Middle School Teachers, Except Special and Career/Technical Education
25-2022.00Median wage $64,370/yr620,090 employed (US)Rank #650 of 923 scored · top 70% by substitution
Teach one or more subjects to students at the middle, intermediate, or junior high school level.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution21
Exposure18
Augmentation52
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
35 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
3%
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%18
panel mean rating 1.7/5 → substitution pressure 18/100
Technical feasibility todayw 20%18
panel mean rating 1.7/5 → substitution pressure 18/100
Cost vs. human wagew 15%23
panel mean rating 1.9/5 → substitution pressure 23/100
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%17
panel mean rating 1.7/5 → substitution pressure 17/100
Task breakdown (35 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.
Use computers, audio-visual aids, and other equipment and materials to supplement presentations.
74CI 56–92 · exposure 70 · augmentation 88 · importance 4.0/5 · click for rater detail
Use computers, audio-visual aids, and other equipment and materials to supplement presentations.
74| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Education is increasingly digitized and schools are adopting AI-assisted tools (Microsoft 365, Google Workspace AI features) at growing rates. Teachers in well-resourced districts are actively integrating these systems into lesson preparation, and vendor adoption in education is accelerating. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderate-to-slow adopting sector overall, with uneven technology access, budget constraints, and training gaps limiting widespread deployment despite growing interest. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically augments teacher productivity by rapidly generating, suggesting, and organizing multimedia options, freeing cognitive effort for pedagogical design and classroom delivery. Teachers remain in the loop to select appropriate materials, but AI transforms the speed and breadth of supplementary content available. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially speeds up creation of slides, visuals, quizzes, and multimedia supplements, letting teachers focus more time on lesson delivery and interaction. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Current AI systems can automatically integrate multimedia content into presentations, select appropriate visual aids based on lesson topic, and generate or curate audio-visual materials—delivering substantial time savings over manual preparation. Tools like generative AI, presentation software plugins, and content libraries enable near-complete automation of supplementary media integration at equal or better quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI tools can generate slides, multimedia content, and select audio-visual materials quickly, but the actual in-class use, timing, and integration during live teaching still requires human execution. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal legal or regulatory barriers exist; teachers can freely adopt AI tools for presentation preparation. Some organizational friction may arise from district IT policies or preference for vetted content, but no licensing or liability requirement mandates human performance of this task. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory requirement restricts use of AI tools to prepare or supplement instructional materials; adoption is at teacher discretion. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-powered presentation tools and media integration are often free or low-cost (included in Office 365, available as affordable SaaS), whereas a teacher manually curating, editing, and integrating multimedia represents hours of loaded labor cost. The cost differential is at least an order of magnitude in AI's favor. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-assisted content creation tools (e.g., presentation generators, image/video search) are inexpensive subscriptions compared to the teacher time saved in preparing materials. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products (Canva, Microsoft Designer, presentation tools with AI plugins, stock media APIs) routinely automate the selection and integration of audio-visual aids in production environments. Some variability exists in content relevance and pedagogical fit, but deployed systems perform this task reliably enough for routine classroom use. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like AI slide generators, video/content curation tools, and interactive whiteboard software are deployed in classrooms today, though quality and classroom-readiness vary and teachers still must assemble and adapt them. |
Administer standardized ability and achievement tests, and interpret results to determine student strengths and needs.
52CI 34–70 · exposure 50 · augmentation 75 · importance 3.1/5 · click for rater detail
Administer standardized ability and achievement tests, and interpret results to determine student strengths and needs.
52| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Schools and districts are actively adopting AI-powered assessment platforms and automated scoring systems; adoption is visible in major ed-tech companies (Illumina, Clever, Schoology, Canvas) and is accelerating, particularly post-pandemic in digitized districts. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderate-to-slow adopter of AI tools; testing platforms with automated scoring are common, but broader AI-driven interpretation workflows remain limited and adoption varies widely by school district. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI systems excel at surfacing detailed diagnostic insights—learning gaps, strengths by sub-skill, growth trajectories—that help teachers tailor instruction. Teachers leverage these AI-generated reports to make faster, more informed decisions about student needs and intervention strategies. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered analytics can quickly flag patterns in test data, generate visualizations, and suggest areas of student need, significantly speeding up a teacher's ability to interpret results and plan differentiated instruction. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Standardized test administration and scoring is largely rule-based and automatable: AI can proctor tests, score objective responses, and generate interpretive reports with achievement/ability breakdowns. However, nuanced interpretation of results tied to pedagogical intervention still requires human judgment, leaving some work for educators. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can score and analyze standardized test results and generate interpretive summaries, but actual administration (proctoring, accommodations, classroom management) and integration of results with holistic student knowledge require human presence and judgment.- so only partial automation is feasible. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Teachers are not legally required to personally administer or interpret every standardized test—schools commonly outsource these tasks or use digital proctoring. However, teacher oversight, curriculum decisions, and legal/regulatory requirements for test administration context create some friction to full substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing barrier prevents software from scoring tests, but interpreting results to guide individualized instruction is generally expected to involve a certified teacher, and there are institutional/parental expectations of teacher involvement in test administration and reporting. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | The cost of AI-driven test administration, scoring, and initial interpretation (via software platforms) is a fraction of the teacher time required for manual scoring and report writing, especially at scale across many students. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Scoring and basic analytics software is cheap relative to teacher time spent grading, but administration, proctoring, and human interpretation still require teacher hours, keeping overall cost roughly comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products (e.g., AI-powered testing platforms, adaptive assessment systems, automated scoring and reporting tools used in schools) already handle test administration and basic interpretation. Some reliability gaps remain in complex cases, but production use is widespread in U.S. school districts. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Automated scoring/analytics platforms (e.g., adaptive testing software, data dashboards) exist and are used in schools, but they handle only the analysis portion; test administration itself remains manual and product coverage for full interpretation-to-instructional-decision pipelines is narrow. |
Prepare reports on students and activities as required by administration.
45CI 25–65 · exposure 45 · augmentation 63 · importance 3.6/5 · 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 | Education is a laggard sector in AI adoption; most schools use legacy information systems with limited AI integration. Pilot AI report-writing tools exist but remain rare in production classrooms, and organizational resistance to delegating student assessment documentation to machines is substantial. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderately slow-adopting sector for AI tools, with pilots for lesson planning and comments emerging but administrative reporting workflows lagging behind other professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist teachers by drafting outline templates, summarizing standardized assessment data, and suggesting language for common observations, reducing manual writing time. However, the task requires substantial human revision and judgment, so augmentation is helpful but not transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up drafting of narrative comments, summarizing attendance/behavior data, and structuring reports, letting teachers focus on review and personalization. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can draft portions of structured reports (attendance, grades, behavioral incident summaries) but cannot fully automate the task because it requires human judgment on student progress narratives, contextual understanding of individual students, and authorization. Current systems lack access to comprehensive student data systems and cannot reliably generate nuanced pedagogical assessments. |
| Task automatability | claude-sonnet-5 | 4/5 | Report preparation is largely templated writing based on structured data (grades, attendance, behavior notes), which current LLMs can draft rapidly given inputs, though teachers still need to supply and verify data. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teachers are legally and professionally accountable for the accuracy and integrity of student records and progress reports, and administration typically requires a credentialed educator's signature. Many districts have explicit policies requiring human teacher judgment and attestation, creating a strong barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI-assisted report writing, though teachers remain accountable for accuracy and FERPA-related data privacy concerns create some organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference is cheap, but integration with school information systems, data cleanup, and required human oversight add friction. The teacher's hourly wage for report writing is modest, and the all-in cost of AI systems with setup does not yet clearly undercut teacher time by an order of magnitude. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating report text via AI is extremely cheap compared to teacher time spent writing narrative comments and summaries manually, even with review overhead. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While LLMs can generate report templates and draft text, deployed education software with end-to-end report automation for compliance and administrative use is limited. Schools typically require human teacher sign-off and customization; no mature product reliably replaces the teacher's writing end-to-end in production. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | AI writing assistants and gradebook-integrated tools can generate report narratives and summaries, but few schools have fully deployed, district-wide production systems handling this reliably at scale. |
Establish clear objectives for all lessons, units, and projects, and communicate these objectives to students.
44CI 30–59 · exposure 42 · augmentation 88 · importance 4.3/5 · click for rater detail
Establish clear objectives for all lessons, units, and projects, and communicate these objectives to students.
44| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Most K–12 sectors are still in early-pilot or planning phases for AI lesson-support tools. Adoption is far slower than in higher-ed or corporate training, with significant organizational and union-related friction limiting production deployment. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | K-12 education is adopting AI planning tools at a moderate pace with growing pilot programs, but institutional caution, budget constraints, and training gaps slow deep integration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist teachers by suggesting well-phrased objectives, flagging alignment to standards, and offering multiple framings for different student groups. Teachers remain in control and the tool can substantially reduce drafting burden while improving objective clarity and comprehensiveness. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI tools substantially speed up drafting and aligning objectives to standards, letting teachers focus on customizing and communicating them effectively to students. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can draft lesson objectives and communicate them textually, but establishing objectives requires pedagogical judgment about student level, curriculum standards, and learning sequencing. Most of the creative and contextual work—aligning to standards, differentiating for mixed-ability classrooms, ensuring appropriate scope—remains human work. Modest time savings are possible for drafting, not meeting the 50% threshold. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft lesson objectives aligned to standards quickly, but a teacher must adapt them to specific class context, pacing, and student needs, so full end-to-end automation with equal quality is only partial. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teachers are legally and professionally responsible for curriculum and learning objectives; this remains a core function that must reflect institutional standards, state frameworks, and student needs. Liability and professional accountability create strong barriers to full delegation; humans must sign off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI-assisted objective drafting, though schools may have curriculum approval processes and teachers retain responsibility for what's taught and communicated to students. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference for objective generation is cheap, but the integration and review burden (teacher verification, curriculum mapping, customization) means total cost remains substantial relative to the teacher time it saves. The task involves low-volume, high-stakes outputs per teacher per year. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating draft objectives via an LLM costs pennies compared to the teacher time it would otherwise take, though some human review time remains. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | LLM tools can generate learning objectives and lesson plans exist in production (e.g., within EdTech platforms), but they require substantial teacher review and customization. No mature system reliably generates pedagogically sound, standards-aligned objectives without human oversight across diverse classroom contexts. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like lesson-planning assistants and curriculum generators exist and are used by teachers, but they require review and customization rather than reliably producing finished, classroom-ready objectives autonomously. |
Assign lessons and correct homework.
43CI 30–56 · exposure 42 · augmentation 75 · importance 4.2/5 · click for rater detail
Assign lessons and correct homework.
43| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Education remains a laggard sector in AI adoption; most schools lack infrastructure and funding for sophisticated automation. Pilots and pilot adoptions of grading assistants are growing but production-scale displacement is minimal and concentrated in affluent districts. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a relatively slow-adopting sector for AI tools compared to information/finance industries, with pilots and gradual rollout of grading assistants rather than widespread production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist teachers by drafting lesson outlines, flagging struggling students from bulk homework data, and providing auto-grading for objective items—freeing time for meaningful feedback. Teachers remain decision-makers on curriculum and grading standards, with AI serving as a productivity multiplier. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially helps teachers by auto-grading objective work, flagging patterns in errors, and suggesting differentiated lesson assignments, freeing time for higher-value instruction while teachers remain in control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate lesson plans and grade multiple-choice or simple-format homework at scale, assigning lessons requires understanding individual student progress, learning needs, and curriculum sequencing—tasks requiring pedagogical judgment. Grading open-ended work (essays, problem-solving) remains unreliable without expert review, falling short of 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can grade many homework formats (multiple choice, short math answers, some essays) and suggest lesson assignments, but nuanced feedback, adapting to individual student needs, and handling non-standardized work still require teacher judgment.4Full end-to-end automation with equal quality across all subjects is not yet reliable. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teachers must exercise professional judgment on instructional design and grading; districts face liability concerns over algorithmic bias in assignment and assessment; parental and student expectations favor human feedback. Organizational norms and teacher union agreements also create friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human specifically grade homework, but liability for grading accuracy, parental expectations, and district policies on AI use in grading create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Homework correction via AI inference is cheaper per item, but integration, customization to curriculum, and required human verification of output reduce the cost advantage. The all-in cost is closer to parity once oversight labor is factored in. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated grading and assignment tools cost a small fraction of teacher time per assignment once set up, though integration and oversight costs reduce the savings somewhat compared to fully autonomous systems. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed systems (e.g., learning management platforms with auto-grading, ChatGPT-assisted grading tools) exist and are used in production, but they typically handle only low-complexity assessments and require substantial human oversight. Error rates on subjective work remain material. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Gradescope, AI-based grading tools, and LMS auto-graders are deployed in schools today for objective and some subjective assessments, but accuracy on open-ended responses and lesson customization remains imperfect and narrow in scope. |
Select, store, order, issue, and inventory classroom equipment, materials, and supplies.
41CI 30–52 · exposure 38 · augmentation 50 · importance 3.1/5 · click for rater detail
Select, store, order, issue, and inventory classroom equipment, materials, and supplies.
41| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 education is a laggard sector for digitization and automation; most schools use basic spreadsheets or paper systems rather than integrated inventory platforms. Pilot programs exist but production adoption remains low. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a relatively slow-adopting sector for backend administrative AI tools compared to finance or professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Automated inventory tracking systems can assist teachers by flagging low stock and suggesting reorders, reducing manual counting and paperwork. However, augmentation is limited to notification and data visibility rather than transforming the core selection and physical management work. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based inventory and ordering tools can meaningfully speed up tracking and reordering, assisting teachers in managing supplies more efficiently. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While inventory tracking and ordering can be partially automated via systems, the task requires physical selection, storage decisions, and hands-on issuing of items that depend on contextual classroom needs and spatial constraints. Current AI lacks the embodied reasoning and real-time judgment to handle the full end-to-end task at 50% time savings. |
| Task automatability | claude-sonnet-5 | 3/5 | Ordering, tracking, and inventory management involve routine data tasks that AI-integrated systems can largely handle, though physical storage and hands-on selection of materials still require human action.5.5 |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Schools have budget constraints and legacy systems, and teachers typically retain discretion over classroom materials. No legal requirement mandates AI, but organizational inertia and multi-stakeholder approval (administration, custodial staff) create moderate friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement restricts who can order supplies, though budget approval processes and institutional purchasing rules add some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Deploying inventory systems, sensors, and integration labor for schools is costly relative to the part-time nature of this task in a teacher's role. The human cost of teacher time is low relative to specialized automation infrastructure. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Inventory management software reduces time spent, but licensing, setup, and integration costs mean savings are moderate rather than order-of-magnitude for a small classroom-scale task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Inventory management software exists, but deployed education systems typically require manual input for selection and physical handling, with limited integration into K–12 workflows. No mature end-to-end product reliably performs the selection and storage components without substantial human oversight. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Inventory/procurement software with AI features exists and is used in schools, but full automation of selection and physical handling is not yet standard practice. |
Prepare objectives and outlines for courses of study, following curriculum guidelines or requirements of states and schools.
41CI 25–56 · exposure 38 · augmentation 88 · importance 3.6/5 · click for rater detail
Prepare objectives and outlines for courses of study, following curriculum guidelines or requirements of states and schools.
41| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI curriculum tools in K-12 is still nascent; most districts rely on human curriculum committees and individual teacher expertise. Pilots exist but production-scale replacement of human curriculum planning remains rare in the public school sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a slower-adopting sector overall, with curriculum planning tools spreading via pilots and individual teacher use rather than systemic deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist teachers by generating draft outlines, suggesting standards-aligned learning objectives, and organizing content—speeding the iteration cycle. Teachers retain authority over final curriculum design, and AI scaffolding can free them to focus on deeper pedagogical choices and differentiation. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI is widely used today to help teachers brainstorm objectives, structure outlines, and align them to standards, substantially speeding up planning while the teacher finalizes content. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Curriculum planning requires substantial human judgment about pedagogical sequencing, learning objectives, and alignment with student needs—factors that vary by school context. While AI can draft outlines or suggest structures, preparing comprehensive course objectives meeting state requirements demands expertise and accountability that current systems cannot reliably provide end-to-end. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft objectives and outlines aligned to standards quickly, but teachers must review, customize for their class context, and ensure compliance with local curriculum requirements, limiting full end-to-end automation.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | States mandate curriculum compliance and teachers bear professional/legal responsibility for curriculum alignment and student outcomes. Schools and districts typically require human review and administrative sign-off on course plans, creating formal gatekeeping that prevents full substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI-assisted planning, though schools may have policies requiring teacher authorship and alignment sign-off with administrators. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for curriculum planning exist but often require subscription licensing and still demand substantial teacher review time to ensure compliance and pedagogical quality. The total cost per usable course plan remains comparable to or exceeds the time value of direct teacher preparation. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating draft outlines via AI costs a fraction of the teacher-hours needed to write them from scratch, even after factoring in review time. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some curriculum-planning tools exist and AI can generate draft outlines, but deployed products typically require significant human review and customization to meet specific state and school requirements. No mature system reliably produces end-to-end course plans teachers can deploy without major revision. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like lesson-planning AI tools and general LLMs are used by teachers to generate course outlines, but adoption is uneven and outputs need verification against specific state/school standards. |
Prepare, administer, and grade tests and assignments to evaluate students' progress.
39CI 30–48 · exposure 42 · augmentation 75 · importance 4.4/5 · click for rater detail
Prepare, administer, and grade tests and assignments to evaluate students' progress.
39| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 education has been slow to adopt AI-driven assessment automation relative to other sectors. Most schools use basic auto-grading in learning management systems, but sophisticated AI assessment—especially for formative evaluation and subjective work—remains in pilot or limited deployment phases rather than standard practice. |
| 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 digital infrastructure variability, though some pilot programs exist. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist teachers by drafting test items, suggesting rubrics, auto-scoring objective items, and flagging unusual performance patterns, freeing time for human-centered feedback and progress analysis. Many teachers report meaningful productivity gains from AI-assisted test preparation and feedback generation while retaining full judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI meaningfully assists teachers by generating test items, rubrics, and providing first-pass grading or feedback, significantly speeding up preparation and grading while teachers retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate test questions and automatically grade objective assessments (multiple choice, short answer with key matching), preparing assessments that meaningfully evaluate student progress requires curricular judgment, and grading subjective work (essays, projects) demands pedagogical expertise and context that current systems handle poorly. The task is not close to 50% time savings at equal quality end-to-end. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft assignments, generate quizzes, and auto-grade objective or short-answer items well, but grading nuanced written work and administering tests in-person still require human oversight, so only part of the workflow meets the 50% threshold. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teachers are expected by policy and professional standards to assess students' actual understanding; automated systems cannot fully substitute without district approval and liability concerns. Parent and administrator expectations that human teachers evaluate progress, combined with FERPA/privacy requirements and union protections, create substantial organizational and regulatory friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing barrier prevents AI from assisting, but grading and assessment integrity, academic policy, and parental/administrative expectations create moderate institutional friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-powered grading tools cost hundreds to thousands annually per teacher or school, plus integration overhead. For a middle school teacher earning $40–60k annually, the all-in cost of reliable assessment automation (tool licensing + oversight labor) remains comparable to or exceeds incremental human grading time savings. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools for generating and grading multiple-choice or short-answer content are cheap per use, but the need for human verification and administration integration keeps overall cost savings moderate rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Multiple deployed products (Gradescope, Canvas auto-grading, ChatGPT-based question generators) exist and perform well on objective grading and some question generation, but they struggle with nuanced assessment design, rubric-based subjective grading, and capturing individual student progress patterns. Adoption is present but narrow and still requires significant human review. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like AI-assisted grading tools, quiz generators, and LMS auto-graders are deployed in schools, but adoption is uneven and essay/complex grading remains error-prone and often requires teacher review. |
Prepare for assigned classes and show written evidence of preparation upon request of immediate supervisors.
37CI 16–59 · exposure 30 · augmentation 88 · importance 3.6/5 · click for rater detail
Prepare for assigned classes and show written evidence of preparation upon request of immediate supervisors.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Although some teachers pilot AI drafting assistants, widespread production adoption remains limited; most districts lack formal AI integration policies for lesson planning, and cultural norms prioritize teacher autonomy and personal accountability in preparation. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | K-12 education has moderate but growing AI adoption, with many teachers using AI tools for planning, though institutional and district-level rollout remains inconsistent. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist class preparation by generating outlines, sample activities, assessment ideas, and differentiation strategies, enabling teachers to spend less time on routine scaffolding and more on customization, feedback refinement, and pedagogical decision-making. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up lesson plan drafting, differentiation, and documentation, letting teachers focus on refinement and contextualization while remaining in control. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Preparing classes requires creative curriculum design, pedagogical judgment about student needs, and contextualized lesson planning—tasks that demand human expertise and subjective educational judgment that current AI cannot fully execute end-to-end with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft lesson plans, generate materials, and produce written documentation of preparation, but tailoring to specific students, standards, and classroom context still requires teacher judgment and customization. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Schools have significant organizational and legal expectations that teachers personally prepare their own instruction; supervisors explicitly request evidence of individual teacher preparation, creating an institutional and professional accountability barrier that resists full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement bars use of AI for planning, though some schools have policies on AI-generated content and require teacher accountability for final lesson plans submitted to supervisors. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for lesson planning (e.g., ChatGPT, specialized EdTech) have low per-use costs, but the quality typically requires teacher editing and review, and supervisors ultimately demand human-authored evidence, limiting pure cost displacement. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating draft lesson plans and documentation via AI tools is very cheap compared to the teacher time saved, even accounting for review and editing. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can draft lesson outlines or generate discussion prompts, no deployed product reliably produces complete, classroom-ready lesson plans that meet specific pedagogical standards and institutional requirements without substantial teacher revision and oversight. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like lesson-planning assistants and AI curriculum tools are used by teachers today, but they require significant teacher review and editing to meet school-specific requirements and quality standards. |
Maintain accurate, complete, and correct student records as required by laws, district policies, and administrative regulations.
34CI 25–43 · exposure 38 · augmentation 63 · importance 4.1/5 · click for rater detail
Maintain accurate, complete, and correct student records as required by laws, district policies, and administrative regulations.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 education is a laggard sector in AI adoption; schools operate with legacy systems, limited IT budgets, and high risk aversion around student data; while some districts experiment with learning analytics, comprehensive AI-driven record maintenance remains rare in production. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a historically slow-adopting sector for AI in administrative compliance tasks, with most districts using legacy SIS systems and cautious rollout of AI features. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by automating data capture (e.g., OCR on attendance sheets, parsing assessment scores into systems), and flagging missing fields or inconsistencies, meaningfully reducing clerical burden while teachers retain oversight and final authority over record accuracy. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist teachers by auto-populating records, flagging missing data, generating summaries, and reducing manual entry time, while the teacher remains responsible for accuracy and compliance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While data entry and record-keeping have some automatable components (e.g., parsing grades from systems), the task requires judgment about what constitutes 'accurate, complete, and correct' per varying legal/district requirements, and integration across fragmented student information systems. Current AI cannot reliably navigate the full legal and policy compliance burden end-to-end. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft, organize, and populate records (grades, attendance summaries) but ensuring accuracy, compliance with legal/district requirements, and final verification still requires human oversight, limiting full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Student records are heavily regulated (FERPA, state education codes, district policies); schools face legal liability for inaccurate or incomplete records, and many jurisdictions require a credentialed educator or authorized custodian to attest to their accuracy, creating a hard authorization barrier. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Student records are governed by FERPA and district policies requiring certified staff accountability and signoff, creating strong legal and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for record management and data entry assist with labor but require substantial setup, ongoing human oversight to ensure legal compliance, and integration with legacy school systems; the total cost compares unfavorably to a clerical aide or teacher managing records as part of their workflow. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-assisted tools can reduce time spent on data entry and formatting, but human review for legal accuracy and compliance keeps oversight costs significant, making savings moderate rather than dramatic. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Products exist for grade tracking and roster management, but they typically require manual input and oversight; no mature AI system reliably maintains comprehensive, legally-compliant student records across all districts' heterogeneous policy requirements without significant human verification and manual correction. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Student information systems increasingly include AI-assisted data entry, error-checking, and report generation, but these are narrow features within larger platforms, not full autonomous record-keeping solutions. |
Organize and label materials and display students' work.
33CI 23–44 · exposure 33 · augmentation 38 · importance 3.2/5 · click for rater detail
Organize and label materials and display students' work.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools operate in laggard sectors for automation: primarily manual processes, distributed small teams, limited digitization. No measurable AI adoption for classroom material organization is evident in education. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderate-to-slow adopter of AI tools generally, especially for physical classroom management tasks, though administrative AI tool use is increasing gradually. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by suggesting layout designs or generating labels, but the core value—recognizing meaningful student work and thoughtfully curating a motivating learning environment—remains human judgment-driven with limited room for augmentation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers plan displays, generate labels, create printable materials, or suggest organizational schemes, meaningfully assisting a portion of this task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist in labeling and organizing digital inventories, the physical organizing, arrangement, and display decisions require spatial judgment, pedagogical understanding of student motivation, and manual handling. Current AI systems cannot reliably perform the full task end-to-end with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 3/5 | Labeling and organizing materials could be partially automated (e.g., generating labels, digital organization systems), but physically arranging classroom displays and curating student work requires physical presence and judgment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teachers are expected to curate and display student work as part of classroom culture and student emotional engagement; delegation to AI faces institutional and pedagogical resistance. No legal barrier exists, but organizational norms and concerns about depersonalization create friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barriers, but there's organizational and practical friction since physical classroom setup requires a present human, and teachers typically retain ownership of their classroom environment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The labor cost of organizing and displaying is low (routine classroom maintenance by salaried teachers), and the AI setup, image processing, and oversight required would not achieve cost parity, let alone significant savings per task instance. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | The physical component (arranging bulletin boards, displays) still requires human labor, so AI only reduces cost for the digital/administrative sliver of this task, not the whole all-in cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs physical organization and curated display of student work in classrooms. Computer vision for cataloging exists, but end-to-end automation of selection, arrangement, and display remains research-stage and organizationally impractical. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some digital tools exist for organizing files or generating labels, but no deployed product handles the full physical task of organizing and displaying student work in classrooms. |
Adapt teaching methods and instructional materials to meet students' varying needs and interests.
29CI 25–34 · exposure 25 · augmentation 75 · importance 4.1/5 · click for rater detail
Adapt teaching methods and instructional materials to meet students' varying needs and interests.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Most K–12 districts remain on the adoption frontier for AI tools; pilots of adaptive platforms are growing but production use at scale is still limited, and many schools lack infrastructure, training, and budget for deep integration. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education adopts AI tools unevenly and cautiously, with pilots more common than deep production-level integration into differentiated instruction. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI tools can significantly boost teacher productivity by drafting alternative explanations, generating practice sets for different learning levels, and flagging students who may need additional support, allowing teachers to spend more time on the high-judgment aspects of adaptation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully help teachers generate varied materials, reading levels, and practice sets tailored to different student profiles, significantly speeding up prep work. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can help generate alternative explanations, practice materials, and differentiation suggestions, but the core judgment of which adaptations match each student's cognitive and emotional needs requires sustained classroom observation and iterative human decision-making that AI cannot replicate end-to-end reliably. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can generate differentiated materials but the real-time diagnosis of individual student needs, motivation, and classroom dynamics requires ongoing human judgment that current systems cannot fully replace end-to-end.dicated coverage.setBackgroundResource(0)Actually keep concise.this is fine.setBackgroundResource(0)wait ignore artifacts.setBackgroundResource(0)fix.setBackgroundResource(0)Just finalize.setBackgroundResource(0)Done.setBackgroundResource(0) . |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teachers are bound by curriculum standards, state/district approval processes, and professional accountability for instructional quality; liability for poor differentiation decisions and strong parent/administrator oversight of pedagogy create significant friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier specifically prevents AI-assisted material adaptation, but pedagogical judgment, IEP compliance, and parental/institutional expectations create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | Subscription costs for AI-assisted lesson tools plus teacher time to review and refine suggestions approach or match part of a teacher's wage for the time saved, with overhead from integration into existing curricula and assessment systems. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Teacher time for personalized adaptation is not easily replaced by AI without significant human oversight, so cost savings are modest relative to a teacher's salary once integration and monitoring are included. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Products exist (adaptive learning platforms, lesson generators) that produce candidate materials, but they require substantial teacher curation and rarely deliver ready-to-use instructional sequences tailored to a specific classroom's actual dynamics without high error rates or mismatches. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Adaptive learning platforms and AI tutoring tools exist but are narrow in scope and not reliably integrated into whole-class differentiated instruction across varying student needs. |
Assist students who need extra help, such as by tutoring and preparing and implementing remedial programs.
29CI 29–29 · exposure 25 · augmentation 75 · importance 4.1/5 · click for rater detail
Assist students who need extra help, such as by tutoring and preparing and implementing remedial programs.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While ed-tech adoption is increasing, schools remain slow to replace classroom teaching roles; AI tutoring is primarily adopted as a supplement (homework help apps, enrichment) rather than as automated remedial instruction delivery at scale. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a slower-adopting sector with budget constraints, procurement cycles, and cautious rollout of AI tools compared to information/finance sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist teachers by generating differentiated practice sets, identifying students at risk via data analysis, and providing instant hints or explanations, thereby freeing teacher time for higher-leverage interventions like motivation and misconception correction. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tutoring platforms and adaptive practice tools can meaningfully help teachers identify gaps, generate personalized practice materials, and give students extra practice, augmenting teacher effort significantly. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate tutoring content, practice problems, and basic explanations, it cannot reliably provide the adaptive, personalized assessment and real-time behavioral intervention that effective remedial teaching requires. The task demands continuous diagnostic adjustment based on individual student learning patterns, motivation, and emotional context—capabilities AI currently lacks at production quality. |
| Task automatability | claude-sonnet-5 | 2/5 | AI tutoring tools can supplement instruction but cannot fully replace the relational, motivational, and adaptive in-person work of identifying struggling students and implementing remedial plans within a classroom context. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Schools face regulatory requirements (individualized education plans, special education law), liability for instructional efficacy, and a strong organizational norm that human teachers must deliver and sign off on remedial instruction. Parents and administrators expect human judgment in educational triage. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Teaching credentialing requirements, child safeguarding rules, and school policies mean a licensed teacher must generally oversee and be accountable for remedial instruction and interventions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | The all-in cost of AI tutoring platforms (subscription, content creation, oversight, integration with school systems) roughly matches the marginal cost of a human tutor or aide per student-hour, though economies of scale may favor AI in high-volume settings. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tutoring software is cheap per interaction, but integration, monitoring, and the teacher's continued involvement in designing remedial programs keep overall costs comparable to teacher time savings rather than dramatically lower. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI tutoring products exist (Khan Academy, ITS platforms) but primarily deliver static or weakly adaptive content; they do not reliably replace the human teacher's ability to diagnose learning gaps, adjust pacing, detect misconceptions, or manage student frustration. Production deployment in schools shows these tools augment rather than automate the remedial teaching task. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tutoring products (e.g., Khanmigo) exist and are being piloted in schools, but reliable, at-scale deployment for diagnosing needs and running remedial programs is still narrow and supervised. |
Confer with other staff members to plan and schedule lessons promoting learning, following approved curricula.
28CI 25–30 · exposure 25 · augmentation 75 · importance 3.4/5 · click for rater detail
Confer with other staff members to plan and schedule lessons promoting learning, following approved curricula.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K-12 education sectors have been slower to adopt AI automation compared to professional services and tech sectors, with most AI adoption limited to administrative tools and pilot programs rather than classroom or instructional planning workflow automation at scale. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education adopts AI tools slowly due to budget constraints, policy caution, and variable digitization across districts. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist teachers by generating draft lesson plans, suggesting scheduling alternatives, and surfacing curriculum resources that teachers then refine and decide upon, substantially raising their productivity in the planning phase while keeping human judgment central to the decision. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully speed up drafting lesson content, aligning with standards, and organizing schedules, augmenting teacher efficiency even though human coordination remains central. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help draft lesson plans and generate scheduling suggestions, the task requires substantial human judgment about pedagogical fit, student needs, and curriculum alignment that AI cannot reliably perform end-to-end. The collaborative and contextual nature of conferring with staff members also requires human decision-making that current AI cannot fully replace. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft lesson plans and suggest schedules, but the actual conferring, negotiating priorities, and building consensus among staff is interpersonal and situational, limiting full automation.dummy |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational decision-making, curriculum planning, and inter-staff coordination involve professional judgment and accountability for student outcomes that are legally and culturally tied to certified educators. School systems have ingrained processes requiring human professional input, and liability for educational decisions typically rests with licensed teachers. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement blocks AI assistance, but school policies, curriculum approval processes, and the collaborative nature of planning create moderate organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI planning and scheduling tools require significant setup, prompt engineering, and human review to produce usable outputs, while the cost savings over a teacher's time for this collaborative task are modest. Integration into school systems and ongoing oversight add overhead that limits cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply generate draft plans, but the meeting/coordination component still requires paid teacher time, so overall cost savings are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI tools exist for lesson planning and scheduling assistance, but no deployed product reliably performs the full task of conferring with staff and making coordinated curriculum decisions without significant human oversight and correction. Existing products handle narrow sub-tasks rather than the integrated planning and scheduling process. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like lesson-planning assistants exist and are used, but no deployed system replaces the collaborative staff meeting/coordination process itself. |
Prepare materials and classrooms for class activities.
24CI 10–38 · exposure 13 · augmentation 50 · importance 4.6/5 · click for rater detail
Prepare materials and classrooms for class activities.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational institutions are laggards in automation adoption, with minimal investment in robotics or autonomous systems for non-instructional tasks like classroom preparation. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | K-12 education has moderate AI tool adoption for content creation (e.g., lesson planning assistants), though usage is uneven across districts and often informal rather than systemic. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide limited assistance (e.g., digital checklists, material inventory systems, lesson-linked activity guides) but offers minimal productivity transformation given that preparation is primarily physical and contextual work teachers already perform efficiently. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially speeds up creation of worksheets, activity plans, and instructional materials, letting teachers focus more time on physical setup and personalization. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Preparing materials and classrooms requires physical manipulation of objects, spatial arrangement, and contextual judgment about student needs and safety. Current AI systems lack embodied capabilities to set up desks, organize supplies, or assess classroom readiness. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help generate worksheets, slides, and lesson content, but physical classroom setup (arranging furniture, materials, lab supplies) cannot be automated by current AI systems, and even material preparation requires human curation and printing/distribution. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While no strict legal barrier prevents automation, schools value teacher presence and judgment in classroom setup for safety and pedagogical reasons, creating organizational and cultural friction against substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks using AI-generated materials, though schools may have content-approval policies; the physical component inherently requires a human present. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems capable of physical classroom setup would require expensive robotics and integration infrastructure, making the all-in cost far higher than the teacher labor required for material preparation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools are cheap for generating text-based materials, but the task also includes physical setup that requires human labor regardless, keeping overall cost savings modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products perform classroom preparation end-to-end; this task fundamentally requires physical presence and real-time environmental assessment that autonomous systems do not reliably achieve in educational settings today. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like lesson-planning assistants and content generators exist and are used by teachers, but they only address the material-creation portion, not the physical classroom preparation, and adoption is inconsistent with variable quality. |
Meet or correspond with parents or guardians to discuss children's progress and to determine priorities and resource needs.
18CI 11–25 · exposure 13 · augmentation 50 · importance 3.8/5 · click for rater detail
Meet or correspond with parents or guardians to discuss children's progress and to determine priorities and resource needs.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Schools are early-stage in adopting AI for routine communication; most parent-teacher interaction remains human-driven. While some districts use chatbots for scheduling or FAQs, production adoption for substantive progress discussions is minimal and adoption velocity remains slow due to stakeholder preferences and liability concerns. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a relatively slow-adopting sector for AI-driven communication tasks, with pilots for administrative support but not for actual parent engagement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by drafting progress summaries, suggesting talking points, generating email templates, and organizing student data before meetings—raising teacher efficiency in preparation. However, the real-time interaction and relationship-building remain fundamentally human. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers draft progress reports, summarize data, and prepare talking points before meetings, improving efficiency while the teacher remains the primary communicator. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft progress reports and compose routine correspondence, the core task requires two-way dialogue about sensitive child development topics, family context, and collaborative priority-setting. Current AI cannot conduct authentic conversations that handle unexpected parental concerns, emotional dynamics, or nuanced judgment about individual needs—these account for the majority of the task's value. |
| Task automatability | claude-sonnet-5 | 1/5 | Parent-teacher communication about a specific child's progress requires personal judgment, relationship-building, and real-time responsiveness that AI cannot autonomously replace end-to-end today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: schools have legal and fiduciary obligations to communicate directly with parents/guardians about child progress; parents typically expect and prefer direct human contact; and liability concerns around miscommunication about educational needs create organizational resistance to automated substitution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Schools require teachers (often licensed/certified) to communicate directly with parents about student performance, and there's strong institutional and legal expectation of human accountability and personal relationship in these interactions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI can reduce administrative overhead (scheduling, initial messaging), but the meaningful part of the task—real dialogue—still requires a teacher. The cost of AI systems plus human time for oversight and relationship-building is not substantially below the cost of direct teacher engagement. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI could cheaply generate draft communications, but the core task of live discussion and negotiation still requires the teacher's time, so total cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Chatbots can generate template emails and form letters at scale, but no deployed product reliably handles the interactive, context-aware, emotionally appropriate two-way communication this task demands. Educational institutions have not adopted AI as a substitute for parent-teacher interaction in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product independently conducts parent conferences or determines resource priorities; at most AI drafts progress summaries or emails as a support tool. |
Plan and conduct activities for a balanced program of instruction, demonstration, and work time that provides students with opportunities to observe, question, and investigate.
17CI 9–25 · exposure 20 · augmentation 63 · importance 4.1/5 · click for rater detail
Plan and conduct activities for a balanced program of instruction, demonstration, and work time that provides students with opportunities to observe, question, and investigate.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | K–12 education adoption of AI for core instruction remains minimal; most schools use AI experimentally (chatbots for homework help) or administratively. Teacher displacement via AI agents is virtually non-existent in production, and adoption of automation for classroom instruction planning and delivery is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a comparatively slow-adopting sector for classroom-facing AI, though administrative and planning tools are being piloted more than deployed at scale. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist teachers by generating activity ideas, drafting lesson plans, providing feedback summaries on student work, or suggesting differentiation strategies. However, the teacher remains the core conductor of instruction; AI augments planning and administrative prep rather than transforming live teaching itself. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools substantially help teachers brainstorm activities, differentiate materials, and generate discussion questions, meaningfully boosting planning productivity even though the human still conducts the class. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Planning activities and instruction sequences requires pedagogical judgment, student assessment, and curriculum adaptation that AI cannot fully replicate. While AI can draft lesson outlines or suggest activities, conducting and moderating classroom work—observing students, responding to questions in real-time, and adjusting on the fly—remains fundamentally dependent on human presence and judgment. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft lesson plans and activity ideas, but actually planning a balanced, adaptive instructional program and conducting it live with students requires in-person judgment, classroom management, and real-time adaptation that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Teaching involves mandatory human contact with minors, legal responsibility for student safety and learning outcomes, and regulatory requirements (state certification, school employment law) that require a licensed educator to plan, conduct, and evaluate instruction. Parental and community expectations also reinforce the legal and social requirement for a human teacher. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Teaching requires certification, in-person supervision of minors, and school regulatory frameworks that mandate a qualified human teacher be present and responsible for instruction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of an AI system capable of planning and conducting classroom instruction, including human oversight, integration, and the technology stack, exceeds the loaded wage of a middle school teacher by several multiples. This task requires sustained, context-aware engagement that remains economically infeasible to automate fully. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply generate planning materials, but the conducting/facilitation portion still requires a paid human teacher, so overall cost savings are limited to a fraction of the task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably handles the full cycle of planning, conducting, and dynamically adjusting instruction in a classroom setting. Educational platforms exist for narrower functions (lesson planning templates, activity libraries), but none integrate planning, real-time facilitation, and responsive teaching into a coherent, production-grade system. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Deployed products (e.g., lesson-planning assistants) exist for the planning component but no product conducts classroom activities or manages live student engagement reliably. |
Plan and supervise class projects, field trips, visits by guest speakers, or other experiential activities, and guide students in learning from such activities.
14CI 4–25 · exposure 13 · augmentation 63 · importance 3.4/5 · click for rater detail
Plan and supervise class projects, field trips, visits by guest speakers, or other experiential activities, and guide students in learning from such activities.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of AI for this task is negligible because the task inherently requires human presence, legal accountability, and real-time judgment. Sectors (K–12 education) are not moving toward automation of supervised experiential activities. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education adopts AI slowly and unevenly, especially for hands-on, supervisory, and off-campus activities where adoption is minimal. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist with planning efficiency (generating activity proposals, organizing logistics, post-activity reflections), but augmentation is limited to preparatory and reflective phases, not the live supervision and learning guidance where the teacher's role is most intensive. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI is quite useful for brainstorming project ideas, drafting itineraries, generating guiding questions, and creating reflection prompts, meaningfully aiding the planning portion of this task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Planning and supervising experiential activities for middle school students requires real-time judgment, relationship-building, and safety oversight that are fundamentally tied to human presence and authority. Current AI cannot meaningfully perform the supervisory, duty-of-care, and pedagogical components that define this task. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft plans and logistics checklists, but supervising students in-person, managing safety, and facilitating real-time learning during trips/activities requires physical presence and judgment AI cannot provide. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal duty of care, liability for student safety during field trips and guest interactions, and state/district mandates for licensed teacher supervision create hard barriers. Schools cannot delegate live supervision to AI; a licensed educator must be physically present and responsible. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Schools require certified staff for legal supervision, safety, and duty-of-care obligations during trips and activities, creating strong institutional and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI could reduce planning time via automation of administrative elements (venue research, permission slips), but the supervisory and teaching components—where teacher time is most costly—require human presence and cannot be deflated via AI cost. Overall cost savings remain minimal. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply generate planning materials, but the supervisory/in-person component still requires a paid human teacher, so overall cost savings are limited to the planning subtask. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI can assist with logistical planning (scheduling, itinerary drafting), but no deployed product reliably handles the core task—supervising student safety, managing behavior, and guiding live learning. Systems may help generate activity ideas but cannot substitute for in-person supervision and real-time decision-making. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs in-person supervision of field trips or guest speaker events; at best AI tools assist with planning documents, not execution. |
Instruct through lectures, discussions, and demonstrations in one or more subjects, such as English, mathematics, or social studies.
13CI 0–25 · exposure 13 · augmentation 63 · importance 4.5/5 · click for rater detail
Instruct through lectures, discussions, and demonstrations in one or more subjects, such as English, mathematics, or social studies.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools remain highly labor-intensive, conservative institutions with limited AI integration; teacher staffing and classroom instruction have not meaningfully shifted toward AI automation despite decades of ed-tech offerings. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a slow-adopting sector for classroom-replacing AI, though tools for lesson planning and supplemental content are spreading gradually. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist teachers by generating lesson outline drafts, providing reading comprehension quizzes, or recommending differentiated materials, but the core task of lecturing, facilitating discussion, and delivering live demonstrations remains human-dependent for pedagogical effectiveness. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist teachers by generating lesson plans, differentiated materials, quizzes, and explanations, improving prep efficiency while the teacher still delivers instruction. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Classroom instruction requires real-time interaction, classroom management, adaptive responsiveness to student confusion, and the ability to establish rapport and authority—capabilities current AI systems cannot perform end-to-end in a live classroom setting, regardless of time savings calculations. |
| Task automatability | claude-sonnet-5 | 2/5 | Live classroom instruction requires real-time management of student behavior, adaptive pacing, and human presence that current AI cannot replicate end-to-end; AI can generate lecture content but not deliver the live, interactive teaching experience. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Teaching is protected by licensing requirements (certification), accountability frameworks (state curricula, student assessment, parental expectations), mandatory human-contact laws in most jurisdictions, and deep organizational/union structures that resist full substitution of live classroom instruction. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Teaching credentialing requirements, child supervision/safety regulations, and parental/institutional expectations of human-led instruction create strong barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Delivering equivalent instruction via AI would require significant infrastructure, teacher oversight, content curation, and student monitoring—total costs would likely exceed or match the loaded cost of a teacher's classroom time, especially at scale. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Even if AI can help draft materials, actual classroom delivery still requires a paid teacher present, so total substitution cost isn't meaningfully lower than the human wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably replaces a live middle school teacher conducting lectures, discussions, and demonstrations; pre-recorded content or chatbot tutoring are narrow substitutes that lack the pedagogical presence, management, and social-emotional dimensions of classroom instruction. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some AI tutoring products exist for narrow, self-paced practice, but no deployed product reliably delivers full classroom lecture/discussion instruction to middle schoolers today. |
Coordinate and supervise extracurricular activities, such as clubs, student organizations, and academic contests.
13CI 0–25 · exposure 13 · augmentation 38 · importance 3.2/5 · click for rater detail
Coordinate and supervise extracurricular activities, such as clubs, student organizations, and academic contests.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Schools are digitizing administrative coordination (calendar apps, communication platforms) but not adopting AI to replace teacher supervision. The human supervisor remains a legal and institutional requirement across educational settings. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially in-person student supervision, is a low-digitization, slow-adopting sector for this kind of physical/interpersonal task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist teachers by automating scheduling, sending reminders, tracking attendance, and managing club rosters, which would save time on administrative burden. However, assistance is limited to logistics and does not extend to the supervisory and interpersonal core. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help with scheduling, planning contest materials, or organizing club communications, but offers little assistance for the actual supervisory and interpersonal work. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with scheduling, communication, and tracking logistics, the core supervision and coordination requires human judgment, conflict resolution, and real-time presence that cannot be fully automated. A human supervisor is essential for duty of care, discipline, and interpersonal dynamics. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical presence, live supervision of minors, and real-time interpersonal coordination that AI cannot perform end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal liability and duty of care are substantial barriers: teachers are required by law to supervise and safeguard students. Schools face negligence liability if injuries or misconduct occur under unsupervised conditions, creating hard organizational and legal barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Legal and safeguarding requirements mandate a responsible, often licensed adult supervising minors on-site, making AI substitution effectively prohibited. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for scheduling and communication are inexpensive, but they only address peripheral tasks, not the core supervision that requires human staffing. Meaningful cost savings would require replacement of the supervisor, which is not feasible. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute delivering this output, so any AI cost comparison is moot; the human is the only viable provider. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Current AI systems can handle scheduling and reminder emails but cannot reliably supervise activities, manage student behavior, or respond to emergencies. No production systems perform end-to-end supervision; tools remain administrative only. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product supervises or coordinates in-person extracurricular activities with students; this remains entirely a human function. |
Collaborate with other teachers and administrators in the development, evaluation, and revision of middle school programs.
9CI 7–11 · exposure 0 · augmentation 50 · importance 3.9/5 · click for rater detail
Collaborate with other teachers and administrators in the development, evaluation, and revision of middle school programs.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 education remains a slow-adopting sector with strong institutional resistance to algorithmic decision-making in curriculum matters. While some schools pilot data tools, actual displacement of collaborative program-development work is minimal and adoption remains in the pilot phase. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderate-to-slow adopter of AI tools for administrative collaboration; usage is mostly limited to drafting aids rather than embedded in program governance processes. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could usefully assist by summarizing stakeholder feedback, generating draft language for program documents, or flagging alignment issues with standards—helping educators work more efficiently. However, the core collaborative and deliberative work remains fundamentally human-driven. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help draft materials, summarize data, or suggest curriculum ideas that inform teacher/administrator discussions, providing useful but partial support to the collaborative process. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires substantive judgment about curriculum design, stakeholder input synthesis, and institutional decision-making. While AI can assist in drafting materials or summarizing feedback, the collaborative deliberation, negotiation, and final approval decisions are inherently human-centered and cannot be automated end-to-end at the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires in-person collaborative deliberation, institutional knowledge, and consensus-building among staff and administrators, which current AI cannot substitute for end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | School administrators and teachers are legally and professionally responsible for curriculum decisions; these cannot be delegated to AI without human oversight and final authority. Institutional governance structures, union considerations, and regulatory compliance requirements create substantial structural barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Program development decisions typically require professional judgment, institutional governance, and often administrative/school board approval, creating strong organizational and accountability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While AI could reduce the cost of document drafting or data compilation, the core task involves human deliberation and sign-off that cannot be meaningfully reduced. The all-in cost of AI assistance plus oversight would be comparable to or higher than the task cost itself. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since AI cannot perform the actual collaborative decision-making, there is no valid cost substitution; humans must still do the core work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform the full scope of collaborative program development, evaluation, and revision across multiple stakeholders. Current AI systems cannot independently conduct the iterative meetings, gather meaningful institutional consensus, or make binding programmatic decisions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs collaborative curriculum/program development and revision autonomously; AI at best supports document drafting within a human-led process. |
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 3.2/5 · 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 | School systems are traditionally slow to adopt AI in core operational and supervisory roles, with strong institutional and cultural resistance to removing human supervision from student-facing spaces. Budget constraints and regulatory conservatism limit experimentation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 in-person supervisory duties show essentially no AI adoption; schools remain a low-digitization, physical-presence-dependent environment for this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist marginally with attendance tracking, scheduling, or flagging unusual incidents in video feeds for human review, but these are peripheral to the core supervisory task. The human must remain the decision-maker and authority in student safety contexts. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers negligible assistance for hallway monitoring, bus loading, or cafeteria supervision, though minor library cataloging support could exist elsewhere it does not apply to this physical duty. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | These duties involve real-time supervision, physical presence, and dynamic human interaction (student behavior monitoring, safety) that resist full automation. While scheduling and some record-keeping could be automated, the core monitoring and oversight tasks require human judgment and responsiveness. |
| Task automatability | claude-sonnet-5 | 1/5 | This is physical presence and supervision of students in real-world spaces (halls, cafeteria, buses); AI cannot physically monitor or manage children in these settings.'},'rating explanation continues implicitly.'}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Schools have legal duty-of-care and student safety obligations that typically require human supervision; liability and potential legal challenges to automated-only monitoring create strong barriers. Parental and community expectations for human adult presence in supervisory roles are entrenched. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Child safety, supervision liability, and school policy/legal requirements mandate adult human presence for monitoring students in these physical contexts. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI solutions (cameras, sensors, integration with school systems) would be expensive to implement and maintain across multiple locations, while human monitors are already salaried staff. The all-in cost per task unit would exceed the marginal cost of human time. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute providing this physical supervisory 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 AI systems perform cafeteria monitoring, hall supervision, or bus loading in production today. Computer vision systems exist for object detection but lack the contextual judgment, legal authority, and liability coverage needed for genuine supervisory responsibility in schools. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical hall/cafeteria/bus supervision or library desk assistance for middle schoolers; this remains entirely human-executed. |
Guide and counsel students with adjustment or academic problems, or special academic interests.
6CI 4–9 · exposure 0 · augmentation 38 · importance 3.8/5 · click for rater detail
Guide and counsel students with adjustment or academic problems, or special academic interests.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools remain highly traditional; adoption of AI in student counseling is negligible. No meaningful deployment of AI agents for student guidance exists in production K–12 environments, and regulatory, liability, and professional-culture barriers keep adoption flat. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a slow-adopting sector for AI in interpersonal counseling roles, with pilots limited mostly to homework help, not personal guidance. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with administrative tasks like flagging at-risk students or organizing resources, but direct support for guidance and counseling would require human review that negates productivity gain. Any augmentation is marginal because the core task—listening, building rapport, and judgment—remains fundamentally human. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers by suggesting resources, drafting guidance materials, or flagging academic performance patterns, but the interpersonal counseling itself remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires deep understanding of individual student psychology, nuanced judgment about emotional and behavioral issues, and the ability to build trust—capabilities that current AI systems cannot reliably perform. The task is fundamentally relational and contextual, involving sensitive personal disclosure and adaptive support that falls well outside automation thresholds. |
| Task automatability | claude-sonnet-5 | 1/5 | Guiding and counseling students on personal adjustment or academic issues requires relational trust, in-person judgment, and emotional attunement that current AI cannot replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | School districts have legal and fiduciary duties to provide qualified, credentialed guidance staff; parents expect human counselors; and liability concerns around AI-driven mental health or academic intervention are severe. Most jurisdictions require school counselors to hold specific licenses or certifications, making automated substitution legally and organizationally impossible. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Schools have strong norms, safeguarding regulations, and in loco parentis responsibilities requiring a credentialed adult to handle student welfare and academic counseling. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Even if chatbot systems were deployed, they still require human oversight for risk management and appropriate escalation, and the total cost of integration, monitoring, and liability management approaches that of hiring guidance staff. The task's low-tech nature (conversation, listening, referral) keeps labor costs competitive. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Even if AI tools assist with some administrative aspects, the core counseling interaction still requires a human teacher's time, so cost savings are minimal for the actual task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs student counseling and guidance end-to-end in production. While chatbots exist, they lack the expertise, accountability, and human judgment necessary for meaningful intervention in academic or personal adjustment problems; they are not used by schools as replacements for guidance. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs student counseling autonomously in schools; chatbots exist for tutoring but not for adjustment/behavioral counseling at scale in production. |
Meet with other professionals to discuss individual students' needs and progress.
6CI 5–7 · exposure 0 · augmentation 38 · importance 3.7/5 · click for rater detail
Meet with other professionals to discuss individual students' needs and progress.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools are traditionally low-digitization, laggard sectors with strong human-contact requirements and organizational conservatism. Meeting structures remain human-centric with no measurable displacement by AI agents. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a comparatively slow-adopting sector for AI in interpersonal, judgment-heavy collaborative tasks, with pilots mostly focused on administrative support rather than meeting substitution. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide minor assistance through pre-meeting data summaries or post-meeting transcription, but the core collaborative dialogue cannot be meaningfully augmented by AI participation; the human professionals' interaction remains central. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by summarizing student data, drafting progress notes, or transcribing/synthesizing meeting discussions, meaningfully aiding preparation and follow-up even though it can't attend the meeting itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires human judgment, interpersonal dynamics, and collaborative decision-making about students' individual circumstances. AI cannot meaningfully replace the core function of professionals exchanging observations, resolving conflicting viewpoints, and reaching consensus on educational interventions. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires live, real-time human interaction, professional judgment, and relationship context that current AI cannot conduct on a teacher's behalf end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Professional and parental expectations strongly favor human educators making joint decisions about students' welfare, and educator licensing/professional judgment requirements create meaningful friction against substitution. Schools operate under legal and custodial frameworks that presume human professional accountability. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Such meetings often involve confidential student records, IEP/504 compliance, and professional accountability requiring a credentialed teacher's direct participation and judgment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Even if AI could generate meeting notes or agendas, the meeting itself—requiring human educators and specialists physically or synchronously present—cannot be cost-reduced by AI in any meaningful way. The human time remains necessary. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task, so cost comparison favors the human by default; AI note-taking tools only marginally support rather than replace it. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably participates in multidisciplinary professional meetings to discuss and decide on student interventions. AI systems cannot authentically contribute to collaborative human dialogue about individualized needs in production settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a teacher attending and contributing to interdisciplinary meetings about specific students today. |
Attend professional meetings, educational conferences, and teacher training workshops to maintain and improve professional competence.
6CI 5–7 · exposure 0 · augmentation 38 · importance 3.3/5 · click for rater detail
Attend professional meetings, educational conferences, and teacher training workshops to maintain and improve professional competence.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task is deeply rooted in human professional norms and institutional requirements; there is no meaningful trend toward AI adoption because the task is inherently human-bound. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a slower-adopting sector for AI generally, and this specific task involves in-person professional obligations largely untouched by AI tools. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist minimally by summarizing conference materials or organizing notes post-attendance, but the core task of attending and engaging with professional development cannot be augmented by AI in any material way. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help summarize conference content, suggest relevant sessions, or help teachers process and apply training materials afterward, though it doesn't replace attendance itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Attending meetings, conferences, and workshops inherently requires human physical or synchronous presence and real-time interpersonal engagement. No AI system can meaningfully substitute for a teacher's actual participation in professional development. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical/social attendance and professional development engagement cannot be executed by AI on the teacher's behalf; this is inherently a human participatory activity.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Professional development requirements are often mandated by school districts and certification bodies, creating regulatory and institutional barriers that require documented human attendance and participation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Certification renewal and licensure requirements in many districts mandate documented human attendance at professional development, creating structural barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI has no role in replacing the attendance cost (salary, travel, registration); there is no AI alternative pathway to accomplish this task, making cost comparison moot. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task, so no meaningful cost comparison applies; the human must attend. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can attend meetings or conferences in a human's stead; this task fundamentally involves human presence and interaction, which lies entirely outside the scope of current automation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a human attending and engaging in conferences or workshops for professional growth. |
Supervise, evaluate, and plan assignments for teacher assistants and volunteers.
5CI 5–5 · exposure 0 · augmentation 25 · importance 3.1/5 · click for rater detail
Supervise, evaluate, and plan assignments for teacher assistants and volunteers.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools are highly regulated, risk-averse environments with strong labor protections and limited digitization of personnel management. Adoption of AI for staff supervision remains negligible in K–12 education. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a slow-adopting sector for AI in managerial/HR-type functions, with minimal production deployment of AI for staff supervision. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially draft scheduling suggestions or flag patterns in assistant attendance or task completion, but the core supervisory and evaluative judgment must remain with the human teacher, limiting augmentation impact. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help draft schedules, assignment plans, or performance notes, but this offers only partial assistance not integral to the actual supervisory judgment task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires human judgment about personnel performance, development planning, and real-time supervision of individuals. Current AI cannot meaningfully evaluate teaching assistant performance, provide constructive feedback tied to professional growth, or make decisions about task assignments without human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | Supervising and evaluating human staff and volunteers requires interpersonal judgment, relationship management, and accountability that current AI cannot perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Employment law, duty-of-care requirements, and union agreements in most jurisdictions require a licensed educator or designated administrator to conduct formal evaluations and personnel decisions. Liability and legal standing create hard barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | School policies and employment law typically require a designated, accountable staff member (the teacher) to supervise and evaluate other staff, creating strong organizational and legal barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The overhead of AI systems, integration, and required human review and sign-off would exceed the time cost of a teacher or administrator directly supervising and evaluating assistants, making AI substantially more expensive all-in. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this managerial task, so cost comparison favors the human entirely. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform end-to-end supervision and evaluation of human staff. While AI can log activities or draft notes, actual personnel management—including evaluation fairness, bias detection, and judgment calls—remains non-automated in practice. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product manages personnel supervision or performance evaluation of school staff autonomously; this remains a human managerial function. |
Instruct and monitor students in the use and care of equipment and materials to prevent injury and damage.
4CI 0–9 · exposure 8 · augmentation 25 · importance 3.7/5 · click for rater detail
Instruct and monitor students in the use and care of equipment and materials to prevent injury and damage.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools are slow to adopt AI systems in general due to budgets, data privacy concerns, and conservative governance. The safety-critical nature of equipment supervision makes adoption even slower; risk-averse administrators are unlikely to pilot automation in injury-prevention contexts. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a slow-adopting sector for physical supervisory tasks, and there is no trend toward replacing in-person safety monitoring with AI. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by generating pre-class safety reminders, analyzing near-miss video clips offline, or providing instructional templates, but it offers limited real-time augmentation during active equipment use and monitoring. The teacher remains entirely responsible for on-the-spot risk detection and intervention. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help generate safety instructions, checklists, or training materials in advance, but it offers little real-time assistance during actual hands-on supervision. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI could potentially create instructional materials or generate safety guidelines, but real-time monitoring of physical classroom equipment use, assessing individual student competence, and intervening to prevent injury requires constant in-person observation and judgment that current AI systems cannot deliver. The safety-critical nature of the task severely limits automation potential. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires live physical supervision of students with equipment in real time, which current AI cannot perform end-to-end; no time-saving substitution is possible for the in-person monitoring component. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Teachers have a legal duty of care to protect students from injury; liability falls directly on the educator and institution. Most jurisdictions require a licensed, present educator to supervise hands-on activities, and parental and institutional expectations demand human judgment and accountability for student safety. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Legal and duty-of-care requirements mandate a certified, physically present teacher to supervise minors around equipment, making this a hard institutional and liability barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Setting up comprehensive AI monitoring systems with cameras, hardware, and integration infrastructure would exceed the cost of teacher labor, especially considering the overhead of oversight and verification needed to ensure student safety in this high-liability context. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that can substitute for a teacher's physical presence and safety oversight, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs real-time in-classroom equipment monitoring and injury prevention. While AI-enabled camera systems exist in research settings, they lack the real-world reliability, legal clearance, and integration with classroom workflows needed for production deployment in schools. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product provides physical classroom supervision or real-time safety monitoring of students handling materials; this remains firmly in the human domain. |
Observe and evaluate students' performance, behavior, social development, and physical health.
4CI 0–7 · exposure 5 · augmentation 25 · importance 4.5/5 · click for rater detail
Observe and evaluate students' performance, behavior, social development, and physical health.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Despite some vendor activity in learning analytics, actual production adoption of AI-driven student observation and evaluation remains minimal in K–12 schools. Budget constraints, privacy concerns, regulatory scrutiny, and educator resistance keep adoption in the pilot phase at most. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderately slow-adopting sector for AI in core pedagogical and student-monitoring functions, constrained by privacy, safeguarding, and institutional caution. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can provide modest assistance by aggregating test scores or flagging statistical outliers in attendance or grades, but current systems offer little augmentation for the core human task of evaluating behavior, social development, and physical health. Teachers remain the primary evaluator with limited AI support for data summarization. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help track academic performance data or flag patterns (e.g., attendance, grades) but offers limited assistance for observing behavior, social development, and physical health directly. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Observing and evaluating student performance, behavior, social development, and physical health requires nuanced human judgment, contextual understanding, and real-time interaction that current AI cannot perform end-to-end. While AI can assist with data analysis of test scores, it cannot replicate the holistic assessment of social-emotional development and behavioral patterns that require direct classroom observation and professional educator judgment. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time in-person observation of children's behavior, social dynamics, and physical wellbeing in a classroom, which AI cannot perceive or judge holistically today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task is protected by multiple hard barriers: educators hold professional licenses and legal responsibility for student welfare and assessment; liability for errors in behavioral or health evaluation is high; FERPA and state education regulations mandate human accountability; and parents and institutions strongly prefer human professional judgment on matters affecting student wellbeing and development. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Teaching requires licensed, in-person professionals with duty-of-care and child-safeguarding responsibilities, and evaluating student welfare has strong legal/ethical requirements for human judgment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The all-in cost of AI systems (data infrastructure, labeling, model maintenance, human oversight of false positives) exceeds the cost of teacher time spent directly observing students, especially given the low hourly cost of teacher professional judgment during instructional time. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this holistic in-person evaluation, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs this task independently. While learning analytics platforms can flag test performance anomalies and some classroom monitoring tools exist, they are narrow, error-prone, and require substantial human validation. No production system can comprehensively evaluate behavior, social development, and physical health without human oversight. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product observes and evaluates student behavior, social development, and physical health in situ; this remains a human teacher function. |
Confer with parents or guardians, other teachers, counselors, and administrators to resolve students' behavioral and academic problems.
4CI 0–7 · exposure 0 · augmentation 38 · importance 4.1/5 · click for rater detail
Confer with parents or guardians, other teachers, counselors, and administrators to resolve students' behavioral and academic problems.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools operate in low-digitization, human-contact-required sectors with strong professional norms. There is minimal sector-wide adoption of AI for stakeholder conferencing, and no measurable displacement in this domain. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education adopts AI tools slowly and unevenly, especially for sensitive interpersonal functions like behavioral conferences, which remain largely untouched by automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might assist with generating summary notes from prior student records or suggesting discussion points before a conference, but these are marginal aids. The core task—conferring with parents, counselors, and administrators—requires direct human engagement and cannot be substantially augmented by AI. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers prepare talking points, summarize behavioral data, draft follow-up communications, or suggest intervention strategies, improving efficiency of preparation even though the human conducts the conference. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires human interpersonal judgment, emotional intelligence, and contextual understanding of individual student circumstances. Current AI systems cannot meaningfully replace the nuanced communication, trust-building, and consensus-forming that conferencing with multiple stakeholders demands. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a live interpersonal negotiation requiring relationship management, empathy, and real-time judgment about a specific child; AI cannot conduct these conferences end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal, professional, and organizational barriers protect this task: teachers are licensed professionals, parents expect human contact with educators, liability for student welfare decisions rests on human judgment, and school policies mandate direct human-to-human conferencing for behavioral and academic matters. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Schools require teachers/administrators to be personally accountable for student welfare decisions, and parents expect direct human accountability and trust, creating strong institutional and relational barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI has no substitute cost advantage here since the task cannot be automated; human teachers must conduct these conferences regardless, and any AI tools would only add cost without displacement. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this human conference, so cost comparison favors the human by default since AI cannot deliver the output. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can reliably conduct parent-teacher conferences, mediate among diverse stakeholders, or synthesize solutions to behavioral/academic problems in real school settings. This remains entirely in the human domain in practice. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product conducts parent-teacher conferences or multi-party behavioral problem-solving meetings autonomously; this remains outside current product scope. |
Prepare students for later grades by encouraging them to explore learning opportunities and to persevere with challenging tasks.
4CI 0–7 · exposure 0 · augmentation 38 · importance 4.0/5 · click for rater detail
Prepare students for later grades by encouraging them to explore learning opportunities and to persevere with challenging tasks.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools remain highly conservative about outsourcing motivational and developmental guidance, and digitization is lower than white-collar sectors. Teachers are not being displaced by AI for motivational and exploratory learning functions in practice. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education adopts AI tools slowly and unevenly, with pilots for tutoring or content generation but very limited penetration into core motivational and student-development roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools can assist teachers by suggesting learning resources or tracking student engagement data, but these remain marginal augmentations to the core human work of building student motivation and perseverance. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can support teachers with personalized learning recommendations, progress tracking, and content suggestions that help them encourage exploration and target challenging tasks for individual students, augmenting but not replacing their motivational role. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves motivating students and fostering long-term learning dispositions—inherently relational, contextual, and dependent on human emotional intelligence. Current AI cannot autonomously create the persistent, personalized encouragement and accountability relationships needed to develop student perseverance and self-directed exploration. |
| Task automatability | claude-sonnet-5 | 1/5 | This task is fundamentally about relational mentorship, motivation, and modeling perseverance to adolescents in a classroom context, which requires sustained human presence and judgment that current AI cannot replicate end-to-end.//No off-the-shelf system can autonomously perform this ongoing socio-emotional guidance role. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Teachers must be licensed professionals; there is a legal and ethical requirement that a qualified human educator direct student learning and motivation. Parental and institutional expectations also mandate human judgment in developmental guidance. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Teaching middle schoolers involves in loco parentis responsibilities, licensing requirements, and strong parental/institutional expectations of human mentorship, creating significant structural and trust barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying AI systems to provide meaningful, ongoing motivation and opportunity exploration for individual students (including oversight and integration) would exceed the salary cost of a human teacher per student per unit of genuine behavioral and dispositional change. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since no AI system can perform this task independently, there is no viable AI-only cost basis to compare against the human teacher's wage for this specific relational task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs autonomous student motivation and perseverance-building at scale. Conversational AI can provide tutoring content, but cannot replicate the trust, feedback loops, and adaptive emotional support that constitute this task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product independently motivates and coaches middle schoolers toward perseverance and exploration of learning opportunities; existing edtech tools are supplementary, not autonomous substitutes for the teacher's relational role. |
Attend staff meetings and serve on staff committees, as required.
3CI 0–5 · exposure 0 · augmentation 38 · importance 3.5/5 · click for rater detail
Attend staff meetings and serve on staff committees, as required.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | No adoption of AI for this task has occurred in schools because the task is fundamentally tied to human presence and participation, which cannot be delegated to current systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a slow-adopting sector generally, and this specific in-person collaborative task shows essentially no AI displacement or adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide modest assistance by drafting agendas or summarizing minutes, but the core task—human attendance and participation—cannot be augmented meaningfully by current AI systems. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers prepare meeting notes, summarize agendas, draft committee reports, or take/transcribe minutes, improving efficiency around the task even though it cannot replace attendance. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Attending meetings and serving on committees requires active participation, interpersonal presence, and decision-making that fundamentally depend on human judgment and real-time interaction. Current AI cannot meaningfully replace the human's active presence or voting/consensus role in these settings. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical attendance and active participation in staff meetings and committee work requires human presence, judgment, and social engagement that AI cannot substitute for today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Schools explicitly require staff member presence at meetings and committee participation as a formal job duty and contractual obligation. Legal and organizational requirements mandate that an actual licensed teacher, not an AI agent, fulfill this role. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Institutional and professional norms require the actual employee's participation in governance and collegial decision-making; delegation to AI is not organizationally or professionally acceptable. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | There is no meaningful AI alternative to replace human attendance, so cost comparison is not applicable; the task must still be performed by the teacher at their full loaded wage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this task, so cost comparison is moot; a human must be present regardless of AI cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can reliably attend meetings on behalf of a teacher, participate authentically in committee work, or fulfill the organizational requirement for human presence. This remains entirely in the realm of human-only tasks. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product allows an AI to attend and represent a teacher in staff meetings or serve on committees in their place. |
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 | Classroom behavior management is fundamentally human-facing and relational; education remains a low-automation sector for pedagogical and legal reasons. No meaningful AI adoption is occurring for direct classroom discipline enforcement. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 classroom management is a low-digitization, physically-present function with essentially no AI adoption for direct behavioral enforcement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist marginally with tracking attendance, flagging written violations, or suggesting data-driven interventions, but it offers limited value to the core task of real-time behavioral management and relationship-based enforcement. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help draft classroom rule policies or analyze behavior trend data, but it offers minimal real-time assistance for the moment-to-moment task of enforcing order among students. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Establishing and enforcing behavioral rules requires real-time judgment, emotional intelligence, relationship-building, and contextual decision-making that current AI cannot perform autonomously. AI lacks the social presence and authority necessary to genuinely enforce behavioral standards in a classroom setting. |
| Task automatability | claude-sonnet-5 | 1/5 | Establishing authority, enforcing behavioral rules, and maintaining classroom order in real time requires physical presence, social authority, and in-person judgment that current AI cannot replicate or execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Teachers have legal and professional responsibility for student safety and welfare; classroom management requires in-person authority and duty of care that cannot be delegated to AI. Schools would face liability and regulatory issues attempting to automate classroom discipline. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Supervision of minors, school safety, and disciplinary authority are governed by legal/institutional requirements mandating certified human staff be responsible for student behavior and safety. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Any feasible AI system (monitoring cameras, sentiment analysis, incident logging) would require significant overhead infrastructure and human oversight, making it more expensive than human teachers who already manage behavior as part of their role. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task, so cost comparison favors the human by default since AI cannot deliver the output at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs classroom behavioral management and rule enforcement. While AI can flag policy violations in text or flagged student work, it cannot manage live classroom conduct or enforce rules in real-time interaction. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product manages live classroom discipline or behavioral enforcement; this remains an inherently human, in-person supervisory function. |
Enforce all administration policies and rules governing students.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.1/5 · 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, particularly K–12, has lagged in AI adoption. Student discipline and policy enforcement are high-touch, trust-dependent functions schools will not automate given liability and community resistance. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a low-digitization sector for behavioral/disciplinary functions, with essentially no AI adoption for enforcement of conduct rules. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist by flagging policy violations or suggesting precedents, but enforcement itself demands human judgment, authority, and presence; tools for documentation or flagging violations offer limited productivity gain. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help track infractions, generate reports, or flag patterns in student behavior data, but offers minimal assistance in the actual act of enforcing rules with students. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Enforcing policies requires contextual judgment, student relationship awareness, and dynamic decision-making about proportionality and individual circumstances. Current AI systems cannot navigate the social and behavioral complexity needed to enforce rules fairly and effectively. |
| Task automatability | claude-sonnet-5 | 1/5 | Enforcing rules requires in-person authority, real-time behavioral judgment, and physical presence in a classroom, none of which AI can execute end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | School administration policies are legally enforced by credentialed staff; teachers hold legal authority to manage student conduct. Liability for wrongful punishment, discrimination, and due-process requirements all mandate human decision-making and accountability. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Student discipline and policy enforcement involve legal authority, in loco parentis responsibility, and liability that require a certified human educator or administrator. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | An AI system would require substantial oversight, integration with school management systems, and human review of every enforcement decision, making it more expensive than direct teacher enforcement. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task, so cost comparison favors the human by default since AI cannot deliver the output at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably enforces school policies independently. This task inherently requires human authority, presence, and the ability to make nuanced disciplinary decisions that schools cannot delegate to systems today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs disciplinary enforcement or policy compliance oversight of students; this remains entirely a human administrative/teaching function. |
Provide students with disabilities with assistive devices, supportive technology, and assistance accessing facilities, such as restrooms.
0CI 0–0 · exposure 0 · augmentation 25 · importance 3.9/5 · 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 | Schools are laggard adopters for tasks involving direct student care and physical assistance. No meaningful adoption of AI for this specific task has occurred, as it involves both physical presence and duty of care that schools cannot yet delegate. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education support roles involving physical assistance show essentially no AI adoption, as the task is inherently physical and safety-sensitive. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might assist with administrative tracking of assistive devices or scheduling facility access, but it offers minimal help for the core task of providing hands-on support and physical assistance to students with disabilities in real-time. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Assistive technology (e.g., communication devices, adaptive software) can support the broader goal of accessibility, but AI itself offers minimal augmentation to the specific act of physically assisting a student to a restroom or facility. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires in-person physical assistance, direct interpersonal interaction, and judgment about individual student needs that AI cannot provide. Current AI systems cannot physically hand objects to students, accompany them to facilities, or provide the real-time adaptive support this task demands. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical presence, hands-on assistance, and real-time responsiveness to a student's physical needs, none of which current AI systems can perform. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and regulatory barriers apply: educators must comply with IDEA and ADA requirements, which mandate qualified human staff to provide accommodations and accessibility support. Liability for improper assistance is high, and human presence is a statutory requirement. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Special education law (IDEA, Section 504), safeguarding requirements, and the physical/legal duty of care mandate qualified human staff for direct physical assistance with students with disabilities. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI has no meaningful role in this task today, making cost comparison inapplicable. A human educator must be physically present and engaged, so AI cannot substitute or reduce the cost per unit of service delivered. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI has no viable mechanism to substitute for this physical, in-person assistance, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can autonomously perform the physical and relational components of this task in a school environment. The task requires embodied presence, human judgment, and direct care—well beyond current AI capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product provides physical assistance to students with disabilities or helps them access facilities like restrooms; this remains entirely a human physical-care task. |
Organize and supervise games and other recreational activities to promote physical, mental, and social development.
0CI 0–0 · exposure 0 · augmentation 25 · importance 3.2/5 · click for rater detail
Organize and supervise games and other recreational activities to promote physical, mental, and social development.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | School districts cannot and do not automate supervised recreational activities for minors; this remains a fundamentally human-staffed function driven by regulatory and safety mandates rather than cost optimization. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially physical/recreational supervision, shows minimal AI adoption for this kind of hands-on, safety-critical activity. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist with pre-planning (activity databases, scheduling tools), but offers minimal productivity gain for the actual supervision and social-emotional facilitation during activities themselves. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI might help plan game ideas or activity schedules in advance, but offers negligible assistance during the actual supervision and execution of the activities. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time supervision of children, dynamic behavior management, and responsiveness to emergent social/physical situations that demand human judgment and presence. AI cannot meaningfully replace the core function of on-site supervision and relationship-building. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical presence, real-time supervision of children's safety, and hands-on facilitation of games and physical activities that AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal duty of care, mandatory staff-to-student ratios, liability requirements, and child safety regulations create hard barriers to automation. A licensed educator or authorized supervisor must be physically present and responsible. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Direct in-person supervision of minors during physical activity carries strong duty-of-care, safety, and liability requirements that mandate qualified human staff presence. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A human teacher's presence is legally and practically required for student supervision and safety. AI tools offer no cost substitution for the fundamental requirement of an adult supervisor on-site during recreation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute delivering this output, so any AI cost is irrelevant compared to the necessary human labor cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can autonomously organize and supervise recreational activities with children in a school setting. This requires physical presence, safety accountability, and interpersonal facilitation that current AI systems cannot perform. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product supervises or organizes in-person recreational activities for students; this remains entirely a human physical-presence task. |
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.