Elementary School Teachers, Except Special Education
25-2021.00Median wage $63,970/yr1,388,390 employed (US)Rank #735 of 923 scored · top 80% by substitution
Teach academic and social skills to students at the elementary school level.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution17
Exposure16
Augmentation49
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
38 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%15
panel mean rating 1.6/5 → substitution pressure 15/100
Technical feasibility todayw 20%17
panel mean rating 1.7/5 → substitution pressure 17/100
Cost vs. human wagew 15%19
panel mean rating 1.8/5 → substitution pressure 19/100
Adoption barriersw 20%inverted — strong barriers lower the score23
panel mean rating 4.1/5 (barrier strength) → substitution pressure 23/100
Sector adoption velocityw 10%14
panel mean rating 1.6/5 → substitution pressure 14/100
Task breakdown (38 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.
Prepare for assigned classes and show written evidence of preparation upon request of immediate supervisors.
73CI 59–87 · exposure 70 · augmentation 100 · importance 4.1/5 · click for rater detail
Prepare for assigned classes and show written evidence of preparation upon request of immediate supervisors.
73| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Education is a highly digitized sector with rapid adoption of AI tools; many teachers already use ChatGPT and similar systems for lesson planning in practice, and school districts are increasingly enabling or permitting these workflows. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | K-12 education is a moderate-adoption sector; many teachers use AI tools for planning but institutional policies, training gaps, and district caution keep adoption uneven and often informal rather than fully integrated. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically augments teacher productivity by generating first drafts, differentiated materials, and assessment tools in seconds, freeing the teacher to refine and adapt rather than start from blank pages, transforming the speed and scope of preparation. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI is widely and effectively used by teachers to accelerate drafting of lesson plans, worksheets, and prep documentation while the teacher retains responsibility for final content and delivery. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | AI can generate comprehensive lesson plans, learning objectives, assessment rubrics, and materials at scale with minimal human input, easily meeting the ≥50% time-saving threshold. A teacher can use AI to draft structured preparation documents that supervisors would accept as evidence of planning. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft lesson plans, generate materials, and align content to standards, saving significant time, but teachers must still customize plans to their specific students, curriculum pacing, and classroom context and provide documented evidence of that personalized effort. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While schools informally expect human judgment in lesson design and may prefer teacher autonomy, there are no legal or contractual barriers preventing AI-generated preparation materials from being submitted as evidence of compliance. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier prevents using AI to help prepare lesson content, though school policies, curriculum standards, and the requirement that the teacher of record be accountable for what's taught create some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI inference and document generation cost mere cents per lesson plan, while a teacher's hourly wage for preparation work (often unpaid or bundled) is far more expensive, yielding at least a 10× cost advantage for the AI. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-assisted lesson plan drafting is extremely cheap compared to the teacher time it would otherwise take to prepare materials from scratch, even accounting for review and editing. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products (ChatGPT, Claude, specialized education platforms) reliably generate lesson plans and teaching materials in production; however, some jurisdictions may require human oversight or customization for compliance with local curricula, preventing a full 5. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like lesson-planning AI tools and chatbots are used by teachers today, but they generate drafts requiring review, adaptation, and formatting into whatever documentation format the supervisor requires. |
Assign and grade class work and homework.
61CI 48–74 · exposure 62 · augmentation 88 · importance 4.3/5 · click for rater detail
Assign and grade class work and homework.
61| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Elementary schools are more conservative than higher-ed institutions on AI grading; adoption remains largely limited to pilots and supplementary use in tech-forward districts. Most elementary teachers still grade manually or use basic LMS auto-graders, reflecting slow sector-wide adoption. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a comparatively slow-adopting sector due to budget constraints, teacher training gaps, and privacy/policy caution, though tools like Google Classroom and Canva are being piloted increasingly. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI significantly augments teachers by instantly grading routine assignments, providing instant feedback to students, and freeing teacher time for individualized instruction and higher-order feedback on complex work, while the teacher remains the decision-maker on grades and learning pathways. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI meaningfully assists teachers by auto-generating assignments, providing rubric-based grading suggestions, and flagging patterns in student errors, substantially speeding up the grading workflow while the teacher remains the final decision-maker. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can automatically grade objective questions (multiple choice, true/false, fill-in-the-blank) and provide detailed feedback on many subjective assignments (essays, math work) with minimal setup, easily meeting the 50% time-saving threshold. However, grading still requires teacher oversight for borderline cases, partial credit judgments, and contextual assessment of student understanding, preventing a full 5 rating. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can grade objective assignments (math problems, multiple-choice, short factual answers) and generate assignment sets, but grading nuanced written work or assessing understanding for young children still requires human judgment and contextual knowledge of the student. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Teachers are not legally required to use only human grading, but institutional policies, union concerns about job displacement, parental expectations for human feedback, and organizational adoption friction create moderate adoption friction. Professional norms still favor human judgment in elementary education. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No formal licensing requirement mandates a human grade homework, but school policies, parent expectations, and FERPA-related data privacy concerns create some friction around uploading student work to third-party AI tools. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI grading infrastructure has very low per-task inference costs (fractions of a cent per assignment) and can be integrated into existing LMS platforms, making it orders of magnitude cheaper than the loaded wage of a teacher spending 1–2 hours per night on grading. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI grading tools are cheap per assignment, but the need for teacher oversight, calibration, and handling exceptions keeps overall cost savings moderate rather than order-of-magnitude cheaper for a full class workflow. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products (LMS systems with auto-grading, AI essay evaluation tools, math homework checkers) reliably handle structured grading at scale in K–12 environments. Some subjectivity still requires human review, and integration varies, but these systems are in active production use across thousands of schools. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Gradescope, Google Classroom's AI features, and various AI graders exist and are used in schools, but they work best on structured content and still require teacher review, especially at the elementary level where handwriting and developmental variability complicate automated grading. |
Prepare, administer, and grade tests and assignments to evaluate students' progress.
41CI 30–51 · exposure 42 · augmentation 75 · importance 4.3/5 · click for rater detail
Prepare, administer, and grade tests and assignments to evaluate students' progress.
41| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 education is a laggard sector in AI adoption; most schools use basic digital tools rather than AI-driven assessment, and cultural resistance to automated grading of subjective work remains high. Meaningful displacement in this domain has not yet materialized at scale. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education, especially elementary levels, has historically slow, uneven AI adoption due to budget constraints, oversight concerns, and administrative caution, though pilots are growing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist teachers by auto-grading objective components, suggesting rubric application, flagging outliers, and generating item analysis—freeing time for human review and feedback on complex work. This assistive role is already deployed in many schools and raises productivity without removing the teacher from decision-making. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools substantially help teachers draft assignments, generate practice tests, and pre-grade objective items, freeing time for more personalized feedback and instruction. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate test questions and automatically grade multiple-choice or fill-in-the-blank assignments, preparing and administering assessments that fit a specific curriculum and evaluating open-ended student work still requires substantial human judgment about learning objectives, appropriateness, and interpretation of student understanding. End-to-end replacement with 50% time savings at equal quality is not achievable today. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft tests, grade multiple-choice/short-answer items, and even assess essays with rubrics, but grading young children's work often requires nuanced judgment of handwriting, partial understanding, and developmental context that current tools handle imperfectly. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: teachers are licensed professionals whose role includes evaluative judgment; schools have accountability requirements under education law; parents expect human oversight of grading; and institutional inertia around assessment practices is strong. Regulatory and professional standards create hard friction against full substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement mandates a human grade tests, but final grades typically require teacher sign-off, and parental/institutional trust favors teacher-administered assessment for young children. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Grade-book platforms and basic auto-graders reduce overhead, but per-task cost comparison remains close to neutral; schools pay for licensing and infrastructure, while teacher time saved is modest for subjective assessment components. The loaded cost of human teachers remains lower when factoring in oversight requirements. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated test creation and objective grading is extremely cheap compared to teacher time spent on repetitive grading tasks, though oversight costs reduce the full order-of-magnitude gain. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist for automatic grading of objective tests (Canvas, Blackboard, Google Classroom) and AI-assisted rubric application, but these have material limitations on complex student work and require human review. Reliable fully-automated assessment of essays, projects, and problem-solving remains unreliable enough that teachers must validate results. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like AI-assisted grading platforms and quiz generators (e.g., Google Forms auto-grade, various edtech tools) are used in production, but reliable grading of open-ended elementary-level work still requires teacher review. |
Prepare objectives and outlines for courses of study, following curriculum guidelines or requirements of states and schools.
41CI 25–56 · exposure 38 · augmentation 75 · importance 4.2/5 · 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 | School systems have historically lagged in AI adoption due to budget constraints, risk aversion around student-facing tools, and labor agreement protections. While pilot use of AI drafting assistants is emerging, production-level adoption remains limited in most districts. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a sector with historically slow, uneven AI adoption due to budget constraints, policy caution, and mixed digitization levels, though some pilots for lesson planning tools are emerging. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist teachers by generating initial outlines, suggesting learning objectives aligned with standards, or providing templates that reduce starting-from-scratch time. However, the augmentation is partial since teachers must still validate, customize, and take full accountability for the curriculum product. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI tools substantially speed up drafting of objectives and outlines aligned to standards, letting teachers focus on customization and quality review, a strong augmentation use case. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate draft course outlines and learning objectives based on curriculum standards, the task requires deep knowledge of specific student populations, state regulations, and pedagogical judgment that AI cannot reliably replicate end-to-end. Human review and modification are essential, limiting time savings well below the 50% threshold. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft curriculum objectives and outlines aligned to standards quickly, but teachers must still verify alignment, adapt to specific class needs, and integrate local requirements, limiting full end-to-end automation.the task itself is largely generative/textual though.rating 3 reflects partial but substantial automation potential. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Curriculum preparation for schools typically requires adherence to state educational standards and district policies, and often needs principal or curriculum coordinator approval. Teachers bear professional responsibility for alignment with legal educational requirements, creating meaningful organizational and legal friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human write curriculum outlines, though schools typically require certified teacher oversight and approval of final curriculum content. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The teacher time required to review, validate, and correct AI-generated outlines against curriculum standards and institutional requirements often approaches or exceeds the cost of having the teacher prepare the outlines directly, especially when accounting for integration overhead. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating draft outlines via AI is extremely cheap compared to the teacher time required to research standards and write objectives from scratch, though some human review time still adds cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI tools can produce outline templates and objective suggestions, but deployed products lack reliable integration with diverse state curriculum requirements and school-specific contexts. Products that exist (e.g., ChatGPT for drafting) produce inconsistent results requiring substantial human oversight and rework. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like curriculum-generation tools and LLM-based lesson planners exist and are used by some teachers, but they are not universally deployed or verified reliable across all states' standards and grade-specific nuances. |
Establish clear objectives for all lessons, units, and projects and communicate those objectives to students.
39CI 34–45 · exposure 34 · augmentation 75 · importance 4.4/5 · click for rater detail
Establish clear objectives for all lessons, units, and projects and communicate those objectives to students.
39| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 education shows slower AI adoption than information-sector professions, with significant resistance from unions, regulatory caution, and institutional inertia. AI writing assistants exist but are not yet standard practice in most districts. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | K-12 education is adopting AI lesson-planning tools at a moderate pace, with growing pilot programs but not yet deep, universal integration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist teachers by generating initial objective drafts aligned to standards, offering multiple phrasings, and suggesting scaffolding—substantially raising productivity while the teacher retains judgment and final authority over objectives. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools substantially speed up drafting clear, well-structured objectives aligned to standards, letting teachers focus more time on delivery and differentiation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can draft learning objectives and lesson frameworks quickly, but establishing *clear* objectives requires understanding of specific student populations, curriculum standards, and pedagogical context. Current AI systems lack reliable judgment about what students will actually comprehend, making end-to-end automation with quality parity difficult. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can draft learning objectives aligned to standards quickly, but the human task also involves communicating and adapting objectives to a specific classroom in real time, which requires in-person judgment AI cannot fully replace. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teachers are legally and professionally responsible for curriculum and learning outcomes; educational standards and institutional policies require human educator sign-off on instructional objectives. Liability and accountability requirements are substantial. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement blocks AI-assisted planning, but curriculum standards, school oversight, and the need for a certified teacher to actually teach create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI inference costs for generating objectives and communicative materials are negligible compared to the teacher's hourly wage, making the cost ratio strongly favorable once integrated into existing workflows. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting tools are cheap for the planning component, but overall cost is comparable since a paid teacher must still deliver and adapt objectives live in class. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI writing tools (GPT-based systems, specialized EdTech platforms) demonstrably generate lesson objectives and materials, but they require substantial teacher review and refinement to ensure appropriateness and alignment with standards. Production use exists but remains teacher-supervised rather than autonomous. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Lesson-planning tools (e.g., Khanmigo, Curipod, MagicSchool) reliably generate standards-aligned objectives and are used in production by teachers, but the classroom communication portion is not automated by any product. |
Prepare reports on students and activities as required by administration.
36CI 25–48 · exposure 38 · augmentation 63 · importance 3.7/5 · click for rater detail
Prepare reports on students and activities as required by administration.
36| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 education remains a laggard sector in AI adoption; most schools lack systematic AI deployment infrastructure and prioritize teacher autonomy and accountability. Pilots of automated reporting exist but have not achieved production-scale displacement in the majority of districts. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a relatively slow-adopting sector for AI tools compared to finance or tech, with uneven district-level policies and infrastructure. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by suggesting observation summaries, auto-populating boilerplate sections, and organizing classroom data into draft outlines, reducing teacher reporting time. However, the value is moderate because teacher judgment dominates the task; augmentation is most useful for routine sections rather than the insight-heavy parts. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI writing assistants can meaningfully speed up drafting of narrative comments and summarizing data trends, letting teachers focus on personalization and accuracy checks. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can draft basic progress notes and generate templated activity summaries from raw data or observations, but teacher reports require subjective judgment about student development, behavioral context, and individualized insights that resist full automation. Current systems cannot reliably replace the interpretive and narrative synthesis that makes teacher reports actionable for administrators and parents. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft report language, summarize data, and generate templated narratives from grades/attendance, but teachers must supply accurate underlying data and verify content, so only partial time savings are realized end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teacher reports carry accountability weight: they inform parent conferences, special education referrals, and administrative decisions about student placement. Schools have strong organizational norms expecting teacher authorship and liability concerns about delegating documented statements to AI, even with review. Regulatory compliance and institutional trust create friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically for report writing, but school policies, FERPA/privacy considerations, and administrative sign-off create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI infrastructure for report generation (API calls, model inference, oversight) is inexpensive in isolation, but the teacher's time is already embedded in salary. The marginal cost savings are modest because teachers must still observe, reflect, and review outputs—the labor bottleneck is judgment, not typing. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting tools are cheap per use, but the need for teacher review, data entry, and integration with school systems keeps overall cost savings moderate rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some narrowly scoped report-generation products exist (attendance summaries, grade exports), but no mainstream system reliably produces the qualitative, contextual student assessments that define most teacher reporting. Deployed tools handle only structured data export, not the synthesis of observations into coherent narratives. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like AI writing assistants and gradebook-integrated tools (e.g., report card comment generators) are used in schools today, but adoption is uneven and human review is still standard practice. |
Maintain accurate and complete student records as required by laws, district policies, and administrative regulations.
34CI 25–43 · exposure 38 · augmentation 75 · importance 4.3/5 · click for rater detail
Maintain accurate and complete 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 | Education is a laggard sector for AI adoption; most districts use legacy systems and manual processes. While some schools pilot automated attendance and grade-logging, end-to-end record management automation remains rare in production, and adoption is slow due to regulatory caution and budget constraints. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderately slow-adopting sector for AI tools due to budget constraints, privacy concerns, and fragmented IT infrastructure across districts. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly augment teachers by automating routine data entry, flagging incomplete records, and organizing information for human review. Teachers can use these tools to reduce manual clerical work while maintaining full oversight and responsibility for accuracy and compliance. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully speed up data entry, flag inconsistencies, and generate summary reports, significantly aiding teachers while they retain responsibility for final accuracy and compliance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with data entry and basic record organization, maintaining compliant student records requires human judgment to interpret district policies, assess completeness against varying regulatory requirements, and ensure accuracy in sensitive educational contexts. Full end-to-end automation without human oversight falls short of the 50% time-saving threshold due to compliance verification needs. |
| Task automatability | claude-sonnet-5 | 3/5 | Record-keeping (attendance, grades, basic notes) can largely be automated via integrated school information systems and AI-assisted data entry, but ensuring accuracy, handling exceptions, and compliance judgment still require teacher oversight.time savings are real but partial. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Student records are protected by FERPA and state education laws; many districts require a human educator to sign off on record accuracy and completeness. Liability for record errors is substantial, and legal requirements often mandate human accountability for maintenance. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Legal requirements (FERPA, state education codes) mandate specific recordkeeping accuracy and accountability tied to a licensed teacher/administrator, creating strong compliance-driven barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for education record management are available but typically require significant setup, integration with district systems, and ongoing human oversight to ensure compliance. The combined cost of licensing, integration, and mandatory human verification remains comparable to or higher than basic clerical wages. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Software licensing and IT support costs are moderate; while automation reduces clerical time, human review for legal compliance keeps overall costs comparable to teacher labor for this sub-task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some education software includes automated logging of grades and attendance, but comprehensive maintenance of accurate, compliant student records requires human review and discretion. No deployed product reliably handles the full scope—policy interpretation, legal compliance verification, and exception handling—without material error rates or gaps. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Student information systems (e.g., PowerSchool, Google Classroom integrations) already automate much record storage and reporting, but AI-driven auto-population/verification of records is not yet a mature, reliable standard in most districts. |
Select, store, order, issue, and inventory classroom equipment, materials, and supplies.
34CI 28–40 · exposure 30 · augmentation 38 · importance 3.3/5 · click for rater detail
Select, store, order, issue, and inventory classroom equipment, materials, and supplies.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 education remains a slow-adopting sector with fragmented IT infrastructure, limited digitization budgets, and organizational inertia; while large districts may use inventory systems, most elementary schools rely on manual or ad-hoc processes. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 schools are slow technology adopters generally, and this specific administrative/physical task has seen little AI-driven automation in practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Digital inventory systems can assist teachers by providing quick lookup of available supplies, automated reorder suggestions, and location tracking, reducing time spent searching for materials; however, the augmentation is narrow and does not fundamentally transform the productivity of material selection itself. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Basic spreadsheet or inventory apps can help track supplies, but AI provides limited transformative assistance for this largely manual, physical logistics task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While inventory tracking and ordering can be partially automated via systems that scan barcodes or manage stock levels, the physical selection, storing, and issuing of classroom materials still requires human judgment about what materials suit specific grade levels, learning objectives, and student needs—tasks that current AI cannot perform end-to-end with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Inventory tracking and ordering can be partially automated with software, but selecting appropriate classroom materials requires pedagogical judgment and physical handling that current AI cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Schools face moderate friction: purchasing policies often require human approval, budget constraints limit technology adoption, and educators prefer direct control over classroom materials to ensure pedagogical fit; however, no legal requirement mandates human execution of inventory itself. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a teacher perform this personally, but school procurement policies and physical custody of materials create some organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Automated inventory systems require significant upfront infrastructure, integration with existing school IT, and ongoing maintenance costs; for many elementary schools, especially smaller or under-resourced districts, these costs remain comparable to or exceed the savings from reduced manual inventory labor. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Generic inventory software has low marginal cost, but this is a small, infrequent task for teachers so dedicated AI tooling offers minimal cost advantage over just doing it manually or with existing school systems. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Inventory management software and basic procurement systems exist and are deployed in many schools, but they typically handle tracking and reordering rather than the full workflow of selecting appropriate materials, managing physical storage, and issuing supplies based on pedagogical requirements; performance remains patchy and context-dependent. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Inventory management software exists broadly but is not tailored to classroom supply selection/curation, and physical storage/issuing tasks remain manual; no integrated deployed product handles this whole task. |
Adapt teaching methods and instructional materials to meet students' varying needs and interests.
33CI 25–41 · exposure 30 · augmentation 75 · importance 4.7/5 · click for rater detail
Adapt teaching methods and instructional materials to meet students' varying needs and interests.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Schools are adopting adaptive platforms and AI authoring tools at a moderate pace in better-funded districts, but adoption remains patchy, often confined to pilots or enrichment rather than replacing core differentiation work across the school system. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a comparatively slow-adopting sector for classroom-level AI tools, with pilots and gradual integration rather than widespread production-scale deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI tools can meaningfully augment teacher productivity by generating differentiated worksheet variants, suggesting activity modifications, and analyzing student performance data—functions that free teacher time for interpersonal assessment and adjustment while the teacher retains decision authority. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can generate differentiated worksheets, reading levels, translated materials, and alternative explanations quickly, meaningfully boosting teacher efficiency in preparing varied instructional content. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist in generating differentiated materials and suggesting pedagogical adaptations, but cannot autonomously assess individual student needs, monitor emotional/social contexts, or make the high-stakes personalization decisions that define this task. The human teacher's real-time judgment of classroom dynamics remains essential. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help generate differentiated materials but adapting in real-time to individual classroom dynamics, emotional cues, and relationships requires embedded human judgment that current systems cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teaching requires state licensure, in-person student contact, and parental/administrative trust around pedagogical decisions. Legal and ethical liability for inadequate individualization rests with the credentialed teacher, creating a hard barrier to autonomous AI substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier specifically for using AI tools, but teacher certification requirements, school district policies, and the need for human relational judgment in the classroom create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for content adaptation carry licensing, integration, and teacher-oversight costs that approach or exceed the marginal cost of a teacher's time spent manually differentiating instruction, especially in under-resourced districts. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools for generating differentiated materials are cheap relative to teacher prep time, but the teacher must still integrate, deliver, and adjust in real time, keeping overall costs comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products like adaptive learning platforms and AI lesson generators exist in schools and can tailor content difficulty, but they operate within narrow scopes and often require substantial human curation. Reliability remains inconsistent across diverse learner populations and contexts. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like adaptive learning platforms and AI content generators exist and are used in some classrooms, but they address parts of differentiation (e.g., leveled reading materials) rather than the full scope of adapting methods and interests-based instruction reliably. |
Administer standardized ability and achievement tests, and interpret results to determine student strengths and needs.
33CI 25–41 · exposure 30 · augmentation 75 · importance 3.8/5 · click for rater detail
Administer standardized ability and achievement tests, and interpret results to determine student strengths and needs.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Schools have adopted test-scoring and reporting tools widely, but interpretation and action on results remains primarily human-driven; adoption of AI for insight generation is nascent and pilots are common, with production deployment limited to larger districts. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a relatively slow-adopting sector for AI in core instructional/assessment tasks, with pilots more common than full production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered dashboards, predictive analytics, and visualization of test data substantially assist teachers in identifying patterns and flagging students for intervention, raising productivity in data synthesis while teachers retain judgment on instructional response. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered analytics dashboards and automated scoring meaningfully speed up test processing and flag patterns, letting teachers focus more time on interpretation and instructional response. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can score and statistically analyze standardized tests reliably, but administering tests requires controlled proctoring environments and human judgment in interpreting results within developmental and socioeconomic context. Only a narrow portion of the end-to-end task meets the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can assist with scoring and generating interpretive summaries, but the full task includes physically administering tests, monitoring students, and contextualizing results with classroom knowledge, which resist full automation today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational assessment is heavily regulated (FERPA, state testing mandates, special education law); tests must be administered by qualified personnel; interpretation of results informs legally significant decisions (special education referrals, remediation); and schools face institutional liability and parental expectations for human judgment in assessment. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement for AI scoring tools, but school policies, FERPA/data privacy rules, and reliance on teacher judgment for interpreting results and IEP-related decisions create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While test-scoring software reduces per-student cost, the total integrated cost (platform fees, human interpretation oversight, data management) remains substantial and often comparable to or exceeds the cost of teacher time for routine interpretation. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated scoring and basic analytics are cheap relative to teacher time, but oversight, test administration logistics, and interpretation still require paid staff time, keeping overall costs comparable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Automated test-scoring and basic analytics products exist and work reliably in production (e.g., Illuminate, Schoolzilla), but interpretation of results to identify student strengths/needs remains human-dependent, and deployment for high-stakes interpretation is still limited in scope. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Adaptive testing platforms and automated scoring exist and are widely deployed, but interpretation tied to individual student context and instructional planning is still mostly done by teachers, not AI products. |
Use computers, audio-visual aids, and other equipment and materials to supplement presentations.
28CI 25–30 · exposure 25 · augmentation 75 · importance 4.5/5 · click for rater detail
Use computers, audio-visual aids, and other equipment and materials to supplement presentations.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While schools use digital tools widely, the automation of presentation supplement *selection and deployment* lags significantly behind other sectors; most adoption remains at the tool-use level (projectors, videos) rather than AI-driven autonomous selection and adaptation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderate-to-slow adopting sector for AI tools, constrained by budgets, policy caution, and infrastructure variability across schools. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist teachers by auto-generating slide decks, organizing multimedia libraries, suggesting relevant content, and enabling accessible formats—augmentation that keeps the teacher in control of pedagogy while raising their content-preparation productivity. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully help teachers create slides, videos, quizzes, and interactive materials that enhance presentations, offering strong productivity gains while the teacher remains in control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help generate or organize digital content, the core task—using equipment to supplement live classroom presentations—requires real-time judgment about student engagement, pacing, and pedagogical fit that humans must direct. AI cannot autonomously decide what media to display when or adapt presentation flow during a live lesson. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help generate slides, videos, and materials, but the actual in-classroom use of equipment to supplement live teaching requires physical presence and real-time adaptation that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Schools face strong institutional, regulatory, and liability barriers: teachers are legally responsible for instructional content, student safety, and curriculum alignment; parents expect human judgment in classrooms; and institutional procurement and IT policies restrict autonomous educational systems. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically for AV use, but the broader teaching role requires certified teachers to be physically present, creating organizational and role-based friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The hardware, software licenses, and AI integration costs for automating classroom presentation support often exceed the wage cost of a teacher managing these tools directly, especially when factoring in integration and ongoing maintenance. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI content-generation tools are cheap, the task still requires a human teacher present to operate equipment and deliver the lesson, so overall cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Products exist for automated content generation and media organization, but deployed educational AI systems rarely operate end-to-end with minimal teacher oversight. Most classroom technology still requires significant human curation and real-time control, not autonomous automation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for generating educational content and presentations, but no deployed system autonomously operates classroom AV equipment or manages in-person presentation delivery. |
Prepare and implement remedial programs for students requiring extra help.
28CI 25–30 · exposure 25 · augmentation 75 · importance 4.3/5 · click for rater detail
Prepare and implement remedial programs for students requiring extra help.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 education is a slow-adopting sector due to budget constraints, risk aversion, union contracts, and institutional conservatism; while EdTech tools exist, genuine displacement of remedial teaching by fully autonomous AI remains very limited and adoption is primarily in supplementary, not replacement, roles. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a historically slow-adopting sector for AI-driven instruction, with pilots of adaptive learning tools far more common than deep production-scale deployment for remediation planning. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully augment remedial teaching by generating personalized practice sets, flagging at-risk students from assessment data, and providing teachers with real-time insights into student performance, thus freeing teacher time for one-on-one support and strategic intervention design. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered adaptive practice platforms, progress-tracking dashboards, and content generators can meaningfully help teachers identify gaps and generate differentiated materials, boosting efficiency while the teacher remains the primary instructor. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with generating remedial lesson plans and practice materials at scale, but implementing these programs with students requires real-time monitoring, behavioral assessment, and adaptive adjustments based on individual learning patterns that current systems struggle to execute reliably without human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Designing and delivering remedial instruction requires diagnosing individual student needs, building rapport, and adapting in real time to a child's emotional and cognitive state, which current AI cannot fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal and policy barriers are substantial: schools have mandates under IDEA and related laws requiring qualified educators to design and implement remedial interventions; liability concerns arise if automated systems fail to identify or address learning disabilities; and parent and district expectations strongly favor human teacher involvement in high-stakes remedial work. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier prevents software use, but IEP/504 compliance, parental expectations of teacher involvement, and duty-of-care norms in elementary education create meaningful friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-powered tutoring platforms and adaptive software have material operational costs (licensing, infrastructure, oversight), and when accounting for integration and teacher monitoring time, they are not yet significantly cheaper than direct human instruction at scale, especially in public school budgets. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI tutoring software is cheap per use, the teacher still must diagnose needs, select/customize tools, monitor progress, and provide direct instruction, so total cost savings versus a human-led remedial program are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI tutoring and assessment tools exist and show some promise in controlled settings, no deployed product reliably replaces a teacher's ability to diagnose learning gaps, adjust pedagogy in real-time, and provide the emotional support and motivational scaffolding remedial students require across diverse contexts. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Adaptive learning software and AI tutoring tools exist and are used for practice/remediation in narrow skill areas (e.g., math facts, reading fluency), but no product independently plans and implements a full remedial program without teacher oversight. |
Confer with other staff members to plan and schedule lessons promoting learning, following approved curricula.
28CI 25–30 · exposure 25 · augmentation 63 · importance 4.0/5 · 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 has been slow to adopt automation generally, with strong organizational conservatism around teaching practices and staff collaboration. While AI literacy tools are emerging in schools, substitution of actual planning conferences or curriculum-driven scheduling decisions remains rare and not trending toward rapid adoption. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a relatively slow-adopting sector for AI-driven workflow changes, with pilots for lesson planning tools but little integration into staff coordination processes. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by generating lesson plan drafts, suggesting scheduling options, or flagging curriculum alignment issues before a human-led conference. However, the assistance is limited to pre-work; the core task of collaboratively conferring and deciding remains human-driven. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by generating draft lesson plans, aligning content to curricula, and suggesting schedules, which staff then discuss and finalize together. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft lesson plans and suggest scheduling, the task explicitly requires *conferring with other staff* to plan collaboratively and *following approved curricula*—both of which require human judgment, institutional knowledge, and interpersonal negotiation that AI cannot perform end-to-end. AI might assist one component (draft suggestions) but cannot replace the synchronous human coordination and curriculum alignment decision-making. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a collaborative, in-person planning and negotiation activity requiring interpersonal coordination and shared judgment; AI can support drafting but cannot replace the human conferring and consensus-building process. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Professional judgment and institutional authority to set curricula and coordinate staff schedules remain largely protected by administrative and educational norms. Teachers are expected to exercise professional discretion in lesson planning within an approved framework, and collaborative decision-making is often contractually or operationally required rather than optional. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing barrier prevents AI assistance, but organizational norms of staff collaboration and curriculum compliance create moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of AI into staff planning workflows would require significant human oversight, validation, and refinement. The cost of inference plus coordination overhead likely exceeds the savings from partial automation, especially since the most expensive part—human conferencing time—remains necessary. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | The core activity is human meetings and coordination, which AI cannot substitute for cheaply; savings only apply to peripheral prep work, not the conferring itself. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs the full task of coordinating with colleagues to plan lessons within institutional curricula. AI tools can generate lesson ideas or draft schedules, but they cannot conduct genuine collaborative conferences, interpret school-specific curriculum requirements in context, or make binding scheduling decisions with other staff members. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools exist for lesson planning assistance but no deployed product conducts staff meetings or replaces collaborative scheduling and consensus among teachers today. |
Prepare materials and classrooms for class activities.
22CI 9–35 · exposure 20 · augmentation 50 · importance 4.6/5 · click for rater detail
Prepare materials and classrooms for class activities.
22| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption is extremely limited because the task is inherently physical and context-dependent; schools have not deployed automation solutions for classroom preparation, and the nature of the work resists remote or algorithmic solutions. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a slower-adopting sector for AI tools compared to information/finance industries, with uneven district-level technology adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could modestly assist teachers by generating digital materials lists or suggesting activity setups, but the core physical work of arrangement and organization remains a teacher responsibility with limited augmentation potential from current systems. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly speed up creation of worksheets, lesson plans, and activity materials, freeing teacher time for physical classroom setup. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with some digital materials (worksheets, lesson prep documents), the physical setup of classrooms—arranging desks, organizing supplies, setting up equipment—requires embodied presence and real-time contextual judgment that current AI systems cannot perform end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical setup of classroom materials (arranging supplies, printing worksheets, setting up stations) is largely a manual, physical task that AI cannot perform; AI can help generate or plan materials but not physically prepare the room.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and organizational barriers exist: teachers are required by law and employment contracts to prepare their classrooms, and no AI system is authorized to substitute for teacher judgment on pedagogically appropriate classroom setup and material organization. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier prevents AI-assisted material creation, but the physical component inherently requires a human present in the room. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot meaningfully reduce the labor cost of physical classroom preparation; a teacher's preparation time remains necessary, and no AI system substitutes for the embodied work involved. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI content generation is cheap, but physical setup still requires the teacher's time, so overall cost savings for the full task are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably prepares physical classroom materials and spaces autonomously; this task fundamentally requires human presence and manipulation of physical objects, placing it outside the scope of today's AI capabilities. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product handles the physical classroom preparation; AI tools exist only for the content-generation subset (worksheets, lesson materials), not the full task. |
Observe and evaluate students' performance, behavior, social development, and physical health.
15CI 5–25 · exposure 13 · augmentation 38 · importance 4.5/5 · click for rater detail
Observe and evaluate students' performance, behavior, social development, and physical health.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Schools remain conservative adopters of AI for child-sensitive tasks; while data systems for grades and attendance are common, AI-driven observation and evaluation of behavior and health remain rare in production, with significant cultural and regulatory hesitation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially elementary classroom management and student welfare monitoring, shows minimal AI adoption for this specific observational task; it remains a low-digitization, high-human-contact domain. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI dashboards that aggregate student performance data, flag attendance patterns, or highlight assessment trends offer useful support to teachers' observation work, but the irreducibly human elements of noticing social and physical changes limit transformative impact. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with limited aspects like tracking academic performance data or flagging attendance patterns, but offers little support for the core behavioral, social, and physical health observation requiring direct human presence and judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with some performance tracking (e.g., flagging test scores, counting behavioral incidents), holistic evaluation of social development and physical health requires nuanced human judgment about context, individual circumstances, and interpersonal dynamics that AI cannot reliably replicate end-to-end today. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires continuous in-person observation of children's behavior, social interactions, and physical wellbeing in a classroom setting, which current AI cannot perform end-to-end.dafür It demands embodied presence and contextual judgment about individual children that off-the-shelf AI cannot replicate. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teachers are legally and professionally responsible for child welfare and safeguarding; evaluation of physical health and concerning behaviors often triggers mandatory reporting duties that require human judgment and accountability, creating high legal and liability barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Child welfare, safeguarding regulations, and professional certification requirements mean only credentialed teachers/staff can formally evaluate students' development and health concerns, creating strong legal and ethical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of multi-modal AI systems (video, assessment data, health records) with adequate oversight remains expensive relative to the cost of a teacher spending time on observation, especially given the low error tolerance in child development contexts. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this observational task, so cost comparison favors the human teacher entirely; deploying sensors/monitoring at this fidelity would be far more costly than the teacher's marginal time. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Narrow-scope systems exist for attendance and grade tracking, but no deployed product reliably evaluates the full spectrum of student behavior, social development, and health in real classroom settings; current tools lack contextual understanding and require heavy human interpretation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product observes and evaluates children's real-time behavior, social development, and physical health in classrooms; this remains outside current production AI capabilities. |
Organize and label materials and display students' work.
15CI 0–30 · exposure 8 · augmentation 25 · importance 3.6/5 · click for rater detail
Organize and label materials and display students' work.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools remain low-digitization, labor-intensive environments with minimal automation of physical classroom tasks. Adoption of robotics or automated display systems in K–5 education is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderate-to-slow adopter of AI tools generally, and this specific physical organizational task sees essentially no AI adoption in practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could modestly assist with label generation (text, QR codes) or suggest display layout templates, but the core task of physical organization and arrangement remains human-dependent with limited augmentation value. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with generating labels, organizing digital records, or suggesting display layouts, but offers limited help with the core physical arrangement task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation (organizing, labeling, displaying) and spatial judgment about classroom aesthetics and student motivation—core elements current AI cannot execute without embodied robotics. The task is fundamentally hands-on and present in the classroom environment. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical organization and display of student work in a classroom involves manual handling and spatial arrangement that AI cannot perform end-to-end; AI can help label or design digital templates but not the physical execution. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task is deeply embedded in the teacher's role and presence in the classroom. Regulatory requirements, professional norms, and the educational value of student-teacher interaction and motivational display all create strong legal and organizational barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier exists, but the task is inherently physical and situated in a specific classroom context, creating practical friction against remote or automated substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Any robot or system capable of reliably handling, organizing, and displaying physical classroom materials would cost far more than the teacher labor involved, which typically takes a few hours per term. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Since AI cannot perform the physical labor involved, a human teacher or aide must still do the bulk of the work, so cost comparison favors human labor for the physical components. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform the full scope of organizing physical materials, labeling them, and arranging classroom displays. While AI can generate labels or suggest layouts, the actual execution remains purely human. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product organizes physical classroom materials or hangs student work displays; this remains a manual task requiring physical presence in the classroom. |
Plan and conduct activities for a balanced program of instruction, demonstration, and work time that provides students with opportunities to observe, question, and investigate.
13CI 0–25 · exposure 13 · augmentation 63 · importance 4.6/5 · 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.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | No meaningful automation of live classroom instruction is occurring in practice. Schools remain heavily human-dependent for core teaching activities, with AI adoption limited to administrative or planning support at margins. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education adopts AI slowly due to funding constraints, policy caution, and child-safety concerns, with pilots more common than production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist teachers by generating lesson plan ideas, suggesting activity structures, and recommending differentiation strategies, which could improve planning efficiency. However, the actual conduct of activities remains fundamentally human-led. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools substantially help teachers brainstorm activities, generate materials, and differentiate instruction, meaningfully boosting planning productivity even though delivery remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires real-time classroom presence, student observation, and responsive facilitation of live learning experiences. AI cannot conduct activities with students, manage classroom dynamics, or respond to individual student needs in a classroom setting. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft lesson plans and activity ideas, but actually conducting a balanced, adaptive classroom program requires live facilitation, behavior management, and responsiveness that current AI cannot execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal and regulatory requirements mandate licensed teachers lead classroom instruction and assessment. Parents, policy, and law require human educators to conduct teaching activities with students, creating hard barriers to substitution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Teaching credentials, in-person supervision requirements, child safety regulations, and parental expectations create strong barriers against replacing the teacher for classroom conduct. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot perform the actual task, so cost comparison is moot. The task requires a salaried teacher present in the classroom regardless of any AI support. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI planning tools are cheap, but the 'conduct' portion still requires a full-time human teacher present, so overall cost savings are modest at best. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs the core requirement of conducting activities with students in a classroom. While AI can help draft lesson plans, it cannot replace the live instructional and observational components that define this task. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like lesson-plan generators and AI tutoring tools exist, but no deployed system autonomously conducts classroom instruction and hands-on activities for elementary students. |
Read books to entire classes or small groups.
13CI 4–21 · exposure 5 · augmentation 25 · importance 4.5/5 · click for rater detail
Read books to entire classes or small groups.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools have not adopted AI systems to replace human read-alouds at scale; the sector moves slowly on automation, and there is no evidence of significant pilot or production deployment for this particular task. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially early elementary in-person instruction, is a slow-adopting sector for AI-driven task replacement due to child safety, developmental, and regulatory concerns. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI audiobooks can supplement independent reading or study, but they do not materially assist a teacher conducting read-alouds, since classroom reading is an interactive, instructional performance rather than a technical content-delivery task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help teachers select age-appropriate books, generate discussion questions, or create supplementary audio/visual aids, but it does not materially transform the act of reading aloud itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Reading books aloud to students is fundamentally about human-student interaction, modeling fluent reading, managing classroom attention, and responding to audience reactions in real-time. Current AI lacks the social presence, adaptive responsiveness, and pedagogical judgment to replace this task meaningfully. |
| Task automatability | claude-sonnet-5 | 1/5 | Reading aloud to children is fundamentally a live, embodied social interaction requiring presence, voice modulation, eye contact, and real-time behavioral management that AI cannot perform end-to-end in a classroom setting.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task is protected by strong organizational and professional barriers: teaching requires state licensure, teacher presence in the classroom is mandated, and parent/administrator expectations for human instruction and social-emotional engagement are high. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Elementary classrooms require a certified, physically present adult for supervision, safety, and child development reasons, creating strong practical and regulatory barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI text-to-speech or audiobook playback costs are negligible compared to teacher labor, but the comparison is not economically meaningful since the task cannot be substituted without losing its educational and social value. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While audio narration is cheap, it cannot replace the supervisory and interactive value of a teacher's live reading, so the effective cost comparison for equivalent output is unfavorable to AI. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI text-to-speech systems can read text aloud, no deployed product reliably replicates the pedagogical, emotional, and interactive dimensions that make read-alouds effective in classroom settings. The task requires human presence and real-time responsiveness. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes a teacher physically reading to and engaging a classroom of children; text-to-speech or avatar tools are not used this way in production classrooms. |
Collaborate with other teachers and administrators in the development, evaluation, and revision of elementary school programs.
13CI 0–25 · exposure 8 · augmentation 50 · importance 4.0/5 · click for rater detail
Collaborate with other teachers and administrators in the development, evaluation, and revision of elementary school programs.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | School districts are among the slowest sectors to adopt AI, with limited digitization, strong union presence, regulatory oversight, and institutional conservatism; autonomous AI program development is virtually absent in production. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a comparatively slow-adopting sector for AI-driven collaborative decision-making, with pilots mostly focused on instructional content rather than administrative collaboration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools could potentially assist by drafting summary documents or retrieving archived program data, but the core task of collaborative deliberation, consensus-building, and professional judgment remains fundamentally human; limited productivity gain. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist by summarizing curriculum data, drafting proposals, generating evaluation reports, and streamlining meeting preparation, boosting efficiency while humans retain decision-making authority. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires nuanced discussion, professional judgment, interpersonal negotiation, and deep understanding of educational pedagogy, student outcomes, and institutional context—capabilities that current AI systems cannot meaningfully provide end-to-end or with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a collaborative, interpersonal task involving negotiation, consensus-building, and institutional knowledge that AI cannot conduct end-to-end; AI can assist in drafting materials but not replace the collaborative process itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Curriculum and program development in schools is often governed by state education standards, district policy, union agreements, and accountability requirements; teachers and administrators bear legal and professional responsibility for educational decisions, making human sign-off a hard requirement. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically bars AI, but institutional norms, collective decision-making structures, and accountability for curriculum decisions create moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The human cost of a teacher's time is relatively low compared to the cost of AI infrastructure, oversight, and integration needed to meaningfully assist in or replace collaborative program work; the AI would not be cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply help draft documents or summarize data, but the core deliberative and consensus-based work still requires paid staff time, so overall cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs collaborative program development, evaluation, and revision in school settings; such work requires real-time human dialogue, institutional knowledge, and accountability that AI cannot assume independently. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs collaborative program development and revision among school staff autonomously; this remains a human-driven organizational process. |
Supervise, evaluate, and plan assignments for teacher assistants and volunteers.
13CI 5–20 · exposure 8 · augmentation 38 · importance 3.6/5 · click for rater detail
Supervise, evaluate, and plan assignments for teacher assistants and volunteers.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | School districts, especially public elementary schools, are slow to adopt AI in personnel management due to budget constraints, union agreements, and preference for human-led staff oversight; most adoption remains at the pilot or administrative-scheduling level rather than in actual performance evaluation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a slow-adopting sector for AI in interpersonal management tasks, with essentially no movement toward AI-driven staff supervision. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist with scheduling volunteers, drafting evaluation frameworks, and organizing performance data, moderately raising a teacher's administrative productivity; however, the interpersonal and accountability dimensions of supervision remain heavily teacher-dependent. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help draft assignment schedules or organize performance notes, but the core supervisory and evaluative judgment remains unassisted by current tools. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Managing and evaluating teacher assistants requires consistent interpersonal judgment, contextual understanding of individual performance, and nuanced feedback tailored to each person's development—tasks that current AI systems cannot reliably perform end-to-end. While AI could help draft evaluation templates or schedule planning, the core supervision and evaluation responsibility demands human assessment of subjective performance dimensions. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires managing people—assessing their performance, giving feedback, and assigning duties based on classroom dynamics and personal judgment—tasks AI cannot perform end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Schools face organizational and employment-law barriers: teacher assistants and volunteers expect direct human oversight, performance evaluations typically must be signed by a human supervisor (often with HR and union implications), and parental/administrative trust in classroom management depends on human judgment and accountability. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Personnel supervision and evaluation typically require human judgment, accountability, and often formal HR/administrative authority, creating strong organizational and legal barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tools for scheduling and basic performance tracking cost relatively little, but they cannot replace the full scope of human-led supervision, evaluation, and adaptive planning, so all-in cost remains higher than the loaded wage saved by partial automation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI product performing this supervisory function, so no meaningful cost comparison exists; the human teacher must do this at full cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably supervises staff, evaluates performance, or manages volunteer scheduling in a school setting. AI lacks the contextual awareness, relationship history, and accountability required for real personnel management. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product supervises or evaluates human staff like teacher assistants; this remains a human management function with no AI substitute in production. |
Organize and lead activities designed to promote physical, mental, and social development, such as games, arts and crafts, music, and storytelling.
7CI 0–14 · exposure 13 · augmentation 63 · importance 4.2/5 · click for rater detail
Organize and lead activities designed to promote physical, mental, and social development, such as games, arts and crafts, music, and storytelling.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Education remains a highly regulated, human-centered sector with strong institutional and cultural resistance to replacing classroom teachers. Adoption of AI in teaching remains largely experimental and supplementary, not substitutional. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 classroom instruction, especially hands-on activity facilitation, is a low-digitization, in-person sector with minimal AI displacement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist teachers by generating lesson plans, suggesting age-appropriate activities, providing story prompts, and offering individualized learning recommendations, allowing educators to spend more time on facilitation and one-on-one engagement. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers plan activities, generate craft ideas, or write storytelling scripts in advance, but does not assist in the live facilitation itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate activity plans, craft instructions, or story ideas, the core task requires real-time interaction, relationship-building, and responsiveness to children's emotional and developmental needs. Current AI cannot reliably manage the dynamic group management and spontaneous engagement that defines this task in practice. |
| Task automatability | claude-sonnet-5 | 1/5 | Leading in-person group activities with children requires physical presence, real-time behavior management, and social-emotional responsiveness that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal and regulatory requirements mandate licensed teachers for group instruction and child supervision in most jurisdictions; liability, duty of care, and mandatory reporting obligations create hard barriers to full automation. Parent and institutional expectations also strongly favor human teachers. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Supervising and physically caring for young children requires a licensed, present adult teacher; safety, legal, and developmental requirements make substitution essentially impossible. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Even accounting for content generation and planning tools, the labor cost of human teachers remains far lower than the cost of AI systems that could meaningfully supervise, interact with, and support children's development at scale in a classroom setting. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute delivering this in-person facilitation, so cost comparison favors the human teacher entirely. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably executes this task end-to-end. Educational apps can suggest activities or deliver content, but they do not replace a teacher's ability to organize, adapt, and lead activities while managing classroom behavior and individual developmental progress. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously organizes and leads classroom activities for children; this remains firmly in the human domain. |
Instruct students individually and in groups, using teaching methods such as lectures, discussions, and demonstrations.
6CI 0–11 · exposure 5 · augmentation 63 · importance 5.0/5 · click for rater detail
Instruct students individually and in groups, using teaching methods such as lectures, discussions, and demonstrations.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | K–12 schools remain slow adopters of AI automation; classrooms are still dominated by human instruction. Most ed-tech adoption is teacher-authored or supplemental; replacement of the teacher's instructional role is nascent and not measurably displacing instruction in production. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a slow-adopting sector for classroom-facing AI, with most use confined to lesson planning and administrative support rather than direct instruction. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist teachers by drafting lesson materials, generating differentiated practice problems, and providing real-time feedback to students—raising teacher productivity—but teachers remain the primary instructional agent and decision-maker. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can help teachers generate lesson plans, differentiate materials, create demonstrations, and design discussion prompts, meaningfully boosting prep productivity even though delivery remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Live instruction—engaging students, responding to individual learning needs, managing classroom dynamics—requires human presence, responsiveness, and adaptive judgment that current AI cannot replicate at scale. No end-to-end automation solution can replace a teacher conducting real-time group and individual instruction. |
| Task automatability | claude-sonnet-5 | 1/5 | Live instruction of children requires physical presence, classroom management, relationship-building, and real-time behavioral adaptation that current AI cannot replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Elementary education is heavily regulated; teachers must hold credentials, pass background checks, and are legally responsible for student safety and welfare. Parental expectations for human instruction, state education laws, and liability make full automation of classroom instruction a hard barrier. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Certified teacher licensure, child safety/supervision laws, and mandatory human staffing ratios make this a legally protected human-only task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The loaded cost of a human teacher is low when amortized across 20–30 students per classroom. AI systems that could meaningfully augment or replace instruction would require significant infrastructure, integration, and ongoing supervision costs that exceed the per-student benefit of current technology. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI content generation is cheap, actual in-person instructional delivery still requires a paid human teacher, so total cost of substitution is not lower. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI can support teaching components (generate lecture notes, create quiz questions, provide practice demos) but no deployed system can autonomously instruct students individually and in groups with pedagogical effectiveness. Products exist for limited, narrowly-scoped instruction (drill apps) but not for the full task as stated. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously delivers whole-class or small-group elementary instruction in place of a teacher; AI tutoring tools are supplementary, not substitutive. |
Meet with other professionals to discuss individual students' needs and progress.
5CI 0–10 · exposure 5 · augmentation 38 · importance 4.4/5 · click for rater detail
Meet with other professionals to discuss individual students' needs and progress.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools remain low-digitization, human-dependent environments. Meeting structures and professional collaboration practices are deeply embedded in institutional norms and legal requirements; no measurable displacement of these meetings by AI is occurring. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a slow-adopting, relationship-driven, in-person sector with minimal AI agent deployment for interpersonal collaborative meetings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can modestly help by preparing meeting agendas, summarizing prior notes, or organizing student performance data before meetings begin. However, the core task—interactive professional discussion—remains fundamentally human-driven, limiting augmentation scope. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by summarizing student data, drafting talking points, or transcribing/synthesizing meeting notes, offering moderate productivity support around the core human interaction. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires nuanced interpersonal discussion, professional judgment, and real-time collaborative decision-making among educators. Current AI cannot meaningfully participate as a peer in multi-party professional meetings or synthesize complex, context-dependent observations into actionable recommendations without human presence. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires live, real-time human interaction, relationship-building, and shared judgment among educators/specialists about a specific child; AI cannot conduct these meetings end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Professional collaboration on student welfare involves licensed educators, parental rights, privacy (FERPA), and institutional governance. Legal and regulatory frameworks require human professionals to directly participate in decisions affecting students' educational plans and progress. |
| Adoption barriers | claude-sonnet-5 | 4/5 | School policies, IEP/504 legal requirements, and professional norms typically mandate that certified staff participate directly in these discussions, creating strong organizational and sometimes legal barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems offer no cost advantage for meeting facilitation or collaborative discussion. Human professionals must attend regardless; any AI contribution (e.g., pre-meeting summaries) is additive overhead rather than substitution. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI replacement for participating in the meeting itself, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably conducts or replaces professional meetings. AI can assist with meeting summaries or note-taking, but cannot independently lead or participate in the collaborative problem-solving required to discuss individual student needs and progress with other professionals. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a teacher attending and contributing to interdisciplinary student progress meetings; at most AI takes notes or summarizes afterward. |
Involve parent volunteers and older students in children's activities to facilitate involvement in focused, complex play.
5CI 5–5 · exposure 0 · augmentation 25 · importance 3.2/5 · click for rater detail
Involve parent volunteers and older students in children's activities to facilitate involvement in focused, complex play.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational settings are among the slowest to adopt AI automation for student-facing roles, with strong preferences for human teachers and significant regulatory/institutional resistance to algorithmic decision-making about children. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 classroom instruction and in-person student supervision is a low-digitization, slow-adopting context for AI displacement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might help a teacher plan volunteer recruitment messaging or suggest activity ideas, but the core work of persuading, training, and orchestrating human participants requires the teacher's own judgment and presence. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help plan activities or suggest ways to involve volunteers, but it offers minimal real-time assistance during the actual facilitation of play. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time social facilitation, relationship building, and nuanced judgment about individual children's developmental needs and social dynamics. Current AI cannot recruit, motivate, or meaningfully engage human volunteers in a classroom setting. |
| Task automatability | claude-sonnet-5 | 1/5 | This is an inherently interpersonal, in-person coordination and relationship-building task involving live supervision of children, parents, and older students; AI cannot perform it end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Schools have formal requirements for volunteer screening, liability frameworks, and child safety protocols that mandate human oversight and decision-making. Teachers are legally responsible for activities involving children and parent/student involvement. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Supervision of minors, safeguarding requirements, and the need for a credentialed teacher physically present create strong regulatory and liability barriers to any automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task's core value is human presence and social connection, which AI cannot provide. Any AI assistance would be far more expensive than simply having a teacher or aide perform the coordination directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical, relational task, so cost comparison favors the human teacher entirely. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can independently involve parent volunteers or organize older students for classroom activities; this requires human coordination, persuasion, and presence that AI cannot substitute for today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product manages in-person volunteer coordination and facilitates children's group play; this remains entirely outside current AI product capabilities. |
Plan and supervise class projects, field trips, visits by guest speakers or other experiential activities, and guide students in learning from those activities.
4CI 0–9 · exposure 8 · augmentation 50 · importance 3.7/5 · click for rater detail
Plan and supervise class projects, field trips, visits by guest speakers or other experiential activities, and guide students in learning from those activities.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | K–12 education remains one of the slowest-adopting sectors for automation, with strong institutional resistance, union protection, and direct human-contact requirements. Automation of teacher supervision is not occurring in practice because of legal and organizational constraints. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 classroom supervision and experiential learning activities are a low-digitization, high-physical-presence context with minimal AI adoption for this specific function. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist teachers by generating project ideas, creating itineraries, drafting reflection prompts, and suggesting guest speakers, reducing planning burden. However, the assistance is limited to preparatory work; AI does not augment live supervision or real-time instructional guidance during activities. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers plan itineraries, generate discussion questions, draft permission letters, and create learning guides tied to the experience, meaningfully aiding the planning component. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help draft project plans and suggest field trip ideas, the core work—real-time supervision of children, dynamic classroom management, and responsive pedagogical guidance during activities—requires human judgment and continuous in-person oversight that AI cannot replicate. Current systems cannot meet the 50% time-saving threshold for end-to-end execution. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical supervision of children, logistical coordination, safety management, and in-person facilitation that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Schools have legal and fiduciary duty to ensure students are supervised by qualified, responsible adults. Teachers are legally and professionally accountable for student safety and welfare during activities, and no liability can be transferred to AI systems. This creates a hard barrier to automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Child safety, supervision ratios, and liability laws require a certified teacher physically present for these activities, creating hard legal and regulatory barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Any AI system adequate to handle even planning and oversight coordination would require significant human oversight and correction, making all-in costs higher than a teacher's direct performance. Supervision and safety liability cannot be meaningfully offloaded to AI. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the human supervisory role at all, so there is no viable AI cost comparison for the core task, only for peripheral planning assistance. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs the full scope of planning, supervising, and guiding student learning from experiential activities in a classroom or field setting. The task demands live interaction, safety judgment, and adaptive instruction that no production system can deliver independently. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product supervises children on field trips or manages guest speaker visits; this remains entirely a human, in-person responsibility. |
Meet with parents and guardians to discuss their children's progress and to determine priorities for their children and their resource needs.
4CI 0–7 · exposure 0 · augmentation 38 · importance 4.5/5 · click for rater detail
Meet with parents and guardians to discuss their children's progress and to determine priorities for their children and their resource needs.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Parent-teacher conferences remain a human-centric touchpoint in K–12 education; there is no measurable shift toward AI automation in this domain, and school districts are not piloting AI for parent meetings at meaningful scale. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a relatively slow-adopting sector for AI in direct human interactions, with pilots mostly limited to administrative or grading support rather than parent meetings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist teachers by drafting progress summaries or organizing student data before a meeting, but it does not meaningfully augment the core interpersonal and decision-making work of the conference itself. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers prepare talking points, summarize student data, translate communications, or draft follow-up notes, meaningfully aiding preparation even though the meeting itself remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time relationship-building, nuanced listening, negotiation of priorities, and responsiveness to emotional and social cues from parents/guardians. Current AI cannot reliably conduct such emotionally-intelligent, bidirectional dialogues with the autonomy and contextual sensitivity this task demands. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires live, personal relationship-building, reading emotional cues, and negotiating individualized priorities with parents, which current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and professional barriers protect this task: teachers are required by law and policy to conduct parent communication and conferences; parents expect and have a right to speak with a licensed educator; liability for mishandling parent concerns and educational decisions rests on the school district and credentialed staff. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Parents overwhelmingly expect a human educator to discuss their child, and schools have strong institutional/relational norms requiring teacher involvement in these conversations. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | An AI system capable of conducting meaningful parent conferences would require significant infrastructure, training on district data, and human oversight to avoid harm. This all-in cost would far exceed a teacher's hourly wage for running these meetings. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | A human teacher is required for the actual meeting; AI could only reduce prep time slightly, not replace the interpersonal service, so it is not cheaper overall. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs autonomous parent-teacher progress conferences or priority-setting discussions at scale. Chatbots can draft templated messages but cannot replace the human judgment, empathy, and accountability inherent in these conversations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product conducts parent-teacher conferences autonomously; at most AI drafts progress summaries or schedules meetings, not the actual discussion. |
Prepare students for later grades by encouraging them to explore learning opportunities and to persevere with challenging tasks.
4CI 0–7 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Prepare students for later grades by encouraging them to explore learning opportunities and to persevere with challenging tasks.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational institutions, especially public K–12 schools, are laggard sectors in AI adoption, with low digitization of core teaching tasks and strong institutional preference for human instructors in motivational and developmental roles. Real classroom deployment of AI for student motivation remains virtually nonexistent. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a sector with generally slow, cautious AI adoption, especially for tasks involving direct socio-emotional interaction with children rather than administrative or content tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist teachers by flagging students who seem disengaged or suggesting research-backed encouragement strategies, but such tools would support only peripheral aspects of motivation and cannot augment the core human relational work that drives persistence in challenging tasks. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI tools can help teachers design engaging activities or personalized challenges, offering some support, but the core motivational and perseverance-building interaction remains largely human-driven with limited AI assistance. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires sustained interpersonal relationship-building, individual student motivation assessment, and real-time adaptive encouragement—core elements that current AI systems cannot perform end-to-end. The emotional and developmental judgment needed to motivate specific students through challenge falls outside what AI can replicate at the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 1/5 | This task is fundamentally about building relationships, motivation, and character in young children over time, which requires human presence, trust, and emotional connection that current AI cannot replicate or perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal, ethical, and regulatory barriers protect this task: teachers are state-licensed professionals, in-person student safeguarding is mandatory, parental expectations require human judgment, and liability for student welfare and development falls on credentialed educators. Substitution of AI for human encouragement and mentorship would face profound organizational and compliance resistance. |
| Adoption barriers | claude-sonnet-5 | 4/5 | While not formally licensed as a single discrete act, this task is embedded in the teacher's certified role, requires in-person trust-building with children, and involves child welfare and developmental considerations that create strong organizational and professional barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI systems capable of meaningful student motivation and encouragement, combined with required oversight and integration into a classroom, would exceed the loaded wage of an elementary teacher for this specific function. Human teachers remain far cheaper for this interpersonal work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task, so no meaningful cost comparison can be made against the human teacher's role in mentorship and encouragement. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs the full scope of motivating and encouraging students through challenging academic tasks in a classroom setting. While chatbots can offer generic encouragement, they cannot replace the human teacher's ability to read student emotional states, adjust intensity, and build trust over time. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that autonomously motivates and prepares children for future academic perseverance; this remains squarely a human relational and pedagogical function. |
Attend professional meetings, educational conferences, and teacher training workshops to maintain and improve professional competence.
4CI 0–7 · exposure 0 · augmentation 38 · importance 3.9/5 · click for rater detail
Attend professional meetings, educational conferences, and teacher training workshops to maintain and improve professional competence.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools and education systems are among the laggard sectors in AI adoption, and professional development attendance is a compliance and human-capital practice governed by institutional policy and union contracts rather than cost optimization. No measurable displacement is occurring. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderate-to-slow adopting sector overall, and this specific task (physical/professional attendance) sees essentially no AI-driven displacement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide modest assistance in scheduling optimization or summarizing conference materials, but the core value of professional meetings lies in live participation, networking, and engagement that AI cannot enhance while the human remains in the loop. Limited augmentation potential. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers find relevant conferences, summarize sessions, take notes, or generate follow-up learning plans, offering moderate assistance around the task even though it can't replace attendance. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Attending meetings, conferences, and workshops involves physical presence, real-time social interaction, and active participation in group learning environments that cannot be meaningfully automated by AI today. These are inherently human-centered, synchronous activities requiring judgment about which sessions to attend and how to engage. |
| Task automatability | claude-sonnet-5 | 1/5 | Attending and engaging in professional development meetings/conferences is an in-person, participatory activity that AI cannot perform on a teacher's behalf; it requires human presence and engagement. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong barriers exist: professional development and continuing education are often contractual or licensing requirements for teachers to maintain certification. Educational regulations frequently mandate that teachers themselves attend training; no AI agent or surrogate can substitute for the teacher's required participation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Certification and continuing-education requirements often mandate actual attendance and documented participation by the licensed teacher, creating strong institutional and regulatory barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | There is no meaningful AI cost comparison here because the task fundamentally requires human presence. The task involves the teacher's time and attention in a live setting, making automation economically irrelevant. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI equivalent output for 'attending' a professional event, so cost comparison favors the human by default since AI cannot perform the task at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can substitute for a teacher's attendance at professional development events. AI cannot physically attend, participate in discussions, network, or absorb tacit knowledge from live instruction and peer interaction that characterizes these activities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product attends or substitutes for a teacher's physical/professional attendance at conferences or workshops; this is not a task AI products target. |
Attend staff meetings and serve on committees, as required.
4CI 0–7 · exposure 0 · augmentation 38 · importance 3.4/5 · click for rater detail
Attend staff meetings and serve on committees, as required.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools have not and cannot adopt AI to replace teacher presence at required staff meetings and committee service due to regulatory, contractual, and governance constraints inherent to education administration. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderate-to-slow adopter of AI generally, and this specific task (human presence in meetings) sees essentially no displacement pressure from AI adoption trends. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist by summarizing meeting minutes or pre-populating agenda items, but the core task—synchronous participation in collegial decision-making—offers minimal augmentation opportunity since human judgment and presence are central. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist by transcribing meetings, drafting agendas, summarizing notes, or preparing committee materials in advance, improving efficiency around the task even though the core participation remains human. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Attending meetings and serving on committees requires human presence, interpersonal judgment, and decision-making in real-time discussions. Current AI cannot meaningfully participate in or replace a human's presence at these collaborative, synchronous events. |
| Task automatability | claude-sonnet-5 | 1/5 | Attending meetings and serving on committees requires physical/social presence, real-time interpersonal deliberation, and representation of personal/professional judgment that AI cannot substitute for.14 This is inherently a human participation task, not a content-production task AI can shortcut.14 |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and organizational barriers exist: employment contracts mandate staff meeting attendance, collective bargaining agreements often specify committee participation requirements, and organizational governance structures legally require human representatives on committees. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Institutional and organizational norms require the actual staff member's presence and voice in decision-making bodies; this is a governance/participation requirement embedded in school employment structures, not easily bypassed. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot perform this task at all, making cost comparison moot. Any overhead in attempting to capture meeting content or summarize decisions would add cost rather than replace human attendance. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing the actual attendance/participation role, so no meaningful cost comparison for full task replacement exists; the human must be present regardless. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can attend meetings, participate in committee discussions, or serve on committees as a human employee. This requires embodied presence and organizational recognition as a committee member. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product attends meetings or serves as a committee member on a teacher's behalf; AI tools at best summarize or transcribe meetings but do not perform the participatory role itself. |
Establish and enforce rules for behavior and procedures for maintaining order among the students.
3CI 0–5 · exposure 5 · augmentation 25 · importance 4.8/5 · click for rater detail
Establish and enforce rules for behavior and procedures for maintaining order among the students.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools remain low-digitization, human-intensive environments where in-person teacher presence and relationships are non-negotiable. Adoption of AI for core classroom management functions remains negligible across the sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a slow-adopting sector for AI in core in-person duties like classroom management, with minimal production deployment for this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can provide limited assistance (e.g., flagging attendance patterns or aggravating factors), but most of the task—establishing norms, making contextual judgments, and exercising authority—depends on human presence and does not materially benefit from AI augmentation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help draft classroom rule documents or behavior tracking systems, but offers little real-time assistance for enforcing order among students in the room. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Establishing and enforcing behavioral rules requires real-time judgment, relationship-building, and individual student assessment that current AI cannot perform autonomously in a classroom setting. AI cannot replace a human authority figure's presence and decision-making about student conduct. |
| Task automatability | claude-sonnet-5 | 1/5 | Establishing and enforcing behavioral rules requires real-time physical presence, authority, and situational judgment with children that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal, regulatory, and institutional requirements mandate that a licensed educator must be present and responsible for classroom conduct and student safety. Liability, duty-of-care, and in-loco-parentis laws create hard barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Classroom management and student discipline require a licensed, present adult with legal responsibility for child safety and welfare, making this a hard institutional and legal barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current classroom management AI requires significant infrastructure, human oversight, and integration costs that exceed the wage of a teacher's time spent on behavior management; the problem is not solved by automation but only partially supported. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this function, so cost comparison favors the human teacher entirely. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI monitoring systems exist (video analytics, classroom management software), they function only as narrow assistive tools and cannot independently establish behavioral frameworks or enforce rules in ways that students recognize as authoritative. No deployed product performs this task reliably end-to-end. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product manages classroom discipline or enforces behavioral rules with students; this remains squarely a human responsibility. |
Confer with parents or guardians, teachers, counselors, and administrators to resolve students' behavioral and academic problems.
3CI 0–5 · exposure 0 · augmentation 38 · importance 4.5/5 · click for rater detail
Confer with parents or guardians, teachers, counselors, and administrators to resolve students' behavioral and academic problems.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | School districts remain laggard in AI adoption for core instructional and pastoral roles, with limited digital infrastructure and strong union/regulatory resistance to automated decision-making about student welfare. Pilot projects exist but production displacement is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a slow-adopting sector for interpersonal, relationship-based tasks like parent conferences. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could modestly assist by summarizing prior interactions, suggesting evidence-based intervention frameworks, or scheduling coordination, but these are peripheral to the core conferencing task, which depends on human presence, judgment, and accountability. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers prepare talking points, summarize academic/behavioral data, or draft follow-up communications, but the core conference must be conducted by the human. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires nuanced interpersonal engagement, emotional intelligence, and judgment about student welfare that current AI cannot perform end-to-end. While AI can draft summaries or suggest interventions, the core work—building trust, reading social cues, and making discretionary decisions about individual students—remains fundamentally human-dependent. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires live, trust-based interpersonal negotiation and judgment about a specific child's needs, which current AI cannot conduct end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and regulatory barriers protect this task: teachers hold in loco parentis responsibility, education law mandates human professional judgment in student welfare decisions, and parent-school conferencing is typically a legally required function tied to credentialed staff and cannot be delegated to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Schools require a credentialed teacher or administrator to engage with parents on student welfare, and liability/trust concerns make human involvement effectively mandatory. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI systems that could approximate this task (natural dialogue, case management, scheduling coordination) plus required human oversight and liability burden would exceed the hourly loaded cost of a trained elementary teacher performing it directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this conferencing role, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs multi-party conferencing with parents, teachers, counselors, and administrators to resolve behavioral/academic issues in real school settings. AI lacks the situated judgment, relationship-building capacity, and accountability needed for this sensitive educational and pastoral work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product handles multi-party conferences resolving behavioral/academic issues; this remains firmly human-led. |
Provide a variety of materials and resources for children to explore, manipulate, and use, both in learning activities and in imaginative play.
3CI 0–5 · exposure 0 · augmentation 38 · importance 4.2/5 · click for rater detail
Provide a variety of materials and resources for children to explore, manipulate, and use, both in learning activities and in imaginative play.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools operate in highly regulated, human-centric environments with strong institutional and legal requirements for teacher presence and judgment. Adoption of automation for core classroom material provision is minimal, and physical constraints plus regulatory inertia in K–12 education limit any near-term shift. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 elementary education, especially hands-on early childhood instruction, shows very low AI adoption for physical classroom management tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools can assist teachers by suggesting developmentally appropriate materials, recommending activities based on learning objectives, and helping organize digital inventories of classroom resources. However, the core task of physical provision and real-time material responsiveness remains fundamentally teacher-driven. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help teachers plan and suggest age-appropriate materials or activity ideas, but it offers minimal assistance with the actual physical setup and facilitation of exploratory play. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical preparation of learning and play materials in a classroom environment, hands-on curation of age-appropriate resources, and real-time responsiveness to children's developmental needs. Current AI systems cannot physically manipulate, organize, or arrange classroom materials, nor can they assess the immediate pedagogical or developmental fit of resources for specific groups of children. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, hands-on classroom task involving sourcing, setting up, and managing tangible materials and play environments for young children, which AI cannot execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Teachers are legally required to supervise and direct classroom learning environments, and parents/districts expect human educators to make pedagogical judgments about what materials are appropriate. Legal accountability, duty of care, and regulatory requirements that a licensed educator oversee child development activities create hard barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Classroom supervision of young children requires a present, credentialed human teacher for safety, developmental appropriateness, and licensing/regulatory reasons, creating strong barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | An AI system would need to be integrated with robotic hardware, material procurement systems, and ongoing curation intelligence—making it substantially more expensive than the human labor of a teacher organizing and presenting materials as part of their regular work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical provisioning task, so cost comparison favors the human teacher entirely. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs the physical and contextual work of sourcing, organizing, and managing classroom learning materials in situ. While AI can suggest resources or generate activity ideas, actually providing and maintaining a curated learning environment requires embodied presence and judgment that current systems lack. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product physically provides or arranges manipulatives and play materials in a classroom setting; this remains entirely a research-irrelevant physical task. |
Perform administrative duties, such as school library assistance, hall and cafeteria monitoring, and bus loading and unloading.
3CI 0–5 · exposure 0 · augmentation 13 · importance 3.5/5 · click for rater detail
Perform administrative duties, such as school library assistance, hall and cafeteria monitoring, and bus loading and unloading.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Education sectors are historically low-adoption environments for automation, and student supervision duties involve irreducible legal mandates for human presence that resist substitution. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 school administrative/physical supervision functions show minimal AI adoption; schools are a slow-moving, low-digitization sector for these particular in-person duties. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI cannot meaningfully assist with physical supervision, monitoring, or in-person administrative presence in school spaces; these are not tasks where AI tools currently offer productive augmentation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI offers minor assistance, e.g., scheduling monitoring shifts or library cataloging systems, but provides little support for the physical monitoring components themselves. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task inherently requires physical presence, in-person supervision, and real-time responsiveness in school settings. Current AI systems cannot physically monitor hallways, load buses, or provide library assistance. |
| Task automatability | claude-sonnet-5 | 1/5 | These are physical presence and supervision tasks (monitoring hallways, cafeterias, bus loading) requiring real-time human presence and judgment for child safety; current AI cannot perform physical monitoring or intervention end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal and regulatory requirements mandate that school staff directly supervise students in hallways, cafeterias, and during bus loading for safety and duty-of-care reasons. Human supervision is a hard legal requirement. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Child safety supervision requires trusted adults on-site, often under legal/institutional duty-of-care and staffing ratio requirements, creating strong practical and liability barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems cannot perform these tasks at all, making any cost comparison moot; human labor remains the only viable option. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that substitutes for this physical supervisory duty, so the human cost is the only viable option, making AI effectively more 'expensive' since no AI equivalent exists. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can perform these on-site, physically-grounded administrative duties. These require embodied presence and real-time intervention in school environments, well beyond current capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical hallway/cafeteria/bus supervision of children; this remains squarely a human physical-presence task with no commercial substitute. |
Guide and counsel students with adjustment or academic problems or with special academic interests.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.8/5 · click for rater detail
Guide and counsel students with adjustment or academic problems or with special academic interests.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools operate in a highly regulated, traditionally-structured sector with deep cultural norms around the teacher–student relationship. Adoption of AI for actual student counseling and guidance remains negligible; investment in production systems for this task has not materialized, and institutional resistance is strong. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a slow-adopting sector for AI in interpersonal counseling roles, with strong institutional and parental resistance to non-human involvement in student emotional support. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools could modestly assist by flagging students at risk, suggesting evidence-based interventions, or organizing resource materials, but the actual counseling, judgment, and relationship-building must remain with the human teacher. Augmentation potential is limited because the core value lies in human presence and responsiveness. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help teachers track academic performance patterns or suggest resources for student interests, but it offers minimal assistance for the core counseling and adjustment support itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires sustained interpersonal judgment, emotional attunement, and the development of a relationship-based understanding of each student's unique context—qualities that current AI cannot reliably replicate. While AI can provide informational resources or identify general patterns of difficulty, actual counseling and guidance depends on human empathy, trust-building, and the ability to make contextual decisions that account for a child's emotional and developmental state. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires building trusted relationships with young children, reading emotional and behavioral cues, and providing personalized in-person guidance that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and professional barriers protect this work: teachers are legally responsible for student welfare and pastoral duties, parental expectations mandate human relationships, educational standards require licensed educators, and schools face significant liability if counseling is delegated to machines. The human contact requirement is both regulatory and essential. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Child welfare, safeguarding regulations, and school policies require certified educators and counselors to handle student well-being and academic guidance, creating strong legal and ethical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI systems capable of even limited guidance, combined with required human oversight and the liability exposure of automated counseling, would exceed the cost of a teacher's time for this task. Schools cannot yet reduce labor at meaningful scale through AI on this dimension. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task, so any comparison favors the human teacher who is uniquely positioned to provide this care. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs comprehensive student guidance and counseling at the level required by this task. Although chatbots and educational software exist, they cannot substitute for a teacher's role in diagnosing adjustment problems, building trust with struggling students, or tailoring academic interventions based on deep knowledge of the child. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously counsels children on adjustment or academic problems; this remains firmly in the human relational domain, not a research-stage AI capability even. |
Enforce administration policies and rules governing students.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Enforce administration policies and rules governing students.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational institutions are risk-averse regarding student discipline and liability; adoption of AI for rule enforcement is minimal to non-existent. Schools prioritize human relationships and accountability in discipline. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a slow-adopting, highly regulated, low-digitization-of-authority sector; no momentum exists toward AI enforcing student conduct rules. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by flagging patterns or documenting incidents, but the core act of enforcing rules—communicating expectations, exercising judgment, maintaining authority—remains fundamentally human and cannot be meaningfully augmented by current systems. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI might help track infractions or draft communications about policy violations, but it offers minimal assistance to the actual act of enforcement itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Enforcing policies and rules requires real-time judgment, contextual understanding of student circumstances, and discretionary authority that current AI systems cannot exercise. This task fundamentally depends on human authority and presence that AI cannot substitute. |
| Task automatability | claude-sonnet-5 | 1/5 | Enforcing rules requires real-time in-person authority, judgment about context, and interpersonal presence with children that AI cannot exercise or embody today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Schools are required by law to employ licensed teachers responsible for student welfare and discipline. Teachers have legal accountability for enforcement; AI systems cannot hold this authority or liability. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Enforcing school policy involves legal authority, child safety obligations, and requires a certified, accountable adult present physically—an unavoidable human/institutional requirement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI systems to supervise and enforce rules, plus human oversight costs, would far exceed the cost of a teacher already present. Schools would need to maintain human staff regardless. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this function, so cost comparison favors the human by default since AI cannot deliver the output at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs disciplinary enforcement or policy application in schools today. This requires human judgment, accountability, and legal standing that AI systems lack in educational settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs disciplinary enforcement or rule application with students in classrooms; this remains entirely a human responsibility. |
Provide students with disabilities with assistive devices, supportive technology, and assistance accessing facilities, such as restrooms.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.2/5 · 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 institutional, risk-averse sectors with tight regulatory oversight of special education services. Adoption of AI for direct student support—especially physical assistance—remains minimal and faces structural resistance. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 special education support services show minimal AI adoption for hands-on physical assistance tasks, as this sector is low-digitization for physical caregiving functions. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools might assist with scheduling accessibility features or documenting assistive device inventory, but meaningful augmentation of the core task (providing hands-on support, navigating facilities with students) is minimal; the teacher remains responsible for direct care delivery. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Assistive technology (e.g., communication devices, mobility aids) can be AI-enhanced to help students function more independently, but the teacher's direct physical assistance role sees limited AI augmentation. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical assistance with facilities access, real-time adaptation to individual student needs, and direct human contact with vulnerable children. Current AI systems cannot physically provide devices, escort students, or manage the hands-on support this task demands. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical presence, hands-on assistance, and real-time judgment to help children with disabilities access facilities and use assistive devices—none of this can be performed end-to-end by AI today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal and regulatory barriers are severe: educators working with students with disabilities are subject to duty-of-care requirements, IEP accommodations mandated by federal law (IDEA), and safeguarding obligations that require licensed professionals to assess and deliver individualized support. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Physical care of children, especially those with disabilities, involves strict legal, safety, and child-protection requirements mandating direct human supervision and assistance, creating hard barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot substitute for the human labor component of this task (physical assistance, facility navigation support), making any AI solution more expensive than maintaining human staff to perform these functions. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute providing physical assistance, so the relevant cost comparison is moot; a human aide is the only viable option and thus cheaper by default than any hypothetical robotic solution. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can perform this task end-to-end. The task combines physical assistance, personal care decisions, and safeguarding responsibilities that require human judgment and physical presence in real school environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product physically assists students with mobility, device use, or restroom access; this remains squarely in the domain of human aides and teachers. |
Instruct and monitor students in the use and care of equipment and materials to prevent injuries and damage.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Instruct and monitor students in the use and care of equipment and materials to prevent injuries and damage.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Elementary education remains a low-digitization, human-contact-intensive sector with minimal AI adoption for core instructional duties. Schools have not meaningfully adopted automation for student monitoring or safety supervision, and regulatory/liability constraints severely limit velocity. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially elementary classroom physical supervision, shows minimal AI adoption for this type of hands-on safety monitoring task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AI might assist with documenting equipment maintenance schedules or flagging missing materials, it cannot assist with the live monitoring and injury prevention core of the task. The augmentation value is minimal since the human cannot be absent from the supervision itself. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could provide supplementary materials like safety guides or checklists, but offers negligible real-time assistance for physical supervision and injury prevention. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time supervision, physical presence, and immediate corrective intervention with individual students—capabilities current AI systems fundamentally lack. Teaching equipment care and preventing injuries demands dynamic responsiveness to unexpected situations that no deployed automation can reliably provide. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time physical supervision of children in a classroom, handling actual equipment, and intervening to prevent injury—something current AI systems cannot physically perform.'}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and regulatory barriers exist: teachers have duty-of-care obligations, schools face liability for student injuries, and state education standards require licensed teachers to supervise students. No AI system can legally or ethically replace this human supervisory responsibility. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Child safety supervision carries strong legal, liability, and duty-of-care requirements mandating a responsible adult physically present, creating a hard barrier to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The supervision and safety-critical nature of this task means human oversight cannot be eliminated; any AI assistance would still require full-time educator presence, making the all-in cost substantially higher than a teacher performing the task directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical supervisory 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 product or deployed AI system can autonomously monitor students, physically intervene to prevent injuries, or dynamically adjust instruction in a classroom setting. This task inherently requires human presence and judgment that existing AI systems cannot perform in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product supervises children's physical use of classroom equipment or materials; this remains entirely a human, in-person responsibility. |
Sponsor extracurricular activities, such as clubs, student organizations, and academic contests.
0CI 0–0 · exposure 0 · augmentation 25 · importance 3.1/5 · click for rater detail
Sponsor extracurricular activities, such as clubs, student organizations, and academic contests.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational institutions are among the slowest sectors to automate human-facing roles, particularly those involving student supervision and extracurricular leadership, where in-person human responsibility is non-negotiable. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially extracurricular supervision, shows minimal AI adoption due to the inherently physical, in-person nature of the task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist with scheduling, communications, or activity planning, but the core task—being a responsible human sponsor who builds relationships and manages a group—remains inherently human, limiting meaningful augmentation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help with planning materials, scheduling, or contest questions, but offers little assistance for the core supervisory and mentorship activity itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Sponsoring extracurricular activities requires sustained human mentorship, relationship-building, organizational judgment, and in-person leadership that AI cannot replicate. No part of this task—from motivating students to handling group dynamics—meets a 50% time-saving bar with equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | Sponsoring extracurricular activities requires physical presence, supervision of children, relationship building, and in-person facilitation that AI cannot perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal and organizational barriers are high: schools and districts require a licensed educator to formally sponsor and supervise student organizations for liability, safety, and duty-of-care reasons. Parental expectations and child-safeguarding regulations make human sign-off mandatory. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Legal duty-of-care, child safety regulations, and school liability requirements mandate a responsible adult be physically present and accountable, making substitution essentially impossible. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying an AI system to attempt sponsorship oversight would exceed the labor cost of a teacher performing this role directly, since the human must still be present to fulfill the duty. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this supervisory role, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably sponsors or leads extracurricular activities; this fundamentally requires human presence, accountability, and judgment in a student-facing supervisory role that current systems cannot undertake. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product supervises or leads student clubs and organizations; this remains entirely a human, in-person responsibility. |
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.