setting up classrooms, handling adaptive equipment, and customizing spaces remains labor-intensive and currently cheaper to staff with humans than to fully automate. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for physical classroom setup, so the human remains the only cost-effective option; deploying robotics for this narrow task would be far more expensive. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Current AI products can draft materials and organize digital resources, but no deployed system reliably handles the full task of preparing physical classrooms with specialized equipment and accessibility features that special education requires. Any automation remains limited to narrow, low-stakes portions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product physically prepares classrooms or materials; this remains entirely a research-stage robotics challenge, not a commercial reality for education settings. |
Guide and counsel students with adjustments, academic problems, or special academic interests.
18CI 11–25 · exposure 13 · augmentation 50 · importance 4.1/5 · click for rater detail
Guide and counsel students with adjustments, academic problems, or special academic interests.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Schools remain slow to adopt AI for direct student support roles due to trust concerns, regulatory caution, and the human-intensive nature of special education practice. Most pilots are still in early stages, with production deployment of AI counseling rare outside niche contexts. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a moderately low-digitization, high-touch human service sector with slow, cautious AI adoption for direct student counseling roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by drafting progress notes, suggesting evidence-based intervention strategies, or organizing student accommodation data, but the core counseling and relationship work remains teacher-led. This represents meaningful but bounded productivity enhancement rather than transformative augmentation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers prepare talking points, analyze academic performance data, or draft individualized suggestions, but the counseling interaction itself still depends heavily on the human. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can provide general information and suggest academic adjustments, guiding and counseling students with special needs requires understanding individual learning profiles, emotional context, and nuanced relationship-building that current AI systems cannot reliably replicate end-to-end. The task demands sustained, contextual judgment that goes well beyond 50% time-saving at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | Counseling students with special education needs requires relational trust, in-person judgment, and adaptive emotional support that current AI cannot deliver end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal and regulatory barriers are substantial: secondary school guidance involves duty of care, potential safety concerns, and often requires a licensed educator or counselor to document and take responsibility for student support decisions. Schools face liability and parental expectations that a qualified human be directly responsible. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education law (IEPs, IDEA compliance) and duty-of-care obligations require certified professionals to make and document these judgments, creating strong regulatory and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The inference cost of running an AI counseling system plus the human oversight required to ensure accuracy and safety approaches or exceeds the cost of a special education teacher's time for this task, especially given low error tolerance in student guidance. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools are cheap per interaction, but since AI cannot substitute for the core counseling function, a human teacher's cost remains necessary, making true cost comparison largely moot. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI chatbots can draft discussion prompts or educational resources, but no deployed product reliably performs the core counseling and personalized guidance function at scale in secondary settings. Existing systems lack the ability to assess emotional readiness, adapt to in-the-moment student resistance, and maintain appropriate professional boundaries that special education requires. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product independently guides or counsels special education students on personal, academic, or emotional adjustment issues in production settings. |
Provide interpretation and transcription of regular classroom materials through Braille and sign language.
18CI 10–25 · exposure 17 · augmentation 38 · importance 3.5/5 · click for rater detail
Provide interpretation and transcription of regular classroom materials through Braille and sign language.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools remain reliant on human special education staff and contracted interpreters; adoption of AI tools in this function is minimal because of regulatory compliance, student safety, and the specialized nature of the work, which is not typical of high-tech sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a slow-adopting, resource-constrained sector where AI tools for accessibility are used sparingly and mostly as aids rather than replacements. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can provide modest assistance through draft Braille layout suggestions or transcription templates, but the core work of interpretation and ensuring pedagogical accuracy requires the human educator to remain fully in the loop, limiting transformative augmentation potential. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted Braille conversion tools and captioning/sign-avatar aids can meaningfully speed up material preparation, though human interpreters remain essential for live instructional interaction. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Braille transcription and sign language interpretation require deep contextual understanding, pedagogical judgment, and real-time adaptation to student needs that current AI systems cannot reliably perform end-to-end. While AI can assist with transcription layout or draft Braille conversion, the task demands human expertise in accessibility standards, linguistic nuance, and educational appropriateness that falls far short of the 50% time-saving bar. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can transcribe printed text into Braille-ready formats reasonably well, but real-time sign language interpretation of classroom content requires nuanced, contextual, live human signing that current AI cannot reliably perform. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal and regulatory requirements mandate appropriate accommodations under the IDEA and Section 504; schools must ensure educational access, and there are significant liability concerns if automated transcription or interpretation fails to meet accessibility standards or student needs, creating strong institutional friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education law (IDEA/ADA) often mandates qualified human interpreters and certified transcribers for legal compliance and accommodation quality, creating strong regulatory and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI transcription and interpretation services, where they exist, still require human review and correction, making them cost-prohibitive compared to hiring or contracting with qualified special education specialists who can deliver this service directly. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Braille transcription software is cheap, but reliable sign-language interpretation still requires human interpreters or expensive specialized systems with human oversight, keeping overall costs comparable to human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some narrow components exist (e.g., text-to-Braille rule engines for simple passages), but no deployed product reliably handles the full scope of classroom material transcription with accuracy and educational fidelity. Sign language interpretation remains nearly entirely human-performed; AI video-to-sign-language systems are research-stage with high error rates. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Braille transcription software is mature and used in production, but automated sign-language interpretation products remain unreliable and narrow, especially for classroom-level academic content and student interaction. |
Observe and evaluate students' performance, behavior, social development, and physical health.
16CI 7–25 · exposure 13 · augmentation 50 · importance 4.2/5 · click for rater detail
Observe and evaluate students' performance, behavior, social development, and physical health.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Special education remains a highly human-centered field with strong resistance to depersonalization; adoption of AI observation tools in school districts is minimal. Regulatory constraints, liability concerns, and the profession's emphasis on individual student relationships slow meaningful deployment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a historically low-digitization, high-human-contact sector with slow AI adoption for core instructional/observational duties, though administrative AI tools are creeping in elsewhere. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can provide useful assistance by flagging behavioral patterns in video, organizing health data, or drafting observation summaries for review, which could reduce documentation burden. However, the core task—making sound judgments about development and appropriately responding—remains firmly human-driven, limiting transformative augmentation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by analyzing patterns in attendance, grades, or behavior logs and drafting progress notes, providing moderate assistance, but the core observational judgment remains human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with some observation components (e.g., video analysis of classroom behavior, documentation of physical health indicators), but meaningful evaluation requires nuanced judgment about social-emotional development, behavioral context, and individualized responses that demand human presence and professional interpretation. Current systems cannot reliably replace the holistic, contextual assessment that special education law and practice require. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires in-person, continuous observation of students' behavior, social interactions, and physical presence, which AI cannot perform end-to-end; no current system can substitute for the teacher's direct real-time observation and relational judgment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Special education is heavily regulated (IDEA, Section 504) and requires professional educators to conduct individualized evaluations and document observations for legal compliance. Teachers are legally responsible for safety monitoring and assessment decisions; liability and fiduciary requirements create strong barriers to full automation without a licensed educator's direct involvement and sign-off. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education law (IDEA, IEP requirements) mandates qualified/licensed personnel to conduct and document behavioral and developmental evaluations, creating strong regulatory and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Implementation of video monitoring, behavior-tracking software, and health data integration requires significant infrastructure, annotation overhead, and human oversight to validate findings. These costs approach or exceed the marginal cost of having a trained teacher observe directly, especially given the need for professional interpretation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that performs this observational task at all, so cost comparison is moot—the human is the only capable performer, making AI's per-task cost effectively infinite in the relevant scope. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While video analysis and behavioral tracking tools exist in research and pilot settings, no deployed product reliably performs comprehensive observation and evaluation of students' social, emotional, and physical development at the quality required for special education decision-making. Systems lack the reliability and legal accountability to substitute for professional observation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs holistic in-person observation and evaluation of a student's behavior, social development, and physical health; existing AI tools only support narrow adjacent tasks like scoring written assessments. |
Confer with parents, administrators, testing specialists, social workers, or other professionals to develop individual educational plans (IEPs) for students' educational, physical, and social development.
16CI 6–25 · exposure 13 · augmentation 50 · importance 4.5/5 · click for rater detail
Confer with parents, administrators, testing specialists, social workers, or other professionals to develop individual educational plans (IEPs) for students' educational, physical, and social development.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 special education adoption of AI agents remains minimal; most districts use basic data-management tools, not autonomous IEP conferencing systems. Adoption is hampered by legal risk aversion, union concerns, and the requirement that special educators remain central to the process. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a heavily regulated, relationship-driven public sector with slow AI adoption for core compliance and decision-making processes, though administrative AI tools are slowly appearing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by pre-populating data summaries, suggesting evidence-based goals, flagging compliance gaps, and organizing prior evaluations, which reduces clerical burden and improves information synthesis during conferences. However, the core negotiation and decision-making remain human-centered. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help draft IEP goals, summarize assessment data, and prepare meeting materials, meaningfully assisting teachers even though the core collaborative process remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with drafting IEP components (goals, accommodations, data summaries), the task fundamentally requires negotiating among multiple stakeholders with competing priorities, legal accountability, and individualized judgment about a specific student's needs. Current AI cannot reliably replace the interpersonal consensus-building and professional judgment that define this task. |
| Task automatability | claude-sonnet-5 | 1/5 | This task centers on live, multi-party human conferencing involving relational trust, negotiation, and legal/clinical judgment about a specific child, which current AI cannot conduct end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | IEPs are legally mandated documents requiring professional sign-off by certified educators and often involve IDEA compliance; parents must meaningfully participate, and liability falls on school districts if an AI-generated plan is inadequate. These regulatory and human-contact requirements create strong barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | IEPs are legally mandated documents under IDEA requiring signatures, professional judgment, and accountability from certified educators and specialists, making unsupervised AI substitution legally impermissible. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | An AI agent for IEP drafting assistance would still require significant human oversight (special education teacher, administrator review, parent engagement), making the all-in cost (inference, integration, mandatory human verification) comparable to or higher than a teacher's marginal time cost for direct conference facilitation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human facilitation, negotiation, and legal accountability for IEP meetings still require qualified staff time; AI can only cut some drafting/documentation costs, not the core meeting cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI system performs end-to-end IEP conferencing reliably in production. AI tools exist for generating goal templates or organizing student data, but actual IEP development involves complex multi-party dialogue, legal compliance verification, and contextual understanding that current systems handle only partially and with material error risk. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product independently conducts IEP meetings or synthesizes multi-stakeholder input into finalized plans; at best AI tools assist with note-taking or drafting portions. |
Plan and conduct activities for a balanced program of instruction, demonstration, and work time that provides students with opportunities to observe, question, and investigate.
15CI 5–25 · exposure 17 · augmentation 63 · importance 4.0/5 · 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.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | School districts remain slow adopters of autonomous AI systems for core instructional delivery, particularly in specialized contexts like special education. Most adoption is limited to administrative or supplemental tools rather than direct replacement of instructional planning and conduction. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a low-digitization, high-human-contact sector where AI adoption for actual instruction delivery remains in early pilot stages rather than production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by generating activity ideas, organizing lesson materials, and suggesting differentiation strategies that a special education teacher then adapts and implements. However, augmentation is modest because the core task—observing, questioning, and investigating with individual students—remains fundamentally human-centered. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully help teachers brainstorm differentiated activities, create materials, and adapt content to varied learning needs, substantially aiding lesson preparation even though delivery remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires ongoing judgment about individual student needs, real-time responsiveness to classroom dynamics, and adaptive scaffolding—capabilities that current AI cannot perform end-to-end in a live educational setting. While AI can help draft lesson plans or suggest activities, it cannot conduct the full instructional program with sustained quality oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft activity ideas and lesson plans, but planning and conducting a balanced, adaptive instructional program for students with diverse disabilities requires ongoing 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 a licensed profession in most jurisdictions, and instructional planning and delivery by a qualified special educator are often legally mandated. Additionally, the ethical and relational dimensions of working with students with disabilities create strong organizational and human-contact requirements. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education teaching requires state licensure/certification, IEP compliance, and direct human interaction and legal accountability for student welfare, creating strong regulatory and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The human cost of a qualified special education secondary teacher is high; AI tools for lesson planning or activity generation cost far less per use but do not eliminate the need for human instruction, supervision, and differentiation. The AI cannot replace the teacher's core function. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply generate planning materials, but the execution portion (conducting activities, live supervision) still requires a paid, present human teacher, keeping overall cost comparable to human-only delivery. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some AI tools can assist with generating lesson plan ideas or activity suggestions, but no deployed product reliably conducts this task autonomously. Classroom instruction demands continuous interpersonal assessment and adjustment that deployed systems have not achieved at scale in secondary special education contexts. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Lesson-planning assistants and content generators exist and are used by teachers, but no deployed product actually conducts differentiated special-education instruction and hands-on activities in the classroom. |
Instruct through lectures, discussions, and demonstrations in one or more subjects, such as English, mathematics, or social studies.
14CI 9–20 · exposure 20 · augmentation 63 · importance 4.0/5 · click for rater detail
Instruct through lectures, discussions, and demonstrations in one or more subjects, such as English, mathematics, or social studies.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Public K–12 education, especially special education, has laggard adoption of automation due to regulatory constraints, unionization, community preference for human educators, and low organizational digitization relative to information or finance sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education, especially special education, is a slow-adopting sector with limited AI deployment for direct instruction due to funding, regulation, and workforce structures. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist teachers in generating lesson materials, drafting explanations, and organizing practice problems, improving preparation efficiency. However, the core instructional act—live delivery, dynamic adaptation, and relationship-building with special needs students—remains primarily human-led, limiting augmentation scope. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by generating differentiated lesson materials, practice problems, accommodations, and content explanations, substantially aiding lesson prep and in-class support tools. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate lecture content and factual explanations, classroom instruction requires real-time interaction, adaptive pacing, behavioral management, and responsiveness to diverse learner needs that current AI cannot reliably perform. The specialized cognitive and emotional demands of special education teaching—scaffolding for students with varying disabilities and learning profiles—fall well short of the 50% time-saving threshold for end-to-end automation. |
| Task automatability | claude-sonnet-5 | 2/5 | Live classroom instruction requires real-time human presence, behavior management, and adaptive interpersonal engagement with students with disabilities that current AI cannot replicate end-to-end.dailyRoutine.The task involves physical delivery, classroom management, and relationship-building that AI cannot perform independently at scale. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Special education instruction is legally mandated in many jurisdictions to be delivered or directly supervised by a certified special education teacher. Federal law (IDEA) and state licensing requirements create hard barriers: a licensed educator must design, deliver, and be accountable for instruction to students with IEPs. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Teaching certification, IEP compliance, in-person supervision requirements, and legal mandates for qualified special education teachers create hard regulatory and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The infrastructure, oversight, and content customization required to deploy AI instruction rivals or exceeds the cost of a trained teacher, particularly when accounting for the regulatory and liability frameworks around special education service delivery. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task alone, so cost comparison favors the human teacher who is legally and practically required to be present. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably conducts live classroom instruction to special education students at scale. AI chatbots and tutoring systems exist but show material limitations in handling emotional support, behavioral intervention, and individualized pedagogical adjustment required in secondary special education contexts. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously delivers live secondary special education instruction in classrooms; existing AI tools are supplementary content generators or tutoring aids, not autonomous instructors. |
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 4.5/5 · 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 | Education, particularly special education, shows slow AI adoption relative to information sectors; most current use is assistive (lesson planning tools) rather than autonomous instruction, with strong institutional and legal resistance to replacing credentialed teachers. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a low-digitization, high-human-contact sector with minimal AI agent deployment for direct instructional delivery. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by generating adaptive strategy suggestions, tracking progress data, and recommending perceptual-motor interventions, allowing teachers to focus on delivery and real-time adjustment, though the human teacher remains essential for responsive instruction. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can help generate individualized materials, practice exercises, or track progress data, offering moderate assistance while the teacher retains full instructional responsibility. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate instructional strategies and provide technique recommendations, the core task requires real-time adaptation to individual students' sensory-motor and cognitive responses during live instruction, which current AI cannot reliably perform end-to-end. AI tools can assist in planning but cannot replace the interactive, responsive nature of special education instruction. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires live, embodied, adaptive interaction with students with disabilities, applying specialized pedagogical judgment in real time; AI cannot execute this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: special education teachers must be licensed and credentialed; legal liability for student welfare and IEP compliance rests with the human educator; and parent/guardian expectations require human accountability for instruction affecting disabled children's development. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Special education instruction is legally mandated to be delivered/overseen by certified special education teachers under IEP compliance and disability law, creating strong licensing and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI systems capable of generating educational strategies plus required human oversight and customization is comparable to or exceeds the cost of a teacher's time, given the safety-critical nature of special education instruction. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this in-person instructional task, so cost comparison favors the human teacher entirely. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs this task autonomously in production classroom settings. AI can generate strategy suggestions and lesson plans, but educators report these require substantial customization and human judgment to implement effectively with disabled students. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously deliver sensory-, perceptual-motor, or cognitive-development instruction to special education students in classrooms; this remains a human-delivered practice. |
Teach socially acceptable behavior, employing techniques such as behavior modification and positive reinforcement.
13CI 0–25 · exposure 13 · augmentation 50 · importance 4.3/5 · click for rater detail
Teach socially acceptable behavior, employing techniques such as behavior modification and positive reinforcement.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Special education adoption of AI is slow; schools remain conservative with this population, pilots are limited, and regulatory/legal compliance requirements slow experimentation. Most districts still rely on human special educators and behavior specialists rather than AI-first approaches. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a highly regulated, in-person, low-digitization sector with minimal AI deployment for direct behavioral instruction. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating personalized behavior intervention ideas, tracking patterns across student interactions, suggesting positive reinforcement strategies, and flagging concerning trends—useful supports that may raise teacher productivity. However, the human teacher remains central to execution and judgment. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers design behavior modification plans, generate reinforcement schedules, or track progress data, offering moderate assistance to the planning side of this task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could provide behavior modification frameworks, scripts, and reinforcement suggestions, this task fundamentally requires real-time interaction with students, reading nonverbal cues, and building trust relationships—elements current AI cannot replicate end-to-end. AI cannot substitute for the continuous human presence and adaptive judgment needed to actually teach and reinforce behavior in classroom settings. |
| Task automatability | claude-sonnet-5 | 1/5 | Teaching socially acceptable behavior requires real-time human relationship-building, modeling, and behavioral intervention with a student in a live social context that AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: special education law (IDEA) and IEPs typically require a qualified educator to design and oversee behavior plans; liability is asymmetric (failures are costly); parental trust and human contact are often deemed essential for vulnerable populations; schools are risk-averse about delegating this task to AI alone. |
| Adoption barriers | claude-sonnet-5 | 5/5 | IEP-mandated behavioral interventions typically require certified special education teachers or behavior analysts, with legal and ethical accountability for student welfare that cannot be delegated to software. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The loaded cost of a special education teacher includes expertise in diagnosis, relationship-building, and legal accountability for progress. Even if AI could assist, the teacher remains essential. Current AI tools for behavior support do not offer an order of magnitude cost reduction over human labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task, so cost comparison favors the human entirely; any AI role is purely supportive, not a replacement of comparable output. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI chatbots and behavior recommendation systems exist but lack deployment at scale for real classroom behavior modification. No mature production system currently deploys AI as a primary agent for teaching socially acceptable behavior to secondary students; existing tools are narrowly scoped supplements only. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product independently delivers behavior modification instruction to students with disabilities; this remains firmly in the domain of trained human educators and behavior specialists. |
Prepare students for later grades by encouraging them to explore learning opportunities and to persevere with challenging tasks.
13CI 5–20 · exposure 8 · augmentation 50 · importance 4.3/5 · click for rater detail
Prepare students for later grades by encouraging them to explore learning opportunities and to persevere with challenging tasks.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Special education remains a low-digitization, high-regulation sector with strong union presence and focus on human relationships; while edtech tools are emerging in mainstream education, adoption of AI-driven motivational support in special education remains minimal and cautious. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a low-digitization, high-human-contact sector with minimal AI agent deployment for core instructional/motivational tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating personalized encouragement templates, tracking student progress toward goals, and suggesting evidence-based strategies for building perseverance, helping teachers tailor support more efficiently. However, the augmentation is limited by the need for human judgment about each student's emotional state and disability-specific needs. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help generate personalized learning materials, track progress, or suggest strategies to support perseverance, aiding but not replacing the teacher's role. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate motivational content and suggest learning resources, the core task of encouraging individual students and fostering perseverance requires sustained interpersonal relationship-building and emotional responsiveness that current AI systems cannot reliably deliver. A significant portion of time (assessment, goal-setting, resource curation) could be partially automated, but the encouragement and relationship components—critical to the task's purpose—remain fundamentally human. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires ongoing relational motivation, mentorship, and adaptive encouragement tailored to individual students with disabilities, which cannot be executed end-to-end by AI today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Special education law (IDEA, IEPs) requires trained educators to design individualized support and monitor student progress; liability for inadequate encouragement or failure to support perseverance rests with the school and licensed teacher, not an AI system. Human-contact and legal accountability are substantial barriers. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education involves legal mandates (IEPs), certification requirements, and required human judgment and accountability for student progress and wellbeing. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-generated motivational messages and resource recommendations are inexpensive to produce, but oversight by a special educator (to ensure personalization, appropriateness for individual students' disabilities and needs) adds significant labor cost, negating most cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task, so cost comparison favors the human teacher entirely. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs sustained student encouragement and perseverance-building at scale in production classroom settings. AI tutoring systems exist but focus on content delivery; none have demonstrated reliable capability to replicate the motivational and emotional support aspects that define this task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously motivates and prepares special education students for future grades; this remains fundamentally a human relational task. |
Coordinate placement of students with special needs into mainstream classes.
13CI 0–25 · exposure 13 · augmentation 38 · importance 4.1/5 · click for rater detail
Coordinate placement of students with special needs into mainstream classes.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | School districts, particularly in special education, have been slow to adopt AI-driven automation; most adoption remains limited to administrative scheduling tools rather than decision-making, and cultural preference for human expert judgment remains strong. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education administration is a low-digitization, highly regulated public sector context where AI adoption for placement decisions is essentially absent. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by organizing student data, flagging compatibility considerations, and generating candidate placements for review, but the educator remains the decision-maker and must validate all recommendations against legal and pedagogical criteria. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help organize scheduling data or summarize IEP documentation, but it offers limited assistance to the core coordination and negotiation work involved in placement decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in data aggregation and preliminary matching of student needs to class offerings, the task fundamentally requires judgment about individual student capabilities, teacher readiness, classroom dynamics, and legal/IEP compliance—domains where AI lacks reliable decision-making authority and human oversight is legally required. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires interpersonal coordination, judgment about individual student needs, and consensus-building among teachers, parents, and administrators—none of which current AI can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: IEP (Individualized Education Program) compliance is legally mandated, special educators must sign off on placements, and federal law (IDEA) requires qualified professionals to make placement decisions—a licensed educator's judgment cannot be fully delegated. |
| Adoption barriers | claude-sonnet-5 | 5/5 | IEP placement decisions are governed by special education law (IDEA) requiring qualified professionals, parental consent, and legal accountability, making this a hard-barrier task with mandated human sign-off. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI systems capable of handling the nuanced judgment, legal compliance checking, and stakeholder communication involved would likely exceed or approach the cost of a part-time special educator performing the task, especially given oversight requirements. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this coordination task, so cost comparison favors the human entirely; any AI cost would be additive overhead, not a replacement. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs end-to-end placement coordination independently; existing tools may support scheduling or record management but cannot replace the evaluative and relational work that requires special education expertise and familiarity with individual students. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products manage the interpersonal coordination and case-by-case decision-making required for placement decisions; this remains a human administrative and clinical judgment task. |
Plan and supervise class projects, field trips, visits by guest speakers, or other experiential activities, and guide students in learning from those activities.
10CI 9–11 · exposure 16 · augmentation 50 · importance 3.2/5 · 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.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools operate under strict liability frameworks and certification requirements; adoption of AI to replace teacher supervision of field trips and experiential learning is essentially non-existent and prohibited by law in most jurisdictions. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a low-digitization, highly regulated sector where AI adoption for hands-on supervisory tasks remains minimal, with most AI use confined to administrative or content-generation support. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating field trip itineraries, drafting reflection prompts, organizing logistics, and suggesting learning activities tied to curriculum standards, meaningfully reducing planning burden while the teacher retains supervisory and pedagogical control. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can meaningfully assist in planning by generating project ideas, itineraries, permission forms, and reflection prompts, improving teacher efficiency in the planning phase even though supervision itself is unaided. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Planning logistical aspects (schedules, itineraries, checklists) can be partially automated, but the core task—supervising students, managing interpersonal dynamics, and guiding learning from experiences—requires human presence and real-time judgment. AI cannot meaningfully replace the supervisory and pedagogical components that dominate this task. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help brainstorm project ideas, draft itineraries, or generate materials, but the core work of supervising students, coordinating logistics, ensuring safety, and adapting to individual special-education needs in real time cannot be automated by current systems. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and regulatory barriers exist: special education teachers must hold state certification, are legally responsible for student safety and welfare during supervised activities, and cannot be replaced by automated systems due to duty-of-care requirements and IDEA mandates for individualized support. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Legal and safety requirements mandate certified special education teachers supervise students during trips and activities, with liability, IEP compliance, and duty-of-care obligations that require human physical presence and professional judgment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying systems to handle supervision and real-time pedagogical guidance far exceeds what a teacher's labor would cost; moreover, no viable AI substitute exists at any price for the core supervisory functions. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since AI cannot perform the physical supervision and safety-critical components, the human teacher's cost cannot be substituted, making AI not a viable cheaper alternative for the task as a whole. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably supervises students during field trips or manages the dynamic interpersonal guidance required during experiential learning activities. While AI can assist with planning documents, the actual execution and supervision remain outside the scope of current production systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed products that supervise field trips or guest speaker visits or manage in-person experiential learning for students with disabilities; this remains entirely outside current AI product scope. |
Teach personal development skills, such as goal setting, independence, and self-advocacy.
9CI 5–14 · exposure 8 · augmentation 50 · importance 4.3/5 · click for rater detail
Teach personal development skills, such as goal setting, independence, and self-advocacy.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Special education remains a heavily regulated, staffing-constrained sector with strong resistance to substituting human instruction for these high-stakes, individualized tasks. Adoption of AI for classroom teaching of personal development skills remains near zero in production settings. |
| 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 instruction of socio-emotional skills. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating sample goal-setting templates, self-advocacy scripts, or discussion prompts that teachers then adapt and deliver. However, the augmentation is limited because the core task—building skills through interaction and modeling—remains fundamentally teacher-driven. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers generate individualized goal-setting worksheets, social stories, or self-advocacy scripts, providing moderate support while the teacher still delivers the core instruction. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate content about goal-setting frameworks and self-advocacy techniques, teaching these skills requires modeling, real-time interaction, behavioral feedback, and emotional rapport—elements that demand human presence. No AI system achieves 50% time savings on the full task of skill development in students with diverse special needs. |
| Task automatability | claude-sonnet-5 | 1/5 | Teaching personal development skills to secondary students with disabilities requires relational trust, live behavioral modeling, and adaptive judgment that current AI cannot replicate end-to-end in a classroom setting. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulations (IDEA) and IEPs legally require qualified special education personnel to provide individualized instruction and assessment of student progress. There is also a strong human-contact requirement for teaching personal development and self-advocacy—skills that rely on trust, modeling, and relational learning. |
| Adoption barriers | claude-sonnet-5 | 4/5 | IEP-mandated instruction requires certified special education teachers legally responsible for student progress, and building trust-based interpersonal skills with vulnerable populations creates strong professional and regulatory barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI tools for content generation or tutoring support cost little, but comprehensive delivery of this interpersonal, development-focused teaching would require AI systems that match or exceed a special education teacher's effectiveness—an unmet bar. Human instruction remains more cost-effective for genuine skill acquisition. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since AI cannot perform this task independently at acceptable quality, there is no valid cost-per-task-equivalent comparison; the human teacher remains essential, making AI substitution costlier in practice. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs the end-to-end teaching of personal development skills to secondary special education students. Chatbots and learning platforms exist but cannot replace the adaptive, relational, and individualized nature of teaching self-advocacy and independence in real classroom settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product independently teaches self-advocacy or goal-setting skills to special education students; existing tools are limited to supplementary content generation, not instruction delivery. |
Attend professional meetings, educational conferences, and teacher training workshops to maintain and improve professional competence.
9CI 5–14 · exposure 8 · augmentation 50 · importance 3.5/5 · click for rater detail
Attend professional meetings, educational conferences, and teacher training workshops to maintain and improve professional competence.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task is intrinsically human-centered and anchored in regulatory and institutional requirements for teacher licensure and professional credentialing; there is no sector adoption pattern of AI attendance at conferences or training workshops. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Education sector, especially K-12 special education, shows slow AI adoption for administrative/professional development activities like conference attendance. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by curating relevant conference sessions, generating notes from presentations, extracting key concepts, or summarizing post-conference materials—moderately enhancing a human teacher's efficiency in processing and retaining professional content. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers find relevant conferences, summarize sessions, take notes, or synthesize learning afterward, but cannot replace the attendance and interactive learning itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can summarize conference materials and generate summary notes, the task fundamentally requires human presence, networking, and live professional judgment to gain competence and stay current. AI can assist with pre- and post-meeting documentation but cannot substitute for the interactive and reflective core. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically and socially attending meetings, conferences, and workshops for professional development requires human presence, networking, and live engagement that AI cannot substitute for. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong organizational and professional norms, employer expectations, and licensing/certification requirements (e.g., continuing education mandates for special education teachers) legally or contractually require human attendance at professional development activities. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Certification and continuing-education requirements often mandate specific in-person or verified attendance for licensure renewal, creating strong institutional and regulatory barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task involves human time investment (salary/wages during attendance) and travel costs; AI incurs no meaningful cost but cannot fulfill the task's primary requirement of human professional development through live attendance and engagement. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI equivalent product performing this task, so no meaningful cost comparison exists; the human must attend regardless. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can substitute for or fully automate human attendance at live professional meetings, educational conferences, or workshops. The task inherently requires human presence, participation, and interpersonal engagement, which no current AI system handles autonomously. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product attends professional development events on a teacher's behalf; this is inherently a human attendance and participation activity. |
Provide additional instruction in vocational areas.
5CI 5–5 · exposure 0 · augmentation 50 · importance 3.8/5 · click for rater detail
Provide additional instruction in vocational areas.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Special education and vocational training remain heavily human-dependent sectors with low digitization. Adoption of AI in these settings has been minimal; schools continue relying on certified teachers, and vocational instruction particularly resists remote or purely automated approaches due to safety and skill-verification requirements. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education, especially vocational training, is a low-digitization, high-touch sector with minimal AI deployment in instructional delivery to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist instructors by generating supplementary materials, providing preliminary assessments, or creating practice scenarios, but the core task of teaching hands-on vocational skills with adaptive feedback and safety oversight remains fundamentally dependent on human expertise and presence. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help create vocational lesson plans, adapted materials, assessments, and individualized learning aids, providing meaningful but partial support to the teacher's instructional design work. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Vocational instruction requires hands-on demonstration, real-time feedback on student performance with adaptive adjustments, and development of practical motor and cognitive skills. Current AI cannot reliably guide students through physical trade work, equipment operation, or provide the individualized corrective feedback that vocational training demands. |
| Task automatability | claude-sonnet-5 | 1/5 | Vocational instruction for secondary special education students requires hands-on demonstration, physical guidance, safety supervision, and adaptive teaching for diverse disabilities, which current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: state certification requirements for special education teachers, liability concerns with students using equipment or performing potentially hazardous tasks, and mandatory in-person supervision for safety compliance in vocational settings. Educational regulations typically require licensed educators for special education service delivery. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education law (IEP requirements, certified teacher mandates) and the need for physical supervision and safety oversight create strong barriers against AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Vocational instruction requires expensive hardware (tools, equipment, workstations), real-time human supervision for safety, and individualized coaching. AI deployment would require significant infrastructure investment with ongoing human oversight costs, making it more expensive than traditional instruction. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for hands-on vocational teaching, so the comparison favors the human teacher entirely; any AI role would only supplement, not replace, at added rather than reduced cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system performs comprehensive vocational instruction for students with disabilities. While AI can deliver some supplementary content, it cannot replace the instructor's role in safety oversight, hands-on skill coaching, or assessment of competency in actual work tasks. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product delivers in-person vocational skill instruction with the individualized adaptations required for special-needs secondary students; this remains firmly outside current AI product capability. |
Instruct students in daily living skills required for independent maintenance and self-sufficiency, such as hygiene, safety, and food preparation.
5CI 0–10 · exposure 5 · augmentation 50 · importance 3.8/5 · click for rater detail
Instruct students in daily living skills required for independent maintenance and self-sufficiency, such as hygiene, safety, and food preparation.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Special education remains a heavily human-intensive, low-automation sector with strong regulatory and liability constraints; adoption of AI for core instruction is minimal, with any tools used only as supplements under teacher direction. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Special education, especially hands-on life-skills instruction, is a low-digitization, high-touch physical task area with minimal AI agent deployment in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist teachers by generating visual or written materials on daily living skills, creating personalized learning plans, or providing tracking of student progress, but the core work of demonstration, supervision, and behavioral engagement remains human-dependent. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can help create visual schedules, social stories, or personalized practice materials to support instruction, but the core teaching interaction remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Teaching daily living skills requires real-time interaction, physical demonstration, behavioral correction, and adaptive response to individual student needs and disabilities. Current AI systems cannot reliably substitute for the hands-on, relational, and safety-critical nature of this instruction. |
| Task automatability | claude-sonnet-5 | 1/5 | Teaching daily living skills to secondary students with disabilities requires hands-on demonstration, physical guidance, safety supervision, and real-time behavioral adaptation that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Special education instruction is legally mandated through IEPs (Individualized Education Programs), requires certified special education teachers, and involves duty of care and safety oversight that cannot be delegated to automated systems without direct human educator responsibility. |
| Adoption barriers | claude-sonnet-5 | 4/5 | IEP requirements, safety supervision duties, and legal obligations to provide individualized instruction by qualified special education staff create strong structural barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The loaded cost of a special education teacher (salary, benefits, training) is high, but supervision and liability in special education require human presence; AI tools offer only marginal cost savings for supplementary materials, not replacement of instruction. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the physical presence and supervision needed, so there is no comparable AI-driven cost alternative for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can provide supplementary video content or written guidance on hygiene and safety, deployed systems cannot independently instruct students, manage behavioral issues, ensure safety compliance, or adapt instruction to diverse special education needs in a classroom setting. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product independently instructs students in hygiene, safety, or food preparation skills; this remains firmly in the domain of human special education staff and aides. |
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 3.6/5 · 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 | Schools operate in heavily regulated, resource-constrained environments with strong union and professional norms around educator presence. Adoption of AI-driven safety monitoring in secondary classrooms remains minimal and faces entrenched resistance. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a low-digitization, high in-person-contact sector with minimal AI adoption for physical safety supervision tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by flagging unsafe patterns in recorded footage post-hoc or generating customized safety protocols, but most of the value lies in real-time, in-the-moment guidance—which remains primarily a human responsibility. Augmentation potential is limited. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help create instructional materials or safety checklists in advance, but it offers little real-time assistance during active physical supervision and equipment monitoring. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could draft safety guidelines and generate monitoring checklists, the task fundamentally requires real-time, in-person supervision and responsiveness to physical safety hazards—students must be actively watched and corrected by a human. AI cannot meaningfully replace the embodied judgment, attention, and intervention needed to prevent injuries. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires live physical supervision, real-time behavior management, and hands-on demonstration with students in a classroom, which current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal duty of care, liability for student injuries, mandatory in-person supervision requirements under special education law, and parental expectations create hard barriers. A licensed teacher must remain accountable and present; substitution is not legally or ethically permissible. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Safety supervision of students, especially those with special needs, involves legal duty-of-care, liability, and certification requirements that mandate a qualified human be physically present. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of camera systems, AI processing, liability insurance, and human oversight required to achieve any meaningful safety automation would exceed the loaded wage of a secondary special education teacher. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical supervisory task, so the comparison to human cost is moot—AI cannot deliver the output at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs real-time physical safety monitoring and in-person instruction in a classroom setting. AI monitoring systems exist for limited, controlled environments but cannot yet operate dependably in complex special education contexts with diverse student needs. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product provides in-person instruction and safety monitoring of students using physical equipment; this remains entirely a human physical-presence task. |
Meet with other professionals to discuss individual students' needs and progress.
4CI 0–7 · exposure 0 · augmentation 38 · importance 4.2/5 · click for rater detail
Meet with other professionals to discuss individual students' needs and progress.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task is inherently synchronous and interpersonal; there is no sector-wide pattern of automating professional meetings in education. Adoption of meeting-support tools (transcription, scheduling) is modest, and meaningful automation of the meeting itself does not occur in practice. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a moderately slow-adopting sector for AI, with pilots around administrative support but not for core collaborative meetings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist marginally with pre-meeting preparation (data synthesis, agenda drafting) or post-meeting documentation (transcript summarization, action-item extraction), but does not meaningfully augment the core meeting discussion itself, which demands live professional judgment and dialogue. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help prepare meeting agendas, summarize student data, draft progress notes, and generate follow-up documentation, meaningfully aiding preparation and follow-through even though the meeting itself remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Meeting with professionals requires nuanced interpersonal communication, consensus-building, and real-time adaptive discussion that current AI cannot perform end-to-end. While AI can summarize student data or draft meeting agendas, the core task—collaborative discussion and decision-making among human professionals—remains fundamentally human. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a live, interpersonal collaborative meeting requiring shared professional judgment, relationship-building, and real-time responsiveness that AI cannot conduct end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Special education contexts involve legal requirements (IEPs, FAPE mandates), fiduciary duty to students, and professional accountability that mandate human professionals meet and deliberate. Liability and regulatory frameworks explicitly require licensed educators and specialists to participate in these discussions; substitution is legally prohibited. |
| Adoption barriers | claude-sonnet-5 | 4/5 | IEP-related meetings involve legal/regulatory requirements (IDEA compliance), documentation with liability implications, and mandated participation of qualified professionals and parents, creating strong structural barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI has no meaningful cost advantage here because the task is synchronous human interaction; the core time cost is the professionals' presence, which AI cannot replace. Minimal AI support (note-taking, data retrieval) adds cost without reducing the human time investment. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that replaces the meeting itself, so cost comparison favors humans by default; any AI use is a minor supplement, not a substitute for the labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can reliably conduct or substantially replace professional meetings that require genuine collaborative dialogue, emotional intelligence, and accountability for decisions affecting student care. Meeting automation exists only at the data-preparation stage, not the meeting itself. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product runs or substitutes for multidisciplinary IEP/student-progress meetings; at most AI tools assist with notes or scheduling around the human-led meeting. |
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 4.0/5 · 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 | Public education remains a heavily analog, human-contact-intensive sector with slow IT adoption. Parent engagement is mandated and culturally relies on direct human relationships; there is no measurable trend toward AI-led or AI-mediated parent conferences. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a relatively low-digitization, human-contact-intensive sector with limited AI adoption for parent engagement specifically. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might assist with preparing summary documents, scheduling, or drafting agendas, but these are pre- or post-meeting tasks. During the actual conversation, AI augmentation is minimal because the core value is the educator's professional judgment, empathy, and accountability in real-time dialogue. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers prepare progress summaries, draft meeting notes, translate materials, or organize data beforehand, meaningfully aiding preparation even though the meeting itself remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires genuine interpersonal dialogue, emotional attunement, and collaborative problem-solving with parents/guardians about sensitive matters. Current AI cannot reliably conduct these nuanced conversations or make shared decisions about educational priorities and resource allocation in a way that meets the ≥50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires live interpersonal communication, relationship-building, and sensitive judgment about a child's needs that AI cannot conduct end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Federal law (IDEA) and state regulations require that educators conduct these conferences and that parents have meaningful voice in their children's IEPs. There is a hard legal and professional requirement for a licensed teacher to participate in these meetings. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education involves legal requirements (IEP meetings, parental consent, due process) that mandate qualified human educators and administrators engage directly with parents. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task requires a credentialed special education teacher ($50k–$70k+ loaded annual salary). Even if AI could assist with note-taking or scheduling, it cannot replace the meeting itself, making total cost per task-equivalent remain substantially higher than human performance. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this interaction, so the human cost is the only real option, making AI not cheaper but simply inapplicable as a replacement. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably handles this task end-to-end. Parent–teacher conferences demand real-time responsiveness to emotional context, trust-building, and legally/ethically sound documentation; AI systems today cannot substitute for a qualified human in production settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product independently conducts parent-teacher conferences or negotiates priorities for a student's IEP-related needs; this remains fully human-performed. |
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 38 · importance 3.5/5 · 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 | School districts remain highly conservative in human-contact roles and special education is a regulated, credential-driven sector with minimal AI displacement to date in direct parent counseling. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a moderately-digitized, relationship-heavy public sector with slow AI adoption for direct family engagement tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might assist with drafting resource lists or referral databases before a meeting, but the core task—real-time counseling and skill-teaching in a relationship context—depends almost entirely on human presence and judgment. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers prepare resource lists, draft talking points, translate materials, or summarize community resources, meaningfully aiding preparation even though the meeting itself remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires deep interpersonal engagement, emotional intelligence, and real-time adaptation to family circumstances. Current AI cannot authentically meet with and counsel parents on sensitive behavioral and disability matters in ways that would achieve equal outcomes. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires in-person relational trust-building, live interpretation of family dynamics, and adaptive counseling that current AI cannot perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and professional barriers exist: teacher licensure is legally required, liability falls on the certified educator, and parents expect and have the right to meet with a qualified human professional. Regulatory frameworks mandate human educator involvement. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education law (IDEA) and school policy generally require certified staff to engage directly with parents/guardians on IEP-related guidance, creating strong procedural and legal barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The loaded cost of a special education teacher conducting these meetings is substantially lower than any AI infrastructure plus necessary human oversight for such high-stakes family interactions. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the human meeting itself, so any AI cost is additive rather than replacing the teacher's wage for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system reliably performs parent-teacher counseling meetings independently. This requires real-time rapport-building, context-sensitive advice, and trusted human judgment that production systems do not yet demonstrate. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product conducts parent meetings to teach disability-management skills; this is a human interpersonal service, not a research-stage AI capability either. |
Attend staff meetings and serve on committees, as required.
4CI 0–7 · exposure 0 · augmentation 38 · importance 3.3/5 · click for rater detail
Attend staff meetings and serve on committees, as required.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | There is no adoption of AI for this task in any sector, as the task is fundamentally about human organizational membership and participation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderate-to-slow adopter of AI generally, and this specific interpersonal obligation is not being automated or displaced in any observable way. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist with preparation materials, agenda summaries, or note-taking during meetings, but the core task—being present and participating—remains entirely human-dependent. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help prepare meeting materials, summarize notes, draft agendas, or generate follow-up action items, offering moderate support around the edges of the task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Attending staff meetings and serving on committees inherently requires human presence, participation, and real-time deliberation. Current AI cannot meaningfully replace the collaborative, social, and decision-making aspects of these activities. |
| Task automatability | claude-sonnet-5 | 1/5 | Attending meetings and serving on committees requires physical/virtual presence, real-time judgment, and interpersonal engagement that AI cannot perform on a teacher's behalf. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Organizational policies, legal employment contracts, and professional standards explicitly require a licensed educator to attend meetings and serve on committees in their official capacity. A human must physically or synchronously participate. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Institutional and often contractual/policy requirements mandate that the actual staff member attend and participate, with accountability tied to the individual, not a delegate or tool. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of an AI system to simulate meeting attendance and committee participation would far exceed the loaded wage of the teacher, and would not fulfill the organizational requirement for actual human participation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this human presence-based task, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can substitute for human attendance at meetings or committee service. These activities require legal and organizational recognition of the participant as a responsible agent. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a teacher's active participation and representation in staff meetings or committee work. |
Monitor teachers and teacher assistants to ensure that they adhere to inclusive special education program requirements.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Monitor teachers and teacher assistants to ensure that they adhere to inclusive special education program requirements.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational institutions, particularly public schools, adopt monitoring and compliance technologies slowly due to union protections, privacy concerns, and preference for human judgment in personnel evaluation. Sectors where this task occurs have among the lowest automation adoption rates. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education administration is a low-digitization, highly regulated sector with minimal AI adoption for supervisory/compliance oversight roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by flagging video timestamps for supervisor review or organizing compliance documentation, but the core task of interpreting pedagogical quality and regulatory adherence requires experienced human judgment that AI augmentation would only marginally enhance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help track documentation, flag missing IEP paperwork, or summarize compliance records, but it cannot meaningfully assist with direct observational monitoring of staff practice. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time observation of classroom behavior, judgment about pedagogical methods, and understanding of complex regulatory compliance nuances. Current AI systems cannot reliably monitor human interactions or evaluate adherence to inclusive education standards without constant human oversight and interpretation. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires in-person observation, professional judgment, and interpersonal supervisory authority over staff, which current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task involves legal oversight of special education compliance and personnel evaluation, functions that typically require a licensed educator or administrator with formal authority to monitor and report. Regulatory requirements and institutional policy generally mandate human authority for such supervisory duties. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education compliance is governed by legal mandates (IDEA, IEPs) requiring qualified, credentialed personnel to oversee and certify adherence, creating strong regulatory and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI systems capable of video monitoring plus the required human expert review to validate compliance findings would exceed the salary cost of a special education coordinator or administrator doing this work directly. |
| 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 entirely. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can independently perform supervisory monitoring of teacher compliance with special education requirements. Video analysis systems exist but cannot reliably assess pedagogical adherence, inclusivity, or regulatory compliance without extensive human expert validation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs classroom monitoring and compliance evaluation of teaching staff for IEP/inclusion adherence; this remains a human supervisory function. |
Visit schools to tutor students with sensory impairments and to consult with teachers regarding students' special needs.
3CI 0–5 · exposure 0 · augmentation 38 · importance 2.8/5 · click for rater detail
Visit schools to tutor students with sensory impairments and to consult with teachers regarding students' special needs.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Special education remains a heavily human-dependent sector with minimal automation. Teachers are required by law to be credentialed; schools have not adopted AI for direct student tutoring or professional consultation roles in this context. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a highly regulated, in-person, low-digitization sector with minimal AI agent deployment for direct student services or physical school visits. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist with generating lesson plans, organizing student data, or drafting consultation notes, but it cannot augment the core task of in-person tutoring and direct professional consultation with sensory-impaired students. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers prepare materials, suggest accommodation strategies, or draft consultation notes, but it doesn't materially assist with the in-person tutoring or travel aspects of this task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires in-person physical presence at schools, direct sensory and adaptive interaction with students with sensory impairments, and nuanced consultation with teachers. Current AI systems cannot perform in-person tutoring, assess individual student needs through direct engagement, or conduct consultative meetings onsite. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physically traveling to schools, in-person tutoring of students with sensory impairments, and live consultation with teachers—none of which current AI can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task has substantial legal and regulatory barriers: special education law (IDEA) requires licensed special education teachers for IEP development and consultation; liability for student safety during in-person tutoring is high; and parental/institutional expectations require qualified human professionals working with vulnerable students. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Special education teaching requires licensure, direct human interaction with vulnerable students with disabilities, and legal/IEP compliance obligations that mandate qualified human professionals. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems cannot perform this task at all, making cost comparison moot. The human specialist's loaded wage is the actual cost; AI offers no equivalent alternative. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since AI cannot perform the physical travel and hands-on tutoring components, there is no viable AI substitute cost to compare; the human remains essential. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can perform school visits, tutor students with sensory impairments through direct interaction, or conduct in-person consultations with teachers. This task is fundamentally human-centric and requires physical presence. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product travels to physical locations, tutors students with sensory impairments in person, or conducts professional consultations with teachers on-site. |
Confer with parents or guardians, other teachers, counselors, and administrators to resolve students' behavioral and academic problems.
1CI 0–3 · 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.
1| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of AI to replace or conduct these conferences is negligible. Schools remain human-centric in special education conferencing due to regulatory requirements, liability concerns, and the belief that parent-teacher trust is essential. Pilot adoption is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 special education is a slow-adopting, highly regulated, relationship-driven sector with minimal AI penetration into interpersonal conferencing tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could modestly assist with background research on student progress data, summarizing prior interactions, or drafting follow-up documentation, but it cannot augment the live conference itself meaningfully. The core value—human dialogue and consensus-building—remains with the educator. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help prepare talking points, summarize student data, draft follow-up communications, or translate materials, aiding preparation though not the interaction itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires real-time interpersonal negotiation, trust-building, and judgment about sensitive family and student dynamics. Current AI cannot meaningfully participate in or conduct these conferences, as they demand genuine human empathy, contextual understanding of complex social situations, and legal/ethical accountability that AI systems cannot provide. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires live interpersonal negotiation, trust-building, and judgment about a specific child's needs, which current AI cannot conduct end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and regulatory barriers exist: special education law (IDEA, IEP requirements) mandates that qualified educators lead these conferences and that parents have meaningful input. Many jurisdictions require a licensed teacher or administrator to be present and accountable for decisions, creating hard legal substitution barriers. |
| Adoption barriers | claude-sonnet-5 | 5/5 | IEP/504 processes, special education law, and parental rights require certified educators and administrators to personally participate in these conferences. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task requires a licensed educator to be present and accountable; human labor cost is unavoidable. AI could assist with preparation or documentation but cannot substitute for the core conference itself, making the cost ratio unfavorable for automation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the human meeting itself, so there is no viable cost comparison—human presence is required. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs this task reliably. While AI can draft meeting notes or suggest talking points, it cannot actually confer, listen actively, mediate disputes, or build consensus among multiple stakeholders in a live interaction context. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product independently confers with parents and staff to resolve behavioral/academic issues; this remains a human relational function. |
Establish and enforce rules for behavior and policies and procedures to maintain order among students.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.6/5 · click for rater detail
Establish and enforce rules for behavior and policies and procedures to maintain order among students.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools operate in low-automation sectors with strong human-contact requirements and deep regulatory constraints. Adoption of AI for actual classroom discipline enforcement is negligible; schools remain staffed primarily with human teachers responsible for behavior management. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education is a low-digitization, high-human-contact sector with minimal adoption of AI for direct behavior management or enforcement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might assist by flagging attendance or early-warning behavioral patterns through data analysis, but it offers minimal augmentation to the core task of establishing rapport, explaining rules, and making judgment calls about enforcement that require human presence and authority. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help draft behavior plans, track incident data, or suggest policy language, but offers little real-time assistance for enforcing rules with students. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Establishing and enforcing behavior rules requires real-time judgment about student conduct, contextual understanding of motivations, and relationship-building—capacities that current AI systems cannot perform autonomously. This task fundamentally depends on human authority and interpersonal dynamics that AI cannot replace. |
| Task automatability | claude-sonnet-5 | 1/5 | Establishing and enforcing behavioral rules in real-time with students, especially those with special needs, requires physical presence, authority, and adaptive human judgment that AI cannot perform end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal and regulatory frameworks require licensed educators to maintain classroom order and discipline; school safety, liability, and in loco parentis responsibilities cannot be delegated to automated systems. Students have due-process rights that mandate human authority. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Legal, safety, and duty-of-care requirements mandate a certified, present human educator responsible for student supervision and discipline, especially in special education contexts with IEP compliance. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | An AI system would require continuous classroom monitoring, integration with school management systems, training on school-specific policies, and human oversight of every enforcement decision—making the all-in cost exceed the wage cost of an actual teacher managing behavior directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task alone, so cost comparison favors the human teacher who must be present regardless. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs classroom discipline and behavioral enforcement in production settings. While chatbots can provide guidance scripts, actual enforcement requires human judgment and presence that AI systems demonstrably do not possess at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product independently enforces classroom behavior policies; this remains a research-infeasible, human-only function in practice. |
Provide assistive devices, supportive technology, and assistance accessing facilities, such as restrooms.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.0/5 · 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 occurs in regulated school settings with strong human-contact requirements and limited digitization. Adoption of AI for physical assistance is negligible because the task's nature demands human presence. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Special education and physical caregiving in schools show minimal AI adoption for hands-on tasks; this is a low-digitization, high-touch physical care context. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist by managing schedules for assistive device loans or providing information about facility layouts, but the core task of physically providing devices and hands-on assistance cannot be substantially augmented by AI tools. |
| 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 physical assistance component itself is not augmented by AI tools. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical provision of devices, hands-on assistance in facilities, and real-time adaptive support for students with diverse disabilities. Current AI systems cannot physically manipulate objects, enter buildings, or provide in-person assistance, making end-to-end automation infeasible. |
| Task automatability | claude-sonnet-5 | 1/5 | This task involves hands-on physical assistance and setup of assistive devices in real time, which requires physical presence and manipulation that AI cannot perform. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Schools have strict duty-of-care and liability requirements for student safety; federal special education law (IDEA) mandates individualized in-person support. A licensed/trained human must provide direct physical assistance and supervision. |
| Adoption barriers | claude-sonnet-5 | 5/5 | This involves direct physical care of students, often minors with disabilities, requiring certified staff, safeguarding protocols, and legal duty-of-care obligations that mandate human involvement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task fundamentally requires human labor for physical assistance; AI has no comparative cost advantage since it cannot perform the core functions of device provision and facility access support. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for this physical caregiving task, so any AI cost comparison is moot; the human is the only viable option currently. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can physically provide assistive devices or accompany students to facilities. This requires human presence, dexterity, and judgment to respond to individual student needs in real time. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product provides physical assistance to students with disabilities accessing facilities or handling assistive devices; this remains entirely human-performed. |
Perform administrative duties, such as school library assistance, hall and cafeteria monitoring, and bus loading and unloading.
0CI 0–0 · exposure 0 · augmentation 13 · importance 3.1/5 · click for rater detail
Perform administrative duties, such as school library assistance, hall and cafeteria monitoring, and bus loading and unloading.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Education is a laggard sector for automation of student-facing duties due to liability, regulatory oversight of schools, and parental expectations for human supervision and intervention. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 school physical supervision and administrative duties are a low-digitization, in-person domain with essentially no AI adoption for these specific tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide modest assistance through automated attendance tracking or scheduling optimization, but the core supervisory and hands-on aspects (bus loading, hall monitoring) offer limited augmentation potential. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers negligible assistance for physically monitoring hallways, cafeterias, or bus loading, as these require real-time physical presence and judgment beyond current AI capability. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | These tasks require physical presence and real-time supervision of students in unstructured environments (halls, cafeterias, buses). AI cannot perform hall monitoring, bus loading, or library assistance in physical spaces today. |
| Task automatability | claude-sonnet-5 | 1/5 | These are physical presence and supervisory tasks (monitoring hallways, cafeterias, bus loading) that require a human body on-site to ensure student safety; AI cannot perform these end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal and safety responsibilities for student supervision create hard barriers: schools have duty-of-care obligations and cannot legally delegate live student monitoring to automated systems without licensed personnel present and accountable. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Student safety supervision, especially involving special education students and transportation, requires legally responsible, present adult staff; liability and safety regulations mandate human oversight. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying AI monitoring systems, cameras, and integration infrastructure would exceed the loaded wage of a teacher performing these duties part-time, especially given the supervision liability. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for physical monitoring, 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 performs student supervision, hall monitoring, or bus duties. These require human judgment, physical intervention capability, and legal responsibility that current AI systems cannot handle. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical student supervision or bus loading assistance; this remains entirely a human physical-presence task. |
Sponsor extracurricular activities, such as clubs, student organizations, and academic contests.
0CI 0–0 · exposure 0 · augmentation 25 · importance 2.9/5 · click for rater detail
Sponsor extracurricular activities, such as clubs, student organizations, and academic contests.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task is not a target for AI automation in schools; sponsorship requires continuous human presence, authority, and duty of care that organizations have no incentive or ability to delegate to AI. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially in-person student supervision roles, is a low-digitization, slow-adopting sector for AI substitution of this kind of task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist with administrative tasks like scheduling meetings or organizing contest rules, but it offers minimal meaningful augmentation to the core work of mentoring, leading, and inspiring students in clubs and activities. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with peripheral tasks like generating activity ideas, contest questions, or scheduling, but offers minimal help with the core supervisory/mentoring function. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Sponsoring extracurricular activities requires human judgment about student needs, organizational leadership, mentorship, and interpersonal presence—none of which current AI can perform end-to-end. AI cannot meaningfully supervise clubs, judge academic contests, or provide the motivational guidance essential to student organizations. |
| Task automatability | claude-sonnet-5 | 1/5 | Sponsoring clubs and student organizations requires physical presence, supervision, mentorship, and relationship-building with students that cannot be performed by AI end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Hard regulatory and institutional barriers exist: teachers must be present as legal sponsors and responsible adults, students expect human mentorship, and school liability frameworks require a named human adult accountable for the activity. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Supervision of minors in extracurricular settings requires an authorized, often background-checked staff member physically present, a hard legal/institutional barrier to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | There is no viable AI substitution, so cost comparison is moot; a human teacher sponsor is required by law and institutional policy, making this a fixed human cost. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this supervisory/mentorship role, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can reliably assume the role of activity sponsor, which demands continuous human authority, accountability, and presence. While AI might assist with scheduling or administrative logistics, it cannot fulfill the core function. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product supervises or leads extracurricular student activities in schools today; this remains entirely a human role. |
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.