Secondary School Teachers, Except Special and Career/Technical Education
25-2031.00Median wage $72,040/yr1,065,210 employed (US)Rank #588 of 923 scored · top 64% by substitution
Teach one or more subjects to students at the secondary school level.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution23
Exposure22
Augmentation56
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
32 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%21
panel mean rating 1.8/5 → substitution pressure 21/100
Technical feasibility todayw 20%23
panel mean rating 1.9/5 → substitution pressure 23/100
Cost vs. human wagew 15%26
panel mean rating 2.0/5 → substitution pressure 26/100
Adoption barriersw 20%inverted — strong barriers lower the score28
panel mean rating 3.9/5 (barrier strength) → substitution pressure 28/100
Sector adoption velocityw 10%19
panel mean rating 1.8/5 → substitution pressure 19/100
Task breakdown (32 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.
Assign and grade class work and homework.
64CI 51–76 · exposure 62 · augmentation 88 · importance 3.9/5 · click for rater detail
Assign and grade class work and homework.
64| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Schools are piloting AI grading tools and integration is accelerating in well-digitized districts and institutions using modern LMS platforms, but adoption remains uneven; many traditional schools still rely on manual grading, placing education in the 'middling' adoption range. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a comparatively slow-adopting sector due to budget constraints, teacher training gaps, and caution around instructional tools, despite growing pilot use of grading AI. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI grading systems substantially augment teacher productivity by providing instant feedback to students, flagging problematic responses for teacher review, and freeing hours per week for personalized instruction and student engagement, while teachers retain final authority over grades and pedagogical judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially speeds up grading and generates draft feedback, letting teachers focus on review and personalized comments rather than rote scoring. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can automatically grade objective assignments (multiple choice, short answers) and even provide detailed feedback on essays with reasonable accuracy. While subjective evaluation of open-ended work still benefits from human review, the time savings from automated grading and initial feedback easily exceed 50% for most class work and homework. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can grade objective and even many written assignments with rubric guidance, saving significant time, but nuanced feedback, plagiarism/context judgment, and alignment to specific lesson goals still need human oversight. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Schools control curriculum and assessment; teachers have professional autonomy over grading standards, and institutional inertia around pedagogy and trust in automated scoring creates friction. However, no legal barrier prevents AI-assisted grading, and many schools already permit or encourage it, though oversight remains customary. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human grade homework, though schools often require teacher review/final sign-off on grades, creating moderate institutional friction rather than hard legal barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | LMS and AI grading tools cost cents to a few dollars per student per term, while a secondary teacher's hourly cost for grading (loaded wage ~$30–50/hr, many hours spent grading) makes automated grading at least an order of magnitude cheaper per task-equivalent. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated grading tools cost a fraction of teacher time per assignment, especially for multiple-choice, short-answer, and even essay-type work with rubric-based AI scoring. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products like AI-powered gradebooks, essay-scoring systems (e.g., Turnitin's AI tools, Gradescope), and LMS-integrated grading assistants are in production use across many schools. These reliably handle routine grading, though teachers typically still review final grades and subjective assignments, reducing but not eliminating error rates. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Gradescope, Turnitin's AI feedback, and LLM-based grading assistants are deployed in real classrooms, but accuracy on open-ended responses and fairness concerns limit full autonomous use. |
Adapt teaching methods and instructional materials to meet students' varying needs and interests.
53CI 30–76 · exposure 58 · augmentation 88 · importance 4.2/5 · click for rater detail
Adapt teaching methods and instructional materials to meet students' varying needs and interests.
53| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Adaptive learning platforms exist in many schools, but adoption remains patchy and pilots are common; deep, sustained production use is still emerging. Digital education adoption accelerated post-2020, but full AI-driven adaptation is not yet standard practice sector-wide. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education adopts AI tools slowly and unevenly due to budget constraints, policy caution, and variable digitization, with pilots more common than deep production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI assistants significantly boost teacher productivity by generating personalized content, flagging student needs, and automating material customization while teachers retain curriculum and pedagogical judgment. This is among the highest-impact augmentation scenarios in education today. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist teachers by generating varied materials, suggesting differentiated activities, and analyzing student performance data, substantially boosting planning productivity while the teacher retains control. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | AI systems can generate differentiated instructional materials, adapt lesson plans to individual student profiles, and recommend learning pathways at scale with >50% time savings. LLMs and AI tutoring platforms already automate content personalization, assessment adaptation, and material modification. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help generate differentiated materials but cannot autonomously observe classroom dynamics, build relationships, or adjust in real-time to student needs, so it falls well short of full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Teachers remain responsible for classroom instruction and student outcomes, and many districts require human curriculum review. However, no legal mandate prohibits AI-assisted adaptation, creating moderate friction from professional norms and oversight requirements rather than hard regulatory barriers. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing barrier prevents AI-assisted material creation, but the interpersonal, in-person judgment required for real-time instructional adaptation creates practical friction against full substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven adaptation (platform subscriptions, LLM API calls, content generation) costs substantially less than paying teachers to manually differentiate for each student. Marginal cost per adaptation is orders of magnitude below teacher time at scale. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cheaply generate differentiated content, but the human teacher's ongoing observation, relationship-building, and real-time adjustment remain necessary, so overall cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (adaptive learning platforms, AI tutoring systems, content generation tools) demonstrably perform material adaptation and personalization in schools today. However, end-to-end implementation at classroom scale with full quality parity remains inconsistent across organizations. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Adaptive learning platforms and AI content generators exist and are used for material differentiation, but no deployed product reliably handles the full in-classroom adaptation process at scale. |
Establish clear objectives for all lessons, units, and projects, and communicate those objectives to students.
51CI 43–59 · exposure 42 · augmentation 75 · importance 3.9/5 · click for rater detail
Establish clear objectives for all lessons, units, and projects, and communicate those objectives to students.
51| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Many schools have adopted LMS platforms with templating, and some teachers use ChatGPT to draft objectives; however, systematic, widespread displacement of this task is limited by teacher familiarity with tools and institutional inertia. Adoption is growing but remains patchy across school sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a slower-adopting sector relative to information/finance industries, with piecemeal use of AI planning tools rather than systematic deployment across districts. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist teachers by generating candidate objectives, offering alternative wordings, and formatting them for communication to students, freeing teachers to focus on pedagogical judgment and refinement. This is a high-augmentation use case where AI handles routine drafting while the teacher retains control. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools substantially speed up drafting clear, standards-aligned objectives and rephrasing them for student-friendly communication, meaningfully aiding teachers who remain responsible for delivery and adaptation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can draft lesson objectives and communicate them in written form, but the task fundamentally requires teacher judgment about what students need, curriculum alignment, and calibrating clarity to a specific class—human oversight remains necessary. The communication step benefits from AI templating, but establishing educationally sound objectives demands human expertise. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft learning objectives aligned to standards for a given lesson topic quickly, but tailoring to specific class needs, curriculum sequencing, and actually communicating/enforcing objectives with students requires human judgment and presence. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Teachers must verify objectives align with curriculum standards and their pedagogical intent; school policy may require human sign-off on lesson plans. Administrative and professional norms create moderate friction, though no legal barrier explicitly forbids AI assistance with objective-setting. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier prevents AI-assisted objective drafting, though school curriculum policies and teacher certification norms mean a credentialed teacher remains formally responsible for what's taught. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-assisted objective-setting and communication is substantially cheaper than a teacher writing from scratch—a teacher might spend 30 minutes hand-drafting objectives, whereas an AI prompt and light revision takes 5–10 minutes. The per-task cost of inference and integration is negligible compared to teacher labor. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating draft objectives via AI tools costs pennies compared to teacher planning time, though the teacher must still review, adapt, and deliver them, limiting the full cost savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Learning management systems and AI writing assistants can generate draft objectives and distribute them, and some schools deploy these tools; however, they do not reliably produce contextually appropriate, educationally coherent objectives without substantial teacher revision. Production use exists but with meaningful limitations. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like curriculum-planning AI tools and lesson-plan generators exist and are used by teachers today, but they produce drafts requiring review rather than fully autonomous, reliable objective-setting integrated into classroom delivery. |
Prepare for assigned classes, and show written evidence of preparation upon request of immediate supervisors.
47CI 35–59 · exposure 38 · augmentation 88 · importance 3.4/5 · click for rater detail
Prepare for assigned classes, and show written evidence of preparation upon request of immediate supervisors.
47| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Public K-12 education has slower AI adoption than other sectors due to institutional inertia, budget constraints, and teacher skepticism, though some schools now use AI for lesson planning assistance. Adoption is accelerating but remains far from widespread production deployment in most districts. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | K-12 education is adopting AI tools for lesson planning and content generation at a moderate pace, with growing use but uneven institutional support and policy caution. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI tools demonstrably assist teachers by generating draft lesson plans, creating differentiated materials, and suggesting activities, which can reduce preparation time significantly while teachers retain judgment over suitability and customization. This is already happening in practice and meaningfully raises teacher productivity. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up drafting of lesson plans, worksheets, and supporting documentation, letting teachers focus review and customization while retaining ownership of final preparation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with generating lesson outlines, worksheets, and activity ideas, but preparing for a class requires curriculum alignment, pedagogical judgment, student assessment understanding, and classroom management planning that demand human expertise. Evidence of meaningful class preparation requires domain knowledge and accountability that goes beyond what current systems can do end-to-end. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft lesson plans, generate materials, and produce written documentation quickly, saving significant time on the drafting portion, but tailoring to specific students, curriculum standards, and classroom context still requires teacher judgment and review. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Schools and teachers retain strong professional and legal responsibility for pedagogical decisions and student outcomes. Institutional norms, accreditation requirements, and teacher licensing create barriers to full automation; supervisors and parents expect human teacher accountability for class preparation quality and appropriateness. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human write lesson plans personally, though administrators typically expect teacher ownership and accountability for instructional quality, creating some organizational expectation of authorship. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-assisted lesson planning and material generation are now substantially cheaper than paying a teacher to create these artifacts from scratch, particularly for routine content generation. Integration costs remain low with existing LLMs, making the cost-per-preparation significantly favorable compared to loaded teacher wages. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-assisted lesson plan drafting costs pennies per use compared to the teacher time saved, though the teacher still must review and finalize, so it doesn't reach full order-of-magnitude savings on the whole task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI tools (ChatGPT, Claude) can generate draft lesson materials and study guides, no deployed product reliably handles the full scope of class preparation with context-awareness of specific student cohorts, institutional curricula, and learning objectives. Current systems produce artifacts but lack the integrated understanding of student needs and classroom dynamics. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like lesson-planning assistants and generative AI tools are widely used by teachers today to draft plans and materials, but reliability varies and human review/editing is standard practice, not full automation. |
Prepare objectives and outlines for courses of study, following curriculum guidelines or requirements of states and schools.
46CI 32–59 · exposure 38 · augmentation 88 · importance 3.9/5 · click for rater detail
Prepare objectives and outlines for courses of study, following curriculum guidelines or requirements of states and schools.
46| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 education is a digitization laggard relative to information and financial sectors; curriculum design remains a core, highly localized human task; and adoption of AI for this specific task is in pilot phase with minimal production displacement. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | K-12 education is a middling-adoption sector; many teachers experiment with AI for lesson planning but institutional/district-wide deployment remains inconsistent. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can assist teachers by rapidly drafting outline sections, suggesting learning objectives aligned with standards, and offering alternative organizational structures, allowing teachers to refocus effort on pedagogical quality and customization rather than blank-page generation. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI is widely used today to substantially speed up drafting of objectives, outlines, and aligning them to standards, with teachers retaining final judgment and customization. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate draft course outlines and objectives quickly using curriculum guidelines, this task requires pedagogical judgment about learning progressions, student needs, and institutional context that AI cannot reliably handle end-to-end. The 50% time-saving threshold is difficult to meet when human review and revision is still substantial. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft objectives and outlines quickly given curriculum standards, but teachers must review, customize to student needs, and align with school-specific requirements, limiting full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teachers themselves must approve and take responsibility for curriculum design; school district curriculum committees often have formal authority over course structure; and state education boards set legal curriculum standards that require professional educator judgment to interpret and apply. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human write outlines, though schools often require teacher accountability and alignment with state standards, creating moderate oversight expectations. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | LLM-based systems cost pennies per outline draft, while a teacher's time (loaded salary ~$70–100k annually) is substantially more expensive; even with required teacher oversight, the economic advantage is significant. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating a course outline via AI costs a fraction of a cent to a few dollars in compute versus hours of teacher planning time, making it substantially cheaper even with review overhead. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI tools like ChatGPT can produce plausible course outlines, but no deployed product reliably generates complete, coherent, institution-specific course materials that meet state curriculum standards without significant human rework. Products exist for curriculum scaffolding but lack the accuracy and institutional fit needed for production deployment. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like ChatGPT, Khanmigo, and lesson-planning tools are used by teachers to generate curriculum outlines, but they still require significant human editing for accuracy and local compliance. |
Prepare, administer, and grade tests and assignments to evaluate students' progress.
45CI 43–48 · exposure 50 · augmentation 75 · importance 4.1/5 · 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 | Secondary schools are slow digitizers; most use basic LMS grading tools and manual evaluation. High teacher workload creates incentive, but adoption of AI-driven assessment remains in the pilot/voluntary phase across most districts, with limited displacement data in production systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderately slow-adopting sector due to budget constraints, policy caution, and uneven digitization, with pilots more common than full-scale production deployment of AI grading. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI augmentation is substantial and proven: auto-graded quizzes, detailed essay feedback engines, and real-time progress dashboards let teachers spend more time on instruction and individual student support rather than mechanical marking. Teachers retain full control and oversight while productivity on grading rises markedly. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI meaningfully speeds up test/assignment creation, generates rubrics, and provides first-pass grading suggestions, letting teachers focus on final judgment and feedback, which is a strong augmentation case even with human oversight required. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate significant portions: generating test questions, grading objective answers (multiple choice, short answer matching), and providing feedback on essays with ~70–80% accuracy. However, nuanced assessment of student understanding, contextual judgment on partial credit, and detection of cheating or plagiarism still require human oversight, so full end-to-end automation with equal quality falls short of the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft test questions, rubrics, and grade many assignment types (especially objective and short-answer) with significant time savings, but essay grading, alignment to curriculum standards, and handling edge cases still require substantial teacher oversight. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal and professional standards require a qualified, credentialed teacher to make final summative assessment decisions; unions protect grading as a core teaching function; parental and institutional expectations favor human judgment. Regulatory frameworks and liability asymmetry (error in assessment harms student record) create material friction against full delegation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing law mandates a human create every test, but grading (especially for grades/report cards) typically requires teacher sign-off, and parental/administrative trust in AI-generated assessments creates institutional friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | A teacher's grading time represents significant cost (~15–20% of a secondary teacher's workload). AI tools can reduce per-assignment grading cost by 30–50%, but full integration, oversight, and human spot-checking keep total cost roughly comparable to a fraction of teacher wages rather than dramatically cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools for generating and grading assessments are cheap per-use, but required human review, calibration, and integration into school systems narrows the cost advantage, especially given teacher salaries are already a fixed, non-marginal-cost commitment. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed products (e.g., Gradescope, turnitin, canvas auto-graders) handle parts reliably at scale, but none replace the full workflow end-to-end. Auto-grading works well for structured content; essay and open-ended evaluation remain error-prone and require human review, limiting production reliability for complete task substitution. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like AI grading assistants, quiz generators, and platforms (e.g., Gradescope, various LMS AI features) are deployed in schools, but reliability varies for open-ended responses and many teachers still manually verify or override AI grading. |
Prepare reports on students and activities as required by administration.
45CI 25–65 · exposure 45 · augmentation 63 · importance 3.5/5 · click for rater detail
Prepare reports on students and activities as required by administration.
45| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption remains slow; K–12 education is digitally lagging and risk-averse around student data automation, with most districts still relying on manual report-writing or basic templates rather than AI-driven systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a comparatively slow-adopting sector for AI tools due to budget constraints, policy caution, and data privacy rules around student information. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by drafting sections, summarizing data, and catching formatting errors, allowing teachers to focus on substantive assessment and comment refinement rather than rote transcription. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI writing assistants substantially speed up drafting of narrative comments and summarizing data trends, while teachers still verify and personalize final reports. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate draft text summaries of student performance data and activities, administrative reports require subjective judgment, contextual nuance, and verification against institutional policies that current systems struggle with reliably. The task includes discretionary elements (tone, emphasis, compliance) that prevent full end-to-end automation at the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 4/5 | Report drafting from structured data (grades, attendance, behavior notes) is a text-generation task that current LLMs handle well when given inputs, easily meeting the 50% time-saving bar for the drafting portion. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | High barriers exist: teachers have legal liability for report accuracy, FERPA and student data privacy regulations restrict automation scope, institutional governance typically requires a named educator to sign off, and many districts have explicit policies requiring human judgment on sensitive student assessments. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement bars AI-assisted reports, but teachers remain accountable for accuracy of statements about students, creating some liability and review friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current LLM-based report-drafting services remain moderately expensive relative to teacher hourly wages when accounting for integration, customization, and oversight required to ensure accuracy and institutional compliance. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating draft report narratives via AI costs a fraction of a cent per report compared to the teacher's time, though some human review time remains, keeping it just below the top tier. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably generates compliant administrative student reports at scale in production; existing tools offer templates and text suggestions but require substantial human review, fact-checking, and rewrites to meet legal and institutional standards. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like gradebook/LMS integrations and generative AI writing assistants exist and are used by teachers for report comments, but full end-to-end report preparation with accurate data pulls still requires manual review and varies by district system integration. |
Select, store, order, issue, and inventory classroom equipment, materials, and supplies.
43CI 34–52 · exposure 38 · augmentation 50 · importance 2.9/5 · click for rater detail
Select, store, order, issue, and inventory classroom equipment, materials, and supplies.
43| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | School districts adopt basic inventory management systems, but uptake remains patchy and often incomplete; most classroom-level supply management remains manual and performed by teachers rather than centralized AI-driven systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a relatively slow-adopting sector for back-office automation, with many schools still using manual or basic spreadsheet-based inventory processes. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Digital inventory tools and automated reordering alerts meaningfully assist teachers in tracking and ordering supplies, reducing manual stocktaking, though teachers retain final decisions on what is ordered and stored for classroom use. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-enabled inventory and procurement tools can help teachers track supplies and generate order lists, offering moderate productivity gains on the administrative portion of this task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While inventory tracking and ordering can be partially automated with current systems (e.g., barcode scanning, purchase order generation), the full task requires physical selection, storage decisions, and contextual judgment about classroom-specific needs that humans still predominantly perform. Current AI cannot reliably handle the spatial and decision-making components end-to-end. |
| Task automatability | claude-sonnet-5 | 3/5 | Inventory tracking and ordering logistics can be largely automated with inventory management software, though selection of appropriate educational materials still requires human judgment about curriculum needs.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Schools have budgetary approval workflows and purchasing procedures that create modest friction, but no legal requirement mandates human performance. Organizational inertia and preference for teacher oversight of classroom materials provide medium-strength barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a teacher personally perform inventory tasks, though budget authority and school procurement policies create some organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | Automated inventory systems cost moderately to implement and maintain, roughly comparable to teacher labor for this task when spread across a school or district, though specialized classroom logistics might favor human handling in smaller settings. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Software-based inventory systems are cheap to run but still require teacher time for physical handling, ordering decisions, and setup, keeping costs roughly comparable rather than dramatically lower. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Inventory management software exists and performs narrow tracking functions reliably, but deployed systems rarely handle classroom-level material selection or dynamic reordering without human oversight. No mature product fully automates all sub-components of this task in school settings. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Inventory and procurement software products exist and are used in schools, but they require setup and human input for selection decisions, and adoption for classroom-level supply management is uneven. |
Use computers, audio-visual aids, and other equipment and materials to supplement presentations.
41CI 30–52 · exposure 42 · augmentation 75 · importance 3.9/5 · click for rater detail
Use computers, audio-visual aids, and other equipment and materials to supplement presentations.
41| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | School systems adopt educational technology slowly due to budget constraints, teacher training requirements, and institutional resistance. Most adoption remains at the pilot or incremental enhancement level rather than wholesale displacement of teacher-led supplementation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education adopts new technology slowly due to budget constraints, training gaps, and institutional inertia, despite growing edtech interest. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist teachers by auto-generating presentation slide content, suggesting multimedia resources, organizing materials by topic, and adapting existing materials—substantially reducing preparation time while the teacher retains full control of pedagogical quality and classroom delivery. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially helps teachers create slides, visuals, and multimedia content faster, meaningfully boosting productivity while the teacher still delivers the lesson. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help generate or organize presentation content, the core task of integrating equipment and materials to supplement live teaching requires real-time classroom judgment, adaptation to student engagement, and troubleshooting. No current system performs this end-to-end with 50% time savings at equal pedagogical quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can generate slides, videos, and multimedia content to supplement lessons, but selecting, integrating, and delivering these tools in a live classroom still requires teacher judgment and presence. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teachers are legally and professionally accountable for curriculum content and student engagement, creating liability concerns. Schools maintain governance over technology use, digital equity requirements, and data privacy rules that inhibit rapid substitution of teacher judgment with automated systems. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI-generated supplementary materials, though school policies, curriculum standards, and appropriateness review create some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The integration, customization, and classroom-specific oversight required to use AI-generated or AI-assisted presentation materials often approaches or exceeds the cost of a teacher preparing materials themselves, especially for ongoing classroom use. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI content-creation tools are cheap relative to teacher prep time, but integration into classroom delivery and equipment costs keep overall savings moderate rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI products exist for content generation and basic presentation design, but deployed systems lack the contextual awareness and live adjustment needed to effectively supplement a teacher's dynamic classroom delivery. Current tools require significant human direction and oversight. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like AI slide generators, educational video tools, and interactive platforms are used by teachers today, but adoption is inconsistent and quality/reliability varies by subject and grade level. |
Maintain accurate and complete student records as required by laws, district policies, and administrative regulations.
37CI 30–45 · exposure 42 · augmentation 63 · importance 3.8/5 · click for rater detail
Maintain accurate and complete student records as required by laws, district policies, and administrative regulations.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Schools have adopted student information systems widely, but adoption focuses on data storage and basic retrieval rather than end-to-end automation of compliance-sensitive record maintenance. Most updates and policy-driven adjustments still require human teachers and administrators. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a relatively slow-adopting sector for AI-driven administrative automation, with pilots more common than widespread deployment for compliance-sensitive recordkeeping. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | SIS platforms help teachers organize, search, and generate reports on student records, improving productivity in routine documentation tasks. However, the assistance is limited to existing system features and does not substantially transform the human work of interpreting policies and ensuring compliance. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-enabled school management systems meaningfully speed up data entry, flag errors, and generate reports, significantly aiding teachers even though they must remain responsible for final accuracy. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While data entry and record organization can be partially automated, maintaining compliance with varying laws, district policies, and regulations requires human judgment and oversight. Current systems can assist with data input and formatting, but cannot independently ensure legal and administrative accuracy across heterogeneous policy environments. |
| Task automatability | claude-sonnet-5 | 3/5 | AI-integrated SIS/gradebook tools can automate data entry, attendance logging, and compliance formatting, but teachers must still verify accuracy, handle exceptions, and take legal responsibility for record correctness. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Multiple hard barriers exist: FERPA regulations legally restrict access to student records; state education codes mandate specific record-keeping practices; district policies often require human signatures and sign-off; liability for data breaches falls on institutions. Automated systems cannot replace the human authority required to certify record completeness and legal compliance. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Student records are governed by strict laws (e.g., FERPA), requiring accountable human oversight and signature/verification, creating substantial regulatory and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Student information systems have substantial licensing, integration, and maintenance costs. Teachers' relatively modest salaries mean that per-unit automation cost is often comparable to or exceeds the teacher time saved on routine data entry and filing. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Software-based record systems are cheap per-record, but integration, compliance auditing, and human verification of accuracy keep the effective cost comparable to teacher time rather than an order of magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | School management information systems exist and perform routine record-keeping tasks in production, but they require significant human setup, validation, and compliance checking. No deployed product fully automates the judgment-intensive aspects of policy-compliant record maintenance across diverse jurisdictions. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Deployed school information systems already automate much record-keeping and some AI features assist with data validation, but full end-to-end automation with legal compliance oversight is not standard in production. |
Administer standardized ability and achievement tests, and interpret results to determine students' strengths and needs.
36CI 34–39 · exposure 34 · augmentation 63 · importance 3.0/5 · click for rater detail
Administer standardized ability and achievement tests, and interpret results to determine students' strengths and needs.
36| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | School districts are digitizing test administration (online platforms) but remain cautious and fragmented in adopting AI interpretation; adoption is slower than in higher-profit sectors due to budget constraints, teacher skepticism, and regulatory/accountability concerns. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a slower-adopting sector for full automation of assessment administration, though data analytics tools for scoring/interpretation are seeing moderate pilot use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by auto-scoring, generating statistical dashboards, and flagging outlier performance patterns, which helps teachers focus interpretation effort, but the core judgment task remains largely human-driven and AI augmentation is useful on technical steps rather than transformative on the full task. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered analytics can rapidly aggregate test data, flag trends, and suggest interpretations, meaningfully speeding up how teachers analyze results even though final interpretation and instructional decisions remain human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can score standardized tests and generate basic statistical summaries, the interpretation step—determining individualized student strengths, learning needs, and pedagogical implications—requires contextual judgment about each student's circumstances, learning history, and educational goals that AI cannot reliably perform end-to-end today. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can score standardized tests and generate basic interpretive summaries, but the full task includes proctoring/administration and contextualized interpretation tied to classroom knowledge of students, which requires human presence and judgment.atability limited. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teachers are legally and professionally accountable for assessment interpretation and resulting instructional decisions; district policies, special education law (IDEA), and educational standards typically require licensed educators to own the diagnostic judgment and documented response, creating strong liability and authorization barriers. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Standardized testing often has procedural/legal requirements around proctoring, accommodations, and data privacy (FERPA), and interpretation for IEPs or interventions typically requires certified professional judgment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-assisted test scoring and initial analysis is cheaper than manual scoring, but the labor savings are offset by the need for teacher review, validation, and contextualized interpretation, keeping overall cost roughly comparable to human-only workflows. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated scoring and reporting tools are cheap per student, but administration logistics and required human oversight keep overall costs comparable to existing testing infrastructure costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist for automated test scoring and data visualization (e.g., learning management systems, assessment platforms), but interpretation and actionable recommendations remain largely template-based or require significant human oversight, limiting production reliability for the full task. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Test-scoring and analytics platforms (e.g., adaptive assessment tools, data dashboards) are widely deployed and reliably summarize results, but interpretation for individual student needs still generally involves teacher review. |
Prepare materials and classrooms for class activities.
30CI 25–35 · exposure 25 · augmentation 63 · importance 4.3/5 · click for rater detail
Prepare materials and classrooms for class activities.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Schools are slow to adopt automation; most classroom preparation remains manual and teacher-driven. While some districts use digital planning tools, adoption of AI-driven automation for this specific task is minimal and limited to material drafting in early-adopter institutions. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderate-to-slow adopting sector; while some teachers use AI for planning, widespread integrated adoption for full material/classroom prep is limited.: |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by generating material suggestions, proposing activity structures, or helping organize digital resources, raising teacher efficiency in planning phases. However, the physical and judgment-heavy aspects limit the scope of meaningful augmentation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially speeds up creation of worksheets, quizzes, slides, and activity materials, meaningfully boosting teacher productivity even though physical setup remains manual.: |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could help generate lesson material drafts or optimize classroom layouts digitally, the task fundamentally requires physical setup (arranging furniture, organizing supplies, testing equipment) and contextual judgment about specific student needs that AI cannot perform autonomously today. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help generate worksheets, slides, or lesson content, but physically arranging classrooms and preparing tangible materials requires human physical presence and cannot be automated end-to-end.: |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: teachers are legally required professionals, classroom preparation is intrinsic to their role, and institutional structures mandate human educators must set up and manage learning environments. Educational policy and accreditation also reinforce human responsibility. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier prevents using AI for material creation, though physical classroom setup inherently requires a human presence, creating a practical rather than regulatory barrier.: |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI systems plus integration for drafting or planning materials would likely exceed what a teacher spends on direct preparation time, especially considering the need for human oversight and physical work that cannot be automated. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools are cheap for generating content, but the physical labor of preparing classrooms still requires paid human time, keeping overall cost comparable to human-only preparation.: |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably handle the full scope of this task. AI can assist with material generation or provide suggestions, but physical classroom preparation and real-time adaptation to student populations remain human-dependent in practice. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like AI lesson-planning and content-generation tools exist and are used by teachers, but they only cover the digital material-creation portion, not the physical classroom setup.: |
Prepare and implement remedial programs for students requiring extra help.
29CI 25–34 · exposure 25 · augmentation 75 · importance 3.7/5 · click for rater detail
Prepare and implement remedial programs for students requiring extra help.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While EdTech adoption is growing, most secondary schools use AI tools peripherally (homework help, practice generation) rather than entrusting core remedial program design and delivery to AI; uptake remains pilot-stage in most districts due to teacher skepticism and institutional conservatism. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education adopts AI tools slowly and unevenly due to budget constraints, policy caution, and digital access gaps, with pilots more common than full-scale deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist teachers by rapidly generating differentiated practice sets, flagging at-risk students via learning analytics, and suggesting evidence-based intervention strategies, enabling teachers to focus on diagnosis, motivation, and adaptive in-person support rather than content creation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully help teachers by generating differentiated materials, practice sets, and progress tracking, augmenting their ability to design and adjust remedial support. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate generic remedial materials and suggest pedagogical approaches, the task requires personalized diagnosis of each student's learning gaps, adaptive sequencing based on real-time performance, and motivational adjustment—domains where current AI systems lack the reliability and contextual depth to operate end-to-end without substantial human oversight and customization. |
| Task automatability | claude-sonnet-5 | 2/5 | Designing remedial programs requires diagnosing individual student needs, adapting to classroom context, and ongoing relational teaching that AI cannot fully execute end-to-end, though AI can draft materials or suggest interventions.atical |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teachers are legally responsible for student outcomes and curriculum alignment; liability for inadequate remediation, regulatory curriculum standards, parental expectations for human judgment, and school-district procurement friction all create substantial friction against full automation of program design and implementation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement bars AI-assisted content, but IEP/504 compliance, parental expectations, and duty-of-care norms around struggling students create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-generated content and adaptive platforms have moderate per-student marginal cost, but full remedial program design and oversight still require human teacher labor; the all-in cost approaches parity with a teacher's hourly rate for this specific function rather than dramatic savings. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tutoring software has low marginal cost, but integrating it, training staff, and maintaining human oversight for actual remedial teaching still requires teacher time comparable to or exceeding software savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Several EdTech products offer adaptive practice modules and AI-generated worksheets, but deployed systems rarely diagnose root causes of academic struggle accurately or handle the social-emotional dimensions of remediation; most require teacher curation and cannot reliably replace the implementation design phase. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Adaptive learning platforms and tutoring tools exist and are used in some schools, but they handle narrow skill practice rather than the full design and implementation of a remedial program including in-person instruction and monitoring. |
Confer with other staff members to plan and schedule lessons promoting learning, following approved curricula.
28CI 25–30 · exposure 25 · augmentation 63 · importance 3.7/5 · 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 | Educational institutions are digitizing slowly compared to information or financial sectors; most schools still rely on traditional planning meetings and manual coordination. While lesson-planning AI tools are emerging in pilots, production-scale displacement of teacher conferencing remains rare. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a comparatively slow-adopting sector for AI in core planning workflows, with pilots more common than deep production use for collaborative curriculum work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by generating draft lesson plans, suggesting scheduling options, and organizing curriculum resources, helping teachers work more efficiently during planning sessions. However, the core collaborative and judgment-intensive aspects remain teacher-driven, so augmentation is meaningful but partial. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully speed up lesson drafting, alignment to standards, and generation of shared planning materials that teachers then discuss and refine together. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in generating lesson outlines and scheduling suggestions, the task requires substantial human judgment about pedagogical approach, student needs, and curriculum integration. Current AI cannot reliably replace the collaborative deliberation and decision-making inherent in conferring with colleagues, falling well short of 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft lesson plans and align them to curricula, but the actual interpersonal act of conferring with colleagues to negotiate scheduling and pedagogical approach requires human coordination and judgment that current systems cannot autonomously perform. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teachers operate within strong institutional and regulatory frameworks (curriculum mandates, school governance structures, professional standards) that require human educator sign-off on lessons and schedules. Organizational norms and legal accountability for educational quality create substantial friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement blocks AI assistance, but organizational norms, union contracts, and the need for teacher buy-in on curriculum decisions create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The human cost of teachers conferring with colleagues is embedded in their salaries; deploying AI for partial assistance (lesson drafting, scheduling) still requires teacher time for review, revision, and consensus-building. All-in cost of AI plus teacher oversight remains comparable to or exceeds the human effort alone. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI can cheaply generate draft materials, the collaborative planning meetings still require paid staff time, so overall cost savings are modest rather than transformative. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs the full task of conferring with staff to plan and schedule lessons. AI tools exist for lesson generation and calendar management separately, but none demonstrably handle the interpersonal coordination, curriculum alignment, and collaborative refinement that characterize this task in production educational settings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like lesson-planning assistants and scheduling tools exist, but no deployed system reliably conducts staff collaboration or consensus-building around curriculum sequencing at scale. |
Instruct through lectures, discussions, and demonstrations in one or more subjects, such as English, mathematics, or social studies.
25CI 25–25 · exposure 25 · augmentation 75 · importance 4.3/5 · click for rater detail
Instruct through lectures, discussions, and demonstrations in one or more subjects, such as English, mathematics, or social studies.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Most school districts remain slow to adopt AI for core instruction; adoption is largely limited to supplemental homework help and content generation tools. Production-scale replacement of teacher-led instruction is rare, with most investment in student-facing tutoring rather than classroom instruction. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a comparatively slow-adopting sector for full instructional automation, though supplementary AI tools are spreading in pilot programs. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI meaningfully assists teachers by generating lecture outlines, creating practice problems, suggesting explanations for difficult topics, and producing multimedia demonstrations. These tools raise teacher productivity in preparation and delivery while the teacher remains the primary instructor. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully help teachers prepare lecture materials, generate examples, create discussion prompts, and personalize content, significantly aiding lesson delivery even though the human still leads instruction. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate lecture content and demonstrations at scale, delivering instruction requires real-time classroom management, student engagement calibration, and adaptive responsiveness to live questions—tasks current systems perform poorly. No end-to-end system achieves 50% time savings at equal pedagogical quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Live classroom instruction requires real-time interaction, classroom management, and adaptive discussion with adolescents, which current AI cannot fully replicate end-to-end despite being able to generate lecture content or explanations. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teaching is heavily regulated, often requires state certification, and faces strong organizational and parental expectation for human in-person instruction. Schools face legal and governance constraints around unsupervised AI instruction, and cultural barriers to removing human contact are substantial. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Teaching licensure, in-person supervision requirements, child safety regulations, and parental/institutional expectations create strong barriers to full AI substitution in the classroom. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | High-quality AI content generation and video hosting have modest per-student costs, but integration with existing curricula, oversight for accuracy, and the need for human instructors to moderate and supplement consume most cost savings. Full replacement economics remain unfavorable. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Even if AI could generate content cheaply, actual delivery still requires a human physically present with students, so realized cost savings from AI alone are limited for this task as stated. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI can produce draft lecture notes and visual aids; some platforms offer recorded video instruction. However, no deployed product reliably replaces a teacher's live instructional delivery, in-the-moment question handling, and classroom presence at acceptable quality in production school settings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tutoring products exist and can deliver explanations or practice, but no deployed product independently runs live classroom lectures/discussions with a group of students at scale. |
Plan and supervise class projects, field trips, visits by guest speakers, or other experiential activities, and guide students in learning from those activities.
19CI 16–21 · exposure 20 · augmentation 50 · importance 3.3/5 · 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.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Schools are relatively laggard in adopting automation; teaching remains largely human-centered with budgetary constraints and cultural resistance to outsourcing student interaction. Digital planning tools see adoption, but replacement of supervision is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education adopts AI slowly for logistics-heavy, in-person activities; while lesson-planning tools spread quickly, supervision of physical activities sees minimal AI adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist teachers by generating activity plans, providing learning prompts, managing logistics, and creating reflection materials, which meaningfully reduces prep work and can enrich guidance. However, the core supervision and interpersonal aspects limit how transformative AI can be. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can meaningfully assist in planning itineraries, drafting permission forms, generating discussion questions, or suggesting guest speaker topics, improving efficiency of the planning phase. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with logistical planning (scheduling, checklists, activity coordination) and generate activity ideas, but cannot supervise students in real time, manage behavioral/safety dynamics, or guide learning through Socratic dialogue. The irreducibly human elements—presence, judgment, relationship—prevent >50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help brainstorm project ideas or draft logistics plans, but the core task involves physical supervision, coordinating with venues/speakers, and real-time guidance of students, which cannot be automated end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal/institutional liability for student safety during field trips and experiential activities falls on a responsible human educator; regulations typically require credentialed staff presence and duty of care that cannot be delegated to AI. Parental expectation and school policy strongly reinforce human supervision. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Legal duty-of-care, safety liability, and school policy require a certified teacher's physical presence and judgment during trips and activities, creating strong structural barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The human-contact and judgment-intensive nature of this task means AI cost savings are limited to the planning phase; the bulk of the work (supervision, guidance, adaptation) remains human-delivered, making the all-in cost ratio unfavorable for automation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for on-site supervision and guest speaker coordination, so the human cost is irreducible and AI offers no cost substitution for this portion of the task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Products exist for activity planning and content generation, but no deployed system performs end-to-end supervision, real-time student guidance, or safety management. Classroom management remains a core barrier where AI cannot reliably replace human judgment and presence. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product supervises field trips or manages in-person experiential learning; this remains entirely outside current AI product capability. |
Guide and counsel students with adjustments, academic problems, or special academic interests.
16CI 7–25 · exposure 13 · augmentation 50 · importance 3.9/5 · click for rater detail
Guide and counsel students with adjustments, academic problems, or special academic interests.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Schools remain institutional laggards in AI adoption; guidance and counseling are core educator roles, and districts have not meaningfully shifted this work to AI systems in production. Pilot programs exist but deployment is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a slower-adopting sector for AI in interpersonal counseling roles, with adoption concentrated in administrative and content-support tools rather than student guidance itself. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can helpfully surface student data, suggest study resources, and flag patterns of academic struggle, assisting teachers in prioritizing and preparing for student conversations. However, the augmentation is narrow—AI handles information assembly, not the core counseling work. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers by flagging academic performance trends, suggesting resources for struggling students, or drafting communication, but the core counseling interaction remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can provide generic academic advice and identify patterns in student performance data, guiding and counseling students requires sustained relationship-building, reading emotional and social context, and adapting to individual circumstances in real time—tasks that remain fundamentally human. Current systems cannot reliably replace this interpersonal diagnostic and adaptive role. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires reading emotional cues, building trust, and providing personalized human mentorship for adolescents, which current AI cannot perform end-to-end with equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Schools and parents expect human teachers and counselors to provide guidance on sensitive matters; liability concerns around AI-driven academic or personal advice are substantial, and many jurisdictions legally require credentialed educators to assess special needs and adjustments. Regulatory and organizational friction is high. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Schools have duty-of-care, safeguarding, and legal obligations requiring certified staff to handle student welfare and academic counseling, creating strong institutional and regulatory barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | An AI system for academic counseling would require significant setup (integration with student records, content curation, oversight workflows) and ongoing human supervision to ensure safety and appropriateness, making total cost per student outcome comparable to or higher than teacher time. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since AI cannot reliably perform this relational counseling task at all, there is no valid cost comparison—human labor remains the only viable option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Chatbots and tutoring systems exist to provide study suggestions and flag academic issues, but no deployed product reliably performs the full counseling and guidance function—which demands trust, privacy sensitivity, and nuanced judgment about student welfare. Production systems typically assist rather than own this task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a teacher's in-person counseling role with students on personal or academic adjustment issues; existing chatbot tutors address narrow academic content, not holistic guidance. |
Collaborate with other teachers and administrators in the development, evaluation, and revision of secondary school programs.
16CI 7–25 · exposure 13 · augmentation 50 · importance 3.4/5 · click for rater detail
Collaborate with other teachers and administrators in the development, evaluation, and revision of secondary school programs.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 education is a laggard sector in AI adoption; most schools lack infrastructure for agent-based workflows, and adoption of AI in curriculum development remains pilot-stage with limited production deployment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderate-to-slow adopter of AI for core governance and collaborative processes, with most AI use concentrated in content creation or administrative support rather than collaborative decision-making. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by summarizing feedback, generating draft language for program revisions, and organizing data for comparative analysis, thus raising efficiency in parts of the collaborative process while educators retain decision authority. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist by summarizing data, drafting curriculum proposals, analyzing assessment results, or preparing materials that inform the collaborative discussion, meaningfully aiding but not replacing the human collaboration. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in synthesizing curriculum data and drafting program evaluations, the core task—collaborative development requiring nuanced judgment, stakeholder alignment, and institutional knowledge—fundamentally depends on human deliberation and consensus-building that AI cannot orchestrate end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is an interpersonal, deliberative, consensus-building task among colleagues that requires in-person collaboration, institutional knowledge, and negotiation—AI cannot substitute for the human participation itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory and institutional barriers are substantial: educators typically require specialized credentials, school boards and administrations retain formal decision authority, and curriculum revision often involves legal compliance and accreditation standards that mandate human professional judgment and accountability. |
| Adoption barriers | claude-sonnet-5 | 4/5 | School governance, accreditation, and curriculum approval processes typically require certified educators and administrators to participate and sign off, creating strong institutional and sometimes regulatory barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted document generation and analysis may reduce some clerical effort, but the task's core—facilitated collaboration among educators—requires human time that AI does not meaningfully displace, making the cost ratio unfavorable for automation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system replacing this task, so cost comparison favors humans entirely; the task is inherently a human group process. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably handles the multi-stakeholder negotiation and institutional context required for school program development; AI tools exist for document drafting and analysis but not for the collaborative decision-making that defines this task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs collaborative program development and revision among school staff; AI tools at best support drafting inputs to such meetings, not the collaborative act itself. |
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 3.9/5 · 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 | K–12 education is a laggard sector for labor-replacing automation: schools are resource-constrained, risk-averse, unionized in many regions, and constrained by regulations and parental expectations that a human teacher lead instruction. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderately slow-adopting sector for AI in core instructional delivery, with pilots and tool use for planning/support more common than transformation of classroom teaching itself. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist teachers by generating activity ideas, proposing demonstration scripts, and organizing lesson structures, raising planning efficiency. However, the live execution and responsiveness aspects remain human-driven, limiting transformative potential. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI is widely useful for helping teachers design differentiated activities, generate discussion questions, and create investigative materials, meaningfully boosting planning productivity while the teacher still delivers instruction. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires live, real-time classroom management, interactive questioning, and responsiveness to student needs—all contextual judgments that demand human presence and pedagogical expertise. AI cannot conduct the in-person activities, observe student reactions, or adapt on-the-fly. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft lesson plans and activities, but conducting live instruction, managing classroom dynamics, and adapting in real time to student needs requires human presence and judgment that current AI cannot replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Jurisdictions legally require licensed, credentialed teachers to plan and conduct instruction; liability and duty-of-care frameworks assign accountability to a human professional. No regulatory path permits a school to substitute AI for classroom instruction. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Teaching in accredited secondary schools requires licensed, certified educators for legal, safeguarding, and accreditation reasons, creating strong structural barriers to full automation of instruction delivery. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI systems that could generate and organize lesson content, plus the human teacher oversight required to execute it safely and effectively, far exceeds the cost of a teacher doing this task directly. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools are cheap for generating draft plans, but the in-person conducting and facilitation component still requires a full-time human teacher, so overall cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can draft lesson plans and suggest activities, no deployed system reliably plans and conducts a balanced program of live classroom instruction with differentiated demonstration and work supervision at scale. Pilots exist but production deployment in actual K–12 classrooms remains minimal. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like lesson-planning assistants and content generators exist and are used by teachers, but no deployed system independently plans and conducts full classroom activities reliably. |
Observe and evaluate students' performance, behavior, social development, and physical health.
13CI 0–25 · exposure 13 · augmentation 38 · importance 3.9/5 · click for rater detail
Observe and evaluate students' performance, behavior, social development, and physical health.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Schools remain slow adopters of AI-driven surveillance or evaluation systems due to privacy concerns, trust preferences for human educators, and limited budget for technology integration—pilots exist but production deployment remains rare in most districts. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a low-digitization sector with slow AI adoption for core pedagogical and student-welfare judgment tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist teachers by organizing and highlighting patterns in attendance, submitted work, or classroom behavior data, helping them prioritize observations and investigations, though the core professional judgment remains with the teacher. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help log observations, flag attendance/grade anomalies, or organize behavioral notes, but it contributes minimally to the core observational and evaluative judgment involved. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with analyzing recorded classroom data and flagging behavioral patterns, but comprehensive evaluation of social development, physical health, and nuanced performance requires direct observation and human professional judgment that current systems cannot reliably replicate end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires in-person, real-time observation of students' behavior, social cues, physical health signs, and interpersonal dynamics in a classroom, which AI cannot perceive or judge holistically today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: teachers have professional licensing requirements, legal duty of care, parental trust expectations, and student privacy protections (FERPA) that create liability asymmetry; schools face organizational inertia around human professional judgment in safeguarding decisions. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Child welfare, safeguarding duties, and legal/professional responsibilities require a certified teacher's direct judgment and accountability, making this a hard-barrier task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of AI monitoring systems, infrastructure setup, and required human oversight to verify and act on AI outputs would be comparable to or exceed the cost of direct teacher observation and evaluation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this observational task, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some narrowly scoped products exist for attendance tracking and basic behavioral flagging via video analytics, but no deployed systems reliably evaluate the full scope of student performance, social development, and physical health with the sensitivity required in educational settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product observes and evaluates live student behavior, social development, and physical health in classrooms; this remains outside current AI product scope. |
Perform administrative duties, such as school library assistance, hall and cafeteria monitoring, and bus loading and unloading.
9CI 5–14 · exposure 8 · augmentation 13 · importance 2.8/5 · click for rater detail
Perform administrative duties, such as school library assistance, hall and cafeteria monitoring, and bus loading and unloading.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools remain low-digitization, conservative environments with strong in-person supervision mandates and limited AI adoption for core operations. Adoption of AI for student monitoring duties remains minimal and faces cultural and legal resistance. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education administrative/physical supervision tasks show essentially no AI adoption, as this sector lags in deploying AI for physical-presence duties. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could modestly assist with documentation of incidents or scheduling optimization, but these are peripheral to the core supervision task. The augmentation benefit is limited because real-time human judgment and presence are central to the work itself. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers negligible assistance for physically monitoring hallways, cafeterias, or bus loading, as these require direct human observation and intervention. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | These are primarily physical and in-person supervision tasks (hall monitoring, cafeteria duty, bus loading) that require real-time judgment and presence. While some administrative components (attendance, reporting) could be partially automated, the physical supervision and human presence requirements mean current AI cannot perform the full task end-to-end with 50% time saving. |
| Task automatability | claude-sonnet-5 | 1/5 | These duties require physical presence to supervise students in hallways, cafeterias, libraries, and at bus loading zones, which AI systems cannot perform.4/5 They involve real-world physical monitoring and safety oversight, not information processing. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Schools have statutory duties to provide supervision of students for safety and welfare, creating legal requirements that a human (typically a teacher or monitor) must physically be present. This creates a hard barrier to substitution; the task cannot be fully offloaded to AI regardless of capability. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Schools have strong duty-of-care and safety/liability obligations requiring an authorized adult physically present to supervise students, creating a hard barrier against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The minimal automatable components (administrative logging, simple data entry) are small parts of the task. Full substitution is not feasible, and partial automation of paperwork would be comparable or more expensive than the teacher's existing time allocation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical supervisory function, so no meaningful cost comparison exists; the human cost is the only viable option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs in-person hall, cafeteria, or bus monitoring at scale. While attendance systems exist, the core duties require human presence and real-time intervention capacity that current AI cannot deliver in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs in-person student supervision, hall monitoring, or bus loading assistance; this remains purely a human physical presence task. |
Attend professional meetings, educational conferences, and teacher training workshops to maintain and improve professional competence.
9CI 5–13 · exposure 5 · augmentation 50 · importance 3.4/5 · 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 deeply embedded in institutional and human-contact requirements; no meaningful adoption of AI replacement in this context is occurring. Teacher training remains a human-centric, legally and professionally mandated activity. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderate-to-slow adopter of AI overall, and this specific task (attending PD events) has seen little to no AI-driven disruption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by summarizing conference proceedings, flagging relevant sessions, generating reflective notes, or curating follow-up resources post-attendance. These supports enhance preparation and retention but require the teacher to remain the active participant. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers find relevant conferences, summarize workshop content, generate notes, or suggest follow-up resources, but it doesn't change the core in-person/participatory nature of the task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Attending meetings and conferences is inherently a human-presence activity requiring real-time participation, interaction, and social engagement. AI cannot meaningfully substitute for the core function of being present and learning through professional discourse. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical or live virtual attendance, networking, and active professional participation, none of which AI can perform on a teacher's behalf.It cannot be delegated to an AI system. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Professional development and conference attendance are often mandated by school boards, state certification bodies, and employment contracts. Many districts require documented in-person or real-time participation for compliance, creating regulatory and contractual barriers to substitution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Professional development often requires certification credit, licensure renewal, and verified attendance tied to the individual teacher, creating strong institutional and regulatory barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task inherently involves human time investment (travel, registration fees, hours of attendance). AI cannot reduce these costs; sending an AI agent would be an addition, not a substitution, making the cost ratio unfavorable. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI equivalent performing this task, so no cost comparison favors AI; the human must still attend and participate. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can summarize conference materials or generate meeting notes post-hoc, no deployed product reliably attends meetings, participates in discussions, or certifies professional development on behalf of a teacher. Some partial solutions exist but fall far short of the full task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product attends conferences or training workshops as a substitute for a human teacher; this remains entirely a human activity. |
Prepare students for later grades by encouraging them to explore learning opportunities and to persevere with challenging tasks.
8CI 0–16 · exposure 5 · augmentation 63 · 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.
8| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools remain highly conservative in delegating motivational and pastoral responsibilities; there is no evidence of meaningful adoption of AI systems to replace teacher encouragement of student persistence. Sector digitization is slow, and pedagogical change resistant. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderate-to-slow adopter of AI, with pilots for tutoring and content generation but limited integration into core motivational/mentorship roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by identifying at-risk students, suggesting strategies based on learning data, or providing teachers with insights into common persistence barriers—useful augmentation—but the human teacher must remain the primary source of encouragement and role modeling. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can support teachers by personalizing practice problems, tracking progress, and offering adaptive challenges that help sustain student engagement and effort. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires sustained interpersonal influence, motivational judgment tailored to individual students, and adaptive encouragement—elements that demand human emotional intelligence and contextual understanding that current AI systems cannot replicate end-to-end. While AI might generate generic motivational prompts, genuinely encouraging persistence in a student facing a specific academic or social challenge requires human presence and credibility. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires ongoing relational motivation, mentorship, and in-person encouragement tailored to individual students, which current AI cannot deliver end-to-end in place of a teacher. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong regulatory and professional licensing barriers apply: state teaching credentials, mandatory classroom presence, and legal duty of care require a licensed educator to directly engage students in encouragement and academic guidance. This is not a task that can be legally offloaded to an automated system without teacher sign-off. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Teaching credentialing, in-person supervision requirements, and parental/institutional expectations of human mentorship create strong structural barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A teacher's motivational presence and one-on-one encouragement cannot yet be economically replaced by AI; any AI system would still require teacher oversight and would not reduce the teacher's core time commitment on this task, making it more expensive than the current human-only approach. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI tutoring tools are cheap per interaction, the actual task of fostering perseverance and motivation still requires human teacher time, so cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Current products can generate motivational text or suggest strategies, but no deployed system reliably performs the core task of identifying which students need encouragement, assessing their specific barriers, and delivering personalized persistence coaching at classroom or individual scale. AI tutoring systems show promise in narrow domains but lack the adaptive, relational capability this task demands. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously builds student perseverance and long-term learning dispositions; this remains a human relational and pedagogical function. |
Meet with other professionals to discuss individual students' needs and progress.
4CI 0–7 · exposure 0 · augmentation 38 · importance 3.6/5 · 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 | Schools operate in a conservative, highly regulated environment with strong preferences for direct professional collaboration and minimal digitization of core instructional decisions. Adoption of AI for this task is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education adopts AI tools slowly for administrative support, but collaborative case discussions remain almost entirely untouched by automation initiatives. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might help prepare summary data or draft talking points before meetings, but the core task—deliberating in real time with peers about individual students—requires human presence and adds limited assistive value. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by summarizing student data, generating progress reports, or organizing discussion notes beforehand, aiding meeting preparation and follow-up documentation. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires nuanced professional judgment, interpersonal understanding, and collaborative dialogue about individual students' complex, contextual needs. Current AI cannot meaningfully participate in or replace these meetings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is an interpersonal, real-time collaborative discussion requiring human judgment, relationship context, and presence; AI cannot conduct these meetings end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Educational decisions about students involve legal accountability, duty of care, and professional licensure requirements. Licensed educators must participate in these meetings; there is a hard barrier to substitution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Discussions about individual students often involve confidential records (FERPA-protected), professional judgment, and institutional norms requiring qualified staff to participate directly. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task hinges on human professional expertise and real-time interpersonal negotiation; AI systems offer no cost advantage over a trained teacher's participation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI product performing this task, so no meaningful cost comparison exists; the human meeting is the only viable option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably conducts meetings with human professionals to discuss and decide on student interventions. Chatbots lack the contextual depth, rapport, and accountability required. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for teachers meeting colleagues, counselors, or specialists to discuss student needs; this remains purely human interaction. |
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 3.5/5 · click for rater detail
Meet with parents and guardians to discuss their children's progress and to determine priorities for their children and their resource needs.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools show no meaningful adoption of AI for replacing parent-teacher meetings. The practice remains firmly embedded in teacher job descriptions and regulatory requirements across districts, with no evidence of automation-driven displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education adopts AI slowly for high-touch relational tasks; adoption is largely limited to administrative supports rather than replacing parent engagement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could modestly assist by pre-populating student performance summaries, organizing attendance or assignment data, or transcribing notes, but the core task of dialogue and relationship-building is entirely human-dependent, limiting meaningful augmentation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers prepare progress summaries, draft talking points, and analyze student data ahead of meetings, meaningfully aiding preparation while the human conducts the actual conversation. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires human-to-human relationship building, emotional intelligence, and personalized conversation about sensitive child development topics. AI cannot meaningfully replace the interactive dialogue, trust-building, and contextual judgment that parents expect and that effectiveness depends on. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires live, relational, emotionally-sensitive interpersonal interaction with parents that current AI cannot conduct end-to-end; no off-the-shelf system meets the 50% time-saving-at-equal-quality bar. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and professional barriers exist: teachers are expected and often contractually required to conduct parent conferences as part of their role; parents expect and have rights to speak directly with the educator; and accountability for student progress involves human professional judgment that cannot be delegated to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Strong organizational and professional norms require a licensed teacher to personally engage with parents; trust, accountability, and legal/professional responsibility for student welfare create high barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot perform this task end-to-end, so a direct cost comparison is not meaningful. Any AI tool that might assist (transcription, summarization) still requires the teacher to conduct the actual meeting, adding cost rather than reducing it. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since AI cannot perform the actual meeting and judgment-based discussion, the human cost is unavoidable and AI adds at most marginal prep-time savings, not a substitute cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably conducts parent-teacher conferences autonomously. While AI can draft messages or summarize records, actually meeting with parents to discuss their child's progress and establish collaborative priorities remains a human-only practice in all educational settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product conducts parent-teacher conferences or negotiates priorities and resource needs; this remains firmly in human hands with only scheduling/notification tools automated. |
Confer with parents or guardians, other teachers, counselors, and administrators to resolve students' behavioral and academic problems.
3CI 0–5 · exposure 0 · augmentation 38 · importance 3.7/5 · click for rater detail
Confer with parents or guardians, other teachers, counselors, and administrators to resolve students' behavioral and academic problems.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational institutions remain highly resistant to automation of student-family communication and conflict resolution due to duty-of-care obligations, cultural expectations, and regulatory environment. Adoption is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a slow-adopting sector for AI in interpersonal, relational tasks, with pilots focused on administrative support rather than replacing human conferencing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AI could assist by preparing notes on student history or suggesting talking points beforehand, it cannot augment the core task of live conferencing and negotiation, where human judgment and relationship-building are essential to resolution. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers prepare talking points, summarize academic records, or draft follow-up communications, improving efficiency without replacing the human interaction itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time interpersonal negotiation, emotional intelligence, and judgment about sensitive behavioral and academic issues involving multiple stakeholders. Current AI cannot independently conduct these nuanced conferences or resolve conflicts that demand human accountability and trust. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time interpersonal negotiation, emotional judgment, and relationship-building with parents and colleagues that cannot be delegated to AI end-to-end today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong barriers exist: teachers and administrators have professional and legal responsibility for student welfare; parents expect and often legally require human-to-human communication on sensitive matters; schools face liability for delegating such conversations to non-human agents. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Legal, ethical, and institutional norms require a credentialed teacher or school official to engage directly with parents/guardians on student welfare matters, creating strong barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The human teacher performing this task brings irreplaceable credibility, legal accountability, and interpersonal rapport. Any AI system would require substantial human oversight and would not reduce the total cost of the interaction. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this conferencing task, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can reliably conduct parent-teacher conferences, mediate between administrators and teachers, or navigate the social complexity of resolving student behavioral problems. This requires human judgment and presence in educational and institutional contexts. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product conducts these sensitive multi-party conferences autonomously; at most AI could draft notes or summaries beforehand. |
Instruct and monitor students in the use of equipment and materials to prevent injuries and damage.
3CI 0–5 · exposure 5 · augmentation 38 · importance 3.4/5 · click for rater detail
Instruct and monitor students in the use of equipment and materials to prevent injuries and damage.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools operate in highly regulated, risk-averse environments with strong legal exposure for automation failures. Adoption of AI for safety supervision is negligible, and organizational and legal barriers make rapid deployment unlikely. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially hands-on supervisory tasks, sees minimal AI adoption for physical safety monitoring; sector and task type are laggards for this kind of automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by flagging hazardous patterns in pre-recorded footage, providing instructional videos on safe equipment use, or alerting teachers to concerning behaviors, but human judgment and presence remain essential for effective safety management. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI might help create safety instructions, checklists, or training materials in advance, but it offers little real-time assistance during actual supervision of equipment use. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time physical supervision, immediate behavioral intervention, and contextual judgment about student safety in dynamic environments. Current AI cannot physically monitor students or intervene when hazards arise, making end-to-end automation infeasible. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence in a classroom or lab to actively supervise students handling equipment and intervene in real time to prevent injury, which current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Duty of care, parental trust, and liability law require a responsible adult present and accountable for student safety. Educational regulations and common law negligence doctrine effectively mandate human oversight of hands-on activities, creating a hard legal barrier to substitution. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Legal liability, duty-of-care obligations, and school safety regulations require a certified, physically present teacher to supervise students with equipment; this is a hard institutional and legal barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying hardware (cameras, sensors, safety robotics) plus AI systems to achieve comparable safety outcomes far exceeds the salary cost of a teacher already present in the classroom for other instructional duties. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for in-person safety supervision, so the comparison is moot and the human is the only functioning option, making AI effectively more costly (infinite) for this specific function. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with pre-instruction content delivery or hazard identification in static scenarios, no deployed product reliably monitors students' actual equipment use and prevents injuries in real time. Existing systems lack the embodied perception and reaction capability this task demands. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product provides live physical supervision and safety intervention for students using equipment; this remains squarely a human responsibility. |
Attend staff meetings and serve on committees, as required.
3CI 0–5 · exposure 0 · augmentation 38 · importance 3.2/5 · click for rater detail
Attend staff meetings and serve on committees, as required.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task is embedded in institutional structure and legal employment requirements. There is no adoption pattern toward AI substitution for staff meeting attendance in schools—it remains entirely human-dependent by definition. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a slow-adopting sector for replacing human presence in governance and administrative participation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide limited assistance via meeting summarization, agenda drafting, or decision support materials, but the core activity of attendance and participation remains human-centric with minimal productivity transformation potential. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by summarizing meeting notes, drafting agendas, or tracking action items, but it doesn't change the core requirement of personal attendance and participation. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Staff meetings and committee service require real-time human presence, active participation in group discussion, decision-making, and interpersonal engagement. No current AI system can substitute for a human's obligation to attend meetings or meaningfully participate in institutional governance. |
| Task automatability | claude-sonnet-5 | 1/5 | Attending meetings and serving on committees requires physical/virtual presence, real-time deliberation, and institutional representation that AI cannot substitute for a human employee. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Staff meetings and committee service are legal and contractual obligations of employment. Institutional policy, labor agreements, and governance structures require human staff members to attend in person. No automation or delegation can substitute for the mandated human presence. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Institutional governance, employment obligations, and collective bargaining/policy requirements typically mandate the actual employee's participation in staff meetings and committees. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task is primarily about mandatory staff presence and participation, not work that scales per unit cost. There is no cost comparison model where AI attendance replaces teacher attendance at institutional meetings. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task, so no cost comparison favors AI; the human must be present regardless. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | While AI could summarize meeting notes or draft agendas, no deployed system can actually attend meetings as a required participant or serve on committees in place of a human. The core task is institutional presence and voice. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product allows AI to attend and participate in staff meetings or committee service on behalf of a teacher. |
Establish and enforce rules for behavior and procedures for maintaining order among students.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Establish and enforce rules for behavior and procedures for maintaining order among students.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools operate in traditionally low-digitization sectors with strong preferences for human-led classroom management. Adoption of AI for discipline and rule enforcement remains negligible; schools are not replacing teachers in this function, and pilots are rare. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a low-digitization sector for physical classroom management tasks, with essentially no adoption of AI to replace in-person behavioral enforcement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist marginally with logging incidents or flagging attendance patterns, but meaningful augmentation is limited because the core task—establishing authority, reading intent, and making judgment calls—relies on human presence and interpersonal dynamics that AI cannot enhance materially. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help draft classroom policies or behavior plans in advance, but offers minimal real-time assistance for the actual task of maintaining order among students. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Establishing and enforcing behavioral rules requires real-time judgment about context, student intent, relationships, and proportional responses that depend on individual student needs and classroom dynamics. Current AI systems cannot reliably perform this task end-to-end as it fundamentally requires human authority, presence, and relational trust. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires real-time physical presence, authority, and interpersonal judgment to manage classroom behavior, which current AI cannot perform in any end-to-end capacity. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and regulatory barriers exist: teachers have specific legal authority and accountability for student safety and discipline; parents expect human judgment; schools have liability frameworks built around human educators; and meaningful rule enforcement requires in-person human presence and interpersonal credibility. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Classroom supervision and discipline enforcement legally and institutionally require a certified, present human teacher; schools have strict duty-of-care and liability requirements. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The loaded cost of a teacher performing this task is already baked into their salary. AI systems would require classroom hardware, software integration, and continuous human oversight to handle exceptions, making total cost comparable to or exceeding the human baseline. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for in-person classroom management, so cost comparison is moot; the human is the only viable option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs classroom behavior management and rule enforcement at scale. While AI can assist with monitoring or alerting, actual rule enforcement requires a human authority figure with legal standing and contextual judgment that current systems cannot replicate in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product manages classroom discipline or enforces behavioral rules among students; this remains entirely a human function requiring physical presence and authority. |
Enforce all administration policies and rules governing students.
0CI 0–0 · exposure 0 · augmentation 25 · importance 3.8/5 · click for rater detail
Enforce all administration policies and rules governing students.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Education is a traditionally low-automation sector; school districts have been slow to deploy even basic administrative AI, and policy enforcement touches on student welfare, making organizational resistance very high. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a slow-adopting sector for AI in disciplinary/administrative authority roles, with essentially no production deployment of AI enforcing student conduct. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by flagging attendance or behavioral patterns for teacher review, but the core task—interpreting context and making fair, defensible enforcement decisions—remains fundamentally human and resistant to meaningful augmentation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help track infractions, generate reports, or flag patterns in student behavior data, but it offers minimal assistance in the actual act of enforcement itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Enforcing policies requires contextual judgment, discretion, and understanding of nuanced student situations—determining when rules apply, how strictly, and exceptions. Current AI cannot reliably make these judgments or substitute for human authority in school settings. |
| Task automatability | claude-sonnet-5 | 1/5 | Enforcing school policies and rules requires in-person authority, real-time judgment about student behavior, and physical/social presence that AI cannot replicate or execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Schools require licensed, credentialed humans to enforce disciplinary policies; parents, regulators, and law expect a qualified adult to make discretionary decisions about student conduct. Legal liability and duty-of-care requirements create hard barriers. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Enforcement of school policy involves legal authority, in loco parentis responsibilities, liability, and requires a certified/authorized staff member, making this a hard institutional and legal barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI deployment for policy enforcement would require substantial custom integration, oversight, and liability coverage, all of which exceed the cost of a teacher performing this core duty as part of their existing role. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this function, so cost comparison favors the human by default since AI cannot deliver the output at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product autonomously enforces school discipline or policy compliance; this inherently requires human judgment, authority, and presence. AI systems today cannot legally or operationally replace a teacher's enforcement role. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs disciplinary enforcement or rule enactment with students; this remains an inherently human, in-person administrative function. |
Provide students with disabilities with assistive devices, supportive technology, and assistance accessing facilities such as restrooms.
0CI 0–0 · exposure 0 · augmentation 38 · importance 3.6/5 · click for rater detail
Provide students with disabilities with 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 K–12 schools, a sector with low adoption of AI automation for direct student support. Adoption is limited to assistive technology tools (speech-to-text, etc.) that augment rather than replace the teacher's role in providing and facilitating access. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education support services show minimal AI adoption for physical assistance tasks; this remains a hands-on human caregiving function. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered assistive technologies (text-to-speech, communication apps, accessibility tools) can help students with disabilities independently or with teacher facilitation, raising overall productivity in accessing learning materials. However, the physical assistance and facility-access components remain entirely human-dependent. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-based assistive technologies (speech-to-text, communication devices) can support students' learning needs, but the core task of physical assistance and facility access is not meaningfully augmented by AI. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical interaction (distributing devices, assisting with facility access) and individualized judgment about student needs, both of which are beyond current AI capabilities. No AI system can autonomously perform the hands-on assistance or environmental accommodations this task demands. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical assistance, device fitting, and helping students access facilities like restrooms requires in-person physical presence and manual assistance that AI cannot perform. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal and regulatory barriers are substantial: schools have formal obligations under the Individuals with Disabilities Education Act (IDEA) and the Americans with Disabilities Act (ADA) to provide accommodations. A licensed educator must assess and implement these accommodations, and liability for failure to accommodate rests on the school and staff, not automated systems. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Special education law (IDEA, Section 504) requires qualified human staff to provide accommodations and physical assistance, and liability/safety concerns around physical care of students with disabilities create hard legal and ethical barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task requires human presence and intervention; AI cannot substitute for the physical assistance and interpersonal judgment needed. The cost of AI systems plus human oversight would exceed the cost of the teacher performing the task directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so cost comparison favors the human by default since AI cannot deliver the output at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs this task end-to-end. While accessibility tools exist, they do not replace the human role of providing assistive devices, supportive technology setup, and physical assistance with facility access in school settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product provides physical assistance or facility access help to students; this is inherently a human physical-support task. |
Sponsor extracurricular activities, such as clubs, student organizations, and academic contests.
0CI 0–0 · exposure 0 · augmentation 25 · importance 3.2/5 · 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 | Schools have shown negligible adoption of AI for extracurricular sponsorship. The sector remains highly human-centered, and there is no evidence of AI agents or systems being deployed in production for this purpose. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a slow-adopting sector for AI generally, and this specific in-person supervisory task has essentially zero AI adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist marginally with administrative tasks like scheduling meetings, drafting communications, or organizing contest rules, but offers little meaningful augmentation to the core relational and leadership work of sponsoring student activities. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help a teacher plan activities, generate contest questions, or organize club materials, but offers minimal help with the core supervisory/mentorship task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Sponsoring extracurricular activities requires human relationship-building, mentorship, judgment about student interests, and real-time engagement with students that AI cannot perform end-to-end. While AI could assist with administrative scheduling or communication drafts, the core sponsorship role—advising, motivating, and leading students—is fundamentally human. |
| Task automatability | claude-sonnet-5 | 1/5 | Sponsoring extracurricular activities requires in-person supervision, mentorship, chaperoning, and relationship-building with students that AI cannot perform physically or socially. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal and institutional barriers are substantial: schools have in loco parentis responsibilities, pastors of student organizations require trusted adult authority, and liability concerns around unsupervised student activities demand a qualified, accountable human. Regulations and organizational norms require human supervision and sponsorship. |
| Adoption barriers | claude-sonnet-5 | 5/5 | School policy, liability for student safety and supervision, and often certification/background-check requirements mandate a qualified human adult be physically present. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying AI systems to attempt this task (including infrastructure, integration, and oversight) would far exceed the marginal wage cost of a teacher already employed to sponsor such activities as part of their role. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the human presence required, so there is no viable cost comparison—the human is the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs the role of an extracurricular sponsor. AI has no presence in the relational, mentoring, and organizational dimensions of this task that define what sponsorship entails in a school setting. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product supervises clubs or in-person student organizations; this is fundamentally a physical presence and mentorship 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.