Self-Enrichment Teachers
25-3021.00Median wage $46,800/yr332,110 employed (US)Rank #224 of 923 scored · top 24% by substitution
Teach or instruct individuals or groups for the primary purpose of self-enrichment or recreation, rather than for an occupational objective, educational attainment, competition, or fitness.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution37
Exposure33
Augmentation62
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
30 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
7%
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%34
panel mean rating 2.3/5 → substitution pressure 34/100
Technical feasibility todayw 20%31
panel mean rating 2.3/5 → substitution pressure 31/100
Cost vs. human wagew 15%39
panel mean rating 2.6/5 → substitution pressure 39/100
Adoption barriersw 20%inverted — strong barriers lower the score53
panel mean rating 2.9/5 (barrier strength) → substitution pressure 53/100
Sector adoption velocityw 10%27
panel mean rating 2.1/5 → substitution pressure 27/100
Task breakdown (30 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.
Maintain accurate and complete student records as required by administrative policy.
80CI 65–95 · exposure 83 · augmentation 75 · importance 4.3/5 · click for rater detail
Maintain accurate and complete student records as required by administrative policy.
80| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Educational institutions have rapidly and deeply adopted learning management systems and student information systems over the past 15 years; digital record-keeping is now standard practice in K-12 and higher education across most developed markets. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Self-enrichment education (adult ed, hobby classes, non-formal instruction) is a fragmented, often small-scale sector with lower digitization and slower adoption of integrated AI administrative tools compared to K-12 or corporate training. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI systems assist teachers by auto-populating records from class activity data, flagging missing or incomplete entries, and organizing records for quick retrieval, substantially raising teacher productivity in record management while teachers retain oversight. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can auto-populate records, flag missing data, generate summaries, and reduce manual entry, meaningfully boosting efficiency while the teacher still reviews and finalizes records. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Record maintenance is highly structured data entry and retrieval that can be fully automated end-to-end through learning management systems and database integration, easily achieving >50% time savings at equal or better quality and accuracy compared to manual record-keeping. |
| Task automatability | claude-sonnet-5 | 4/5 | Recordkeeping tasks like attendance, grades, and progress notes are largely structured data entry that AI-integrated school/education management software can handle with templates and automation, saving significant teacher time.The remaining human input is mostly verification. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While some institutions have legacy systems and require administrative oversight of data accuracy, there are no legal licensing requirements, regulatory mandates that a human must manually enter records, or inherent human-contact requirements for this administrative task. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Some administrative and privacy compliance requirements (e.g., FERPA-like rules) apply, but no licensing mandates a human to personally maintain records, so barriers are moderate-low. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Student record management software costs pennies per student per year once deployed, while manual record entry by a teacher loaded wage represents significant hourly cost, yielding at least an order-of-magnitude cost advantage for automated systems. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated record-keeping software and AI-assisted data entry tools are inexpensive relative to teacher hourly wages spent on paperwork, offering substantial cost savings once integrated. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Mature, deployed products (Blackboard, Canvas, Google Classroom, PowerSchool) reliably perform student record management at scale in thousands of educational institutions today, with well-established error handling and compliance features. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Learning management systems and administrative software with AI-assisted data entry, auto-generated summaries, and reminders exist and are used, but many self-enrichment/community education settings still rely on manual or semi-manual record systems with variable reliability. |
Write instructional articles on designated subjects.
80CI 76–84 · exposure 75 · augmentation 100 · importance 2.4/5 · click for rater detail
Write instructional articles on designated subjects.
80| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | EdTech and online learning platforms are rapidly adopting AI for content generation; many organizations have moved from pilot to production use. Self-enrichment and online course providers show high digitization and fast adoption patterns typical of information-sector work. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Content creation and education sectors show moderate AI adoption with many pilots and some production use for drafting materials, but self-enrichment teaching remains a smaller, less digitized niche with slower uptake. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically augments human instructional writers by drafting initial articles, suggesting structure, providing research synthesis, and enabling rapid iteration. Teachers remain in the loop to refine, validate, and personalize content, significantly raising their output velocity. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI writing tools substantially speed up drafting, provide outlines, examples, and rewrites, letting instructors focus on subject accuracy and personalization while dramatically boosting output speed. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can generate instructional articles on most designated subjects with minimal human input, meeting or exceeding quality standards and time savings beyond 50%. However, some domain-specific nuance, pedagogical customization, and subject matter expertise validation may still require human review, preventing a full 5. |
| Task automatability | claude-sonnet-5 | 4/5 | Drafting instructional articles on a defined subject is well within the capability of current LLMs, which can produce structured, coherent content quickly given a topic and outline., though final review/customization is still needed. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or licensing barriers exist for AI-generated instructional content. Organizational friction (preference for human expertise, quality control, attribution norms) provides modest resistance, but nothing legally prevents substitution in most self-enrichment contexts. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There is no licensing, legal liability, or regulatory requirement forcing a human to write instructional articles; nothing structurally prevents AI-assisted or AI-generated content. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI inference cost per article is negligible (pennies to dollars) compared to the loaded wage of a professional instructional writer ($25–60/hour or higher), creating a cost ratio of at least one to two orders of magnitude in AI's favor. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Generating a draft article via AI costs a fraction of a cent to a few cents in compute versus the hourly cost of a human writer/teacher, an order-of-magnitude or greater saving. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (ChatGPT, Claude, specialized writing tools) reliably generate instructional content at scale in production. Quality is generally high for straightforward subjects, though edge cases and highly specialized topics may introduce errors that require human oversight. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Products like ChatGPT, Claude, and specialized content-generation tools are already used in production to draft instructional and educational articles at scale, though human editing is typically still applied. |
Select, order, and issue books, materials, and supplies for courses or projects.
62CI 47–77 · exposure 58 · augmentation 75 · importance 3.3/5 · click for rater detail
Select, order, and issue books, materials, and supplies for courses or projects.
62| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Educational institutions are moderately digitizing procurement, but adoption of AI-driven ordering remains patchy—many schools still rely on manual requisitions and vendor relationships. Larger districts use automation more; small programs lag. Overall mid-cycle adoption, not yet mainstream. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Self-enrichment education (adult hobby classes, community programs) is a low-digitization sector with limited AI tool adoption for administrative logistics tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can powerfully assist by recommending materials matched to learning objectives, flagging availability and costs in real time, and automating routine ordering while teachers focus on pedagogical judgment and content curation. This maintains human control while multiplying selection speed and scope. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully help teachers research suppliers, compare prices, and draft order lists, significantly speeding up the planning portion of this task even though physical fulfillment remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Most of this task—identifying curriculum needs, searching inventory systems, generating purchase orders, and tracking supplies—can be automated or significantly streamlined with current AI and enterprise systems. The main remaining friction is human judgment about pedagogical fit and approval workflows, but the core execution (search, match, order) achieves >50% time savings. |
| Task automatability | claude-sonnet-5 | 3/5 | Selecting and ordering materials involves research and comparison that AI can assist with, but final selection judgment and physical issuance of supplies require human involvement, capping full end-to-end automation at roughly half the task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or regulatory barriers exist; procurement is lightly regulated for most courses. Main friction is organizational (approval sign-offs, teacher preferences for curation) and institutional habit, but nothing prevents automated ordering subject to budget review. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There are no licensing or regulatory requirements tied to selecting and ordering course materials; it's an administrative task with minimal legal or safety barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automating inventory lookup, supplier comparison, and order generation through APIs and AI is orders of magnitude cheaper than paying a human to manually research, call vendors, and process paperwork. Integration and oversight add modest cost but remain well below human labor cost. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI can cheaply assist with research and ordering lists, but physical logistics (issuing supplies) still require human labor, making overall cost savings moderate rather than dramatic. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Enterprise procurement and learning management systems with API integrations are widely deployed and handle ordering reliably; AI-assisted catalog search and recommendation are mature in retail and education. Some manual verification of course fit remains necessary, but the technical capability is proven in production. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No widely deployed product manages end-to-end curriculum material selection and physical supply issuance for self-enrichment courses; existing tools are general procurement or e-commerce systems adapted ad hoc. |
Prepare instructional program objectives, outlines, and lesson plans.
61CI 59–64 · exposure 50 · augmentation 88 · importance 3.9/5 · click for rater detail
Prepare instructional program objectives, outlines, and lesson plans.
61| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Self-enrichment and adult education sectors are moderately digitized and show growing pilot adoption of AI lesson planning tools, but widespread production deployment remains piecemeal. K–12 and higher education lag significantly due to institutional conservatism. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Education sector adoption of generative AI for lesson planning is growing but still uneven, with many self-enrichment instructors (hobbyist/community-class settings) slow to adopt formal AI tools. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at generating structured outlines, brainstorming objective frameworks, and adapting existing curricula—tasks that significantly boost teacher productivity when used iteratively. Teachers remain essential for final curation and pedagogical judgment, making this a strong augmentation case. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI is highly effective as a drafting and brainstorming aid for objectives and outlines, letting instructors quickly generate and refine structured plans while retaining final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can generate draft lesson plans, outlines, and learning objectives at scale using templates and curriculum frameworks, but human educators typically need to refine content for pedagogical fit, learner needs, and institutional context. This saves moderate time but requires substantial human judgment for quality instruction. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft objectives, outlines, and lesson plans quickly given course topic and audience, but tailoring to specific student needs, local context, and instructor style still requires human review and revision.dur |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or legal requirement mandates human authorship of lesson plans; institutional adoption depends mainly on teacher preference, familiarity, and organizational buy-in. Oversight and quality review remain common practice but are not hard regulatory barriers. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or legal requirement mandates human-only lesson planning for self-enrichment (non-credentialed) instruction; adoption is unconstrained by regulation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | A single API call costs pennies, while a teacher spend 1–2 hours preparing comprehensive lesson plans. Even with human review overhead, the per-task cost of AI-assisted planning is substantially lower than human-only preparation. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating a draft lesson plan via AI costs pennies compared to the hourly wage of a self-enrichment teacher spending time on curriculum design. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Multiple tools (Claude, ChatGPT, specialized EdTech platforms) can produce lesson plans and objectives reliably, but educators in production use these as starting points rather than final outputs. Error rates in appropriateness and pedagogical soundness remain material without review. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like ChatGPT, lesson-planning assistants, and LMS-integrated tools are widely used to generate lesson plans, but quality varies and teachers routinely edit outputs before use. |
Confer with other teachers and professionals to plan and schedule lessons promoting learning and development.
60CI 35–85 · exposure 58 · augmentation 88 · importance 3.8/5 · click for rater detail
Confer with other teachers and professionals to plan and schedule lessons promoting learning and development.
60| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Education has middling AI adoption with many pilots in lesson planning and scheduling, but production deployment remains incomplete; many schools still rely on manual coordination, and institutional conservatism slows adoption compared to information and professional services sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education, especially self-enrichment/non-formal instruction, is a slower-adopting sector with limited AI deployment for collaborative planning tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI assistants meaningfully augment teacher productivity by drafting schedules, suggesting lesson structures, identifying conflicts, and generating communication templates, freeing teachers to focus on pedagogical content and interpersonal finesse while staying in the loop. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can help draft lesson plans, suggest schedules, and summarize discussions, meaningfully aiding teachers who still handle the interpersonal coordination themselves. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Scheduling lessons, coordinating with colleagues, and planning curricula are structured activities involving calendar management, document synthesis, and communication that current AI systems excel at automating end-to-end, achieving well over 50% time savings via calendar integration, email drafting, and lesson-plan generation. |
| Task automatability | claude-sonnet-5 | 2/5 | Coordinating and negotiating lesson plans with colleagues involves interpersonal judgment, scheduling constraints, and relationship dynamics that current AI cannot autonomously handle end-to-end.'}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While schools may prefer human oversight and organizational inertia exists, there are no legal or licensing barriers preventing AI from automating the scheduling and planning components; adoption is primarily a matter of choice and institutional friction rather than regulatory mandate. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI use, but organizational norms and the inherently social nature of conferring with colleagues create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven scheduling and planning tools cost a fraction of human labor hours per lesson coordinated, with integration costs amortized across many users, making per-task cost at least an order of magnitude cheaper than paying a teacher for the same coordination work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human collaboration time is not expensive to replace since AI cannot fully substitute the interactive planning process, so cost savings are limited to partial drafting support. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (calendar automation, email assistants, learning management systems with AI scheduling) reliably handle lesson coordination and planning in production environments, though some edge cases around complex multi-stakeholder negotiation may require human refinement. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some scheduling and collaborative-planning tools exist but no deployed product reliably conducts substantive professional conferring and joint curriculum planning without heavy human involvement. |
Assign and grade class work and homework.
59CI 51–67 · exposure 58 · augmentation 75 · importance 4.1/5 · click for rater detail
Assign and grade class work and homework.
59| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Adoption is uneven: some schools pilot AI grading tools, but most teachers still grade manually or use basic LMS features. Sectors like higher education and corporate training adopt faster, but K–12 self-enrichment teaching shows slower, cautious adoption. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Self-enrichment education is a small, fragmented sector with lower digitization and slower formal AI tool adoption compared to K-12 or corporate training environments. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can draft rubrics, auto-grade objective items, flag outliers, and suggest feedback, substantially raising teacher productivity in the grading workflow while the teacher validates and customizes. This assistive role is widely adopted and effective. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly speed up creation of assignments and provide first-pass grading/feedback, letting instructors focus on personalized coaching and qualitative assessment. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate grading of objective or structured assignments (multiple choice, short fills) and provide feedback at scale, saving significant time. However, grading subjective work (essays, creative projects) and assigning appropriately differentiated homework still requires human judgment, limiting full automatability. |
| Task automatability | claude-sonnet-5 | 4/5 | Grading structured homework and generating assignments can largely be automated with AI tools like automated grading systems and quiz generators, especially for objective content, though subjective work like essays or creative projects still needs human judgment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Teachers remain responsible for pedagogical decisions and grade accuracy; institutional policies, district oversight, and parent/student expectations create friction around full automation. No legal requirement for human sign-off, but organizational and professional norms discourage wholesale replacement. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Self-enrichment teaching (e.g., hobby classes, adult ed) typically lacks strict licensing or regulatory requirements for grading, though instructor oversight and personalized feedback are often valued by students. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI grading tools are inexpensive to deploy per student and significantly reduce teacher labor for routine assignments, making the cost per task-equivalent substantially lower than human wages for bulk grading, though setup and oversight add overhead. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Once set up, AI-based grading and assignment generation tools cost far less per student than instructor grading time, especially for repetitive or standardized content. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist (LMS platforms with auto-grading, ChatGPT-based rubric assistants) that handle objective grading reliably, but subjective assessment remains error-prone and requires human oversight. Deployment is common in K–12 but with material limitations on scope and quality. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Gradescope, Turnitin, and AI grading assistants exist and are used in some settings, but adoption in self-enrichment/adult education contexts is patchier than in K-12 or higher ed, and reliability varies by subject matter. |
Review instructional content, methods, and student evaluations to assess strengths and weaknesses, and to develop recommendations for course revision, development, or elimination.
57CI 48–67 · exposure 58 · augmentation 75 · importance 3.7/5 · click for rater detail
Review instructional content, methods, and student evaluations to assess strengths and weaknesses, and to develop recommendations for course revision, development, or elimination.
57| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Education institutions are exploring AI-driven learning analytics and curriculum tools, with pilots increasing, but production deployment remains mixed. Adoption is faster in large universities and EdTech companies but slower in small independent education programs, reflecting institutional conservatism around curriculum. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Self-enrichment education is a small, less digitized sector with limited AI tool adoption for curriculum evaluation; pilots may exist but production use is rare compared to corporate or higher-ed settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI substantially assists instructors and curriculum designers by rapidly synthesizing student feedback, identifying performance gaps, and surfacing data-driven insights that would require hours of manual review. The human educator remains in the loop to validate recommendations and make final pedagogical judgments, while AI dramatically accelerates the analytical phase. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by aggregating student feedback, identifying patterns in evaluations, and drafting revision recommendations for human review, significantly speeding up the analysis portion of this task. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI systems can analyze instructional content, student evaluation data, and assessment metrics to identify patterns, strengths, and weaknesses at scale. Modern LLMs and analytics tools can generate structured recommendations for course revision with minimal human setup, meeting the 50% time-saving threshold for significant portions of this analytical and synthesis work. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can analyze evaluation data, summarize feedback themes, and draft revision suggestions, but synthesizing pedagogical judgment about course elimination/development requires human contextual decision-making that current AI only partially replicates. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While no strict licensing requirement exists for course revision recommendations, institutional practices, faculty governance norms, and the educational institution's desire to retain human pedagogical judgment create meaningful friction. Educators often retain authority over curricular decisions, limiting pure substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates human-only review, though institutional decision-making processes and accountability for course changes create moderate organizational friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven analytics and content review tools are significantly cheaper than hiring human instructional designers or curriculum specialists to perform the same analysis. The per-task cost of running inference on evaluation data and generating recommendations is orders of magnitude lower than loaded wages for skilled educational professionals. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools can cheaply summarize survey data and draft reports, but human oversight, contextual review of instructional quality, and final decision-making still require significant paid time, keeping costs roughly comparable for a full task cycle. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist (learning analytics platforms, LLM-based educational analytics) that can perform parts of this task reliably, but deployed systems typically require human domain expertise to validate recommendations, interpret context, and make final decisions. Performance is material but not fully autonomous. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like analytics dashboards and LLM-based text summarizers can process student evaluations and generate insights, but no deployed product reliably performs the full curriculum-review-to-recommendation workflow autonomously in self-enrichment education settings. |
Schedule class times to ensure maximum attendance.
57CI 44–70 · exposure 50 · augmentation 63 · importance 3.6/5 · click for rater detail
Schedule class times to ensure maximum attendance.
57| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Self-enrichment education is fragmented across small studios, community centers, and independent instructors with low digitization; adoption of dedicated scheduling AI remains minimal outside large franchises or established online platforms. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Self-enrichment education is a small-business-dominated, less digitized sector where formal scheduling AI adoption is still nascent compared to fields like finance or professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered scheduling assistants can suggest optimal time slots based on historical enrollment patterns and generate conflict reports, materially reducing manual review work while teachers retain decision authority over final schedules. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI scheduling assistants can meaningfully help teachers optimize class times by analyzing attendance trends and availability, though teachers likely still make final decisions based on contextual factors. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Scheduling optimization can be partially automated with enrollment data and availability constraints, but predicting and maximizing attendance requires human judgment about student motivation, marketing timing, and contextual knowledge that current AI systems cannot reliably handle end-to-end. |
| Task automatability | claude-sonnet-5 | 4/5 | Scheduling optimization based on attendance patterns, availability data, and constraints is a well-structured problem that AI/algorithmic tools handle efficiently, though it requires access to relevant data inputs and occasional human judgment for edge cases. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Teachers typically retain final authority over class schedules and attendance goals, and organizational culture often resists algorithmic scheduling; however, no strict licensing or regulatory requirement protects this task from automation. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There are no licensing, legal, or human-contact requirements tied to scheduling logistics, so no meaningful regulatory or professional barrier prevents automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | Calendar and scheduling software costs are modest and overlap with other administrative functions, placing total cost roughly in line with the part-time labor that might otherwise perform manual scheduling in small educational settings. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated scheduling tools are inexpensive relative to the time a human would spend manually coordinating class times, making AI-assisted scheduling considerably cheaper at scale. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Calendar management and basic constraint-satisfaction tools exist in commercial products, but no off-the-shelf system reliably predicts attendance outcomes or integrates multi-factor scheduling decisions at production scale for self-enrichment contexts. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Scheduling software with AI-assisted optimization exists and is used in some educational and enrichment settings, but many self-enrichment teachers still rely on manual scheduling or simple calendar tools rather than dedicated AI scheduling products. |
Prepare materials and classrooms for class activities.
54CI 24–85 · exposure 53 · augmentation 75 · importance 3.8/5 · click for rater detail
Prepare materials and classrooms for class activities.
54| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Educational institutions are experimenting with AI-generated lesson materials and resource organization, but adoption remains in pilot phase across most self-enrichment and community education settings. Larger online education platforms show faster adoption; traditional in-person enrichment remains slower. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Self-enrichment/education services sectors, especially involving hands-on physical classroom setup, show low AI adoption for physical logistics tasks compared to office/knowledge work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI significantly augments teacher productivity by generating customized materials, suggesting classroom layouts, automating resource organization, and handling repetitive formatting tasks, allowing teachers to focus on pedagogical design and content quality rather than mechanical preparation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help plan lesson content, generate materials lists, or draft activity plans, offering moderate productivity assistance even though it cannot perform the physical setup itself. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Preparing materials and setting up classrooms involves routine tasks like printing documents, organizing content, arranging digital resources, and creating layouts—all of which AI can fully automate end-to-end with significant time savings. Content generation, layout design, supply list creation, and scheduling can be handled by current systems with minimal human intervention. |
| Task automatability | claude-sonnet-5 | 2/5 | This involves physical setup of classrooms and materials, which current AI cannot perform; AI can help draft or generate content but not physically prepare a room or arrange materials.rating reflects only partial content-side automation.rating capped low due to physical component. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No legal requirement mandates a licensed teacher prepare materials; many institutions already use administrative staff or shared digital templates. Adoption is primarily blocked by organizational inertia and teacher preference for hands-on preparation, not regulation or liability concerns. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barriers exist, but the physical nature of the task and need for on-site presence create practical friction to automation beyond just planning/content generation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automating material preparation via AI (document generation, printing, digital resource curation) costs pennies per class session compared to the hourly labor cost of a teacher organizing and printing materials manually, creating an order-of-magnitude cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that substitutes for physical setup labor, so cost comparison favors the human doing the physical task; AI cannot replace this labor at any meaningful cost advantage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed AI systems reliably generate lesson materials, create printable handouts, and produce organized resource lists. Document automation and content curation tools are production-ready in educational contexts, though physical classroom arrangement may require human oversight or robotics integration not yet mainstream. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product physically prepares classrooms or materials; this remains a manual, in-person task with no robotics or automation solutions in production for this use case. |
Participate in publicity planning and student recruitment.
54CI 44–64 · exposure 42 · augmentation 75 · importance 3.2/5 · click for rater detail
Participate in publicity planning and student recruitment.
54| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Educational institutions are adopting marketing automation and AI-assisted content tools at middling pace—pilots and email automation are common, but deep AI-driven recruitment strategy is still emerging in the sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Self-enrichment education (small studios, community class providers) is a low-digitization sector with slower AI tool adoption compared to larger institutions or corporate marketing departments. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at generating multiple copy variants, analyzing enrollment funnel data, segmenting student audiences, and drafting outreach materials, significantly boosting a human recruiter or marketing officer's productivity while they retain strategy and relationship decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools substantially speed up drafting of promotional materials, ad copy, and recruitment content, letting instructors focus on strategy and personal outreach while still supervising this. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Publicity planning requires creative strategy, brand voice decisions, and audience insight that AI can assist with but not fully replace. Student recruitment involves relationship-building, persuasion, and institutional knowledge where AI can draft materials or segment audiences but cannot close enrollment decisions autonomously. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can generate marketing copy, social media plans, and recruitment materials quickly, but strategic decisions, relationship-building, and local market judgment still require human involvement, so only part of this task meets the time-saving threshold. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No legal requirement mandates human involvement; however, institutional reputation and trust in recruitment messaging create practical pressure to maintain human judgment and institutional accountability, reducing pure substitution risk. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory barriers prevent using AI tools for publicity and recruitment materials; adoption is a matter of choice and comfort, not compliance. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-powered marketing and recruitment tools have relatively low per-task inference costs, but integration, campaign management, and human oversight (review, strategy approval) add overhead that roughly matches the cost of having a junior marketing or enrollment staff member handle routine tasks. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-generated flyers, social posts, and email campaigns cost a fraction of hiring marketing staff or paying an agency, though some human review and strategy input is still needed. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI tools exist for marketing copy generation, audience targeting, and campaign planning (e.g., marketing platforms with AI-assisted content), but they typically require significant human oversight to match institutional voice and enrollment goals. Production use is common in marketing but less mature in the education recruitment context. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Marketing and content-generation tools (e.g., copywriting assistants, social media schedulers) are widely deployed and used by small businesses and educators, but full recruitment strategy execution still requires human oversight and local knowledge. |
Observe and evaluate the performance of other instructors.
51CI 16–85 · exposure 45 · augmentation 63 · importance 3.5/5 · click for rater detail
Observe and evaluate the performance of other instructors.
51| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Educational institutions have begun piloting AI-powered observation and feedback tools, but adoption remains inconsistent; many organizations still rely on traditional human peer observation, placing this in middling adoption territory. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Self-enrichment education is a small, fragmented, low-digitization sector with little evidence of AI-driven evaluation tools being adopted at scale. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI observation tools significantly assist human evaluators by providing quantitative metrics, highlighting key moments, and surfacing patterns that would require hours of manual review, allowing instructors and administrators to focus on qualitative interpretation and coaching. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by transcribing lessons, flagging speaking patterns, or summarizing feedback, providing useful but partial support to a human evaluator. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Current AI systems can analyze video recordings of instructors using computer vision and speech recognition to evaluate pacing, engagement, clarity, and classroom management against defined rubrics, easily exceeding the 50% time-saving threshold compared to manual observation. |
| Task automatability | claude-sonnet-5 | 1/5 | Evaluating another instructor's teaching performance requires nuanced human judgment about pedagogy, rapport, and adaptability that current AI cannot reliably assess end-to-end from observation alone. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While organizations may prefer human peer review for collegial reasons and evaluation may inform employment decisions (creating some friction), there are no legal requirements mandating human observation of instructors, and AI systems can be deployed with minimal regulatory burden. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement mandates a human evaluator, but organizational norms, trust, and the interpersonal nature of performance reviews create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-based video analysis and automated scoring costs pennies per session compared to paying a human observer's loaded wage for equivalent observation time, representing an order-of-magnitude cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI transcription/analysis tools are cheap, they cannot substitute for the full evaluative task, so any real automation would still require costly human oversight, keeping overall cost comparable or higher. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products including learning analytics platforms, classroom observation software with AI scoring, and video analysis tools already perform instructor evaluation in production environments, though they typically require human review of nuanced judgment calls. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs holistic instructor performance evaluation in self-enrichment settings; at best AI can analyze recorded video/transcripts for narrow metrics, not full evaluative judgment. |
Prepare and administer written, oral, and performance tests, and issue grades in accordance with performance.
49CI 44–55 · exposure 42 · augmentation 75 · importance 4.1/5 · click for rater detail
Prepare and administer written, oral, and performance tests, and issue grades in accordance with performance.
49| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Online self-enrichment platforms (e.g., Coursera, Udemy) have deployed auto-grading at scale for objective tests, but most in-person self-enrichment programs still rely on instructor assessment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Self-enrichment education (hobby classes, adult continuing ed) is a low-digitization, small-provider sector with limited AI tool adoption for grading or test administration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI tools can draft test questions, auto-score and flag borderline answers, and generate grade summaries, substantially accelerating the teacher's workflow while the instructor retains oversight and final grading authority. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully help teachers draft test questions, rubrics, and provide first-pass feedback, freeing time for the human-judgment-heavy parts of oral/performance evaluation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can generate test questions and auto-score objective exams with ~50% time savings, but cannot reliably assess nuanced performance tests (e.g., music, art, movement) or fairly assign grades when subjective judgment dominates the evaluation criteria. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft written tests and grade objective/short-answer responses well, but grading oral and performance-based assessments (e.g., dance, music, art) requires human perceptual judgment that current AI cannot reliably replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No legal requirement for a licensed educator to assign grades in self-enrichment contexts, but organizational policies, parental expectations, and concern over fairness create moderate friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Self-enrichment teaching is typically unlicensed and informal, so few regulatory barriers exist, though customer expectation of personal feedback from the instructor creates some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-powered grading for objective items costs significantly less than human grading, but integration, oversight, and handling of subjective assessments keeps total cost roughly comparable to hiring part-time graders. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | For written test creation and simple grading, AI is much cheaper, but performance evaluation still requires the instructor's presence and judgment, keeping overall cost roughly comparable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Commercial LMS platforms and assessment tools exist for objective testing and basic grading, but performance-based and oral assessment automation remains immature and error-prone in real educational deployments. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like AI quiz generators and essay-grading tools are deployed in education, but performance/oral test evaluation for self-enrichment subjects (crafts, music, fitness) lacks mature, reliable AI products. |
Establish clear objectives for all lessons, units, and projects and communicate those objectives to students.
47CI 37–56 · exposure 42 · augmentation 75 · importance 4.1/5 · click for rater detail
Establish clear objectives for all lessons, units, and projects and communicate those objectives to students.
47| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Education sectors remain relatively slow in adopting AI for core instructional design tasks despite available tools. Most adoption is in supplementary contexts (admin, grading); curriculum and objective-setting remain centrally human-controlled in schools and enrichment programs. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Self-enrichment education (hobby/adult ed/arts instruction) is a low-digitization, small-provider sector with limited AI tool adoption compared to K-12 or corporate training. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI tools substantially assist teachers in brainstorming objectives, organizing learning progressions, and generating communication templates. Teachers use these drafts to accelerate lesson planning while retaining final authority over educational goals and how they are presented to students. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI is highly useful for drafting objectives, aligning them with skill progressions, and rephrasing them for different audiences, meaningfully speeding up lesson prep while the teacher stays in control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can generate draft learning objectives and lesson outlines quickly, but cannot fully replace the teacher's role in tailoring objectives to specific student populations, institutional constraints, and pedagogical philosophy. The communication component requires human judgment about timing, medium, and adaptation to student understanding. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft learning objectives from a topic or standard quickly, but tailoring them to specific student groups and delivering them in-context in class still requires human judgment and adaptation.It saves drafting time but doesn't replace the full task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teaching licenses typically require pedagogical judgment and accountability for educational outcomes; institutional policies often mandate that educators, not systems, own curriculum design and student communication. Professional standards and accreditation bodies expect human educators to establish educational objectives. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement dictates who writes lesson objectives, but instructors typically want ownership of curriculum design and personalized communication style, creating some organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI inference for generating objectives and lesson structure is very inexpensive relative to the teacher time saved on drafting and organizing; however, oversight and customization still require human input, preventing a full 5x cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating and communicating lesson objectives via AI tools costs a fraction of a cent compared to teacher time, though the teacher still needs to review and adapt them for real delivery. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI tools (GPT, Claude) can produce learning objectives and instructional designs in production settings, but outputs require substantial teacher review, refinement, and contextualization. The task of communicating effectively to diverse students remains partially manual and requires human oversight. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like lesson-planning AI tools and chatbots reliably generate objective statements, but their use in live self-enrichment teaching (e.g., art, music, hobby classes) settings is inconsistent and not standardized in production. |
Attend staff meetings and serve on committees, as required.
43CI 7–77 · exposure 45 · augmentation 63 · importance 3.4/5 · click for rater detail
Attend staff meetings and serve on committees, as required.
43| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Education has lagged adoption of AI agents for administrative tasks; schools remain tradition-bound and human-centric in governance structures, with limited evidence of production-scale automation of meeting attendance or committee participation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education and self-enrichment instruction sectors show slow, uneven AI adoption, and this specific interpersonal/organizational task is not a target of current automation efforts. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can powerfully assist teachers by automating meeting summarization, action-item tracking, agenda pre-reading synthesis, and committee document management, freeing teacher attention for active participation and decision-making while human oversight remains central. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with meeting transcription, summarization, agenda preparation, and follow-up task tracking, moderately boosting productivity around the task even though attendance itself is unaffected. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Attending staff meetings and serving on committees involves passive participation, note-taking, and routine coordination—tasks that AI agents can fully handle by monitoring agendas, recording decisions, tracking action items, and maintaining committee records without human presence at meeting times, achieving substantial time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | Attending meetings and serving on committees requires physical/virtual presence, real-time social interaction, and organizational representation that AI cannot perform end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no legal licensing barriers, organizational culture and institutional norms typically expect human presence at staff meetings and committees for accountability and relationship-building, creating meaningful adoption friction despite no hard regulatory requirement. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Institutional and professional norms typically require the actual staff member to attend and represent themselves in meetings and committees, creating strong organizational and accountability barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automating meeting attendance and committee work via AI agents costs pennies per task compared to a teacher's loaded hourly wage, representing orders-of-magnitude cost advantage when measured per meeting processed or document generated. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the human's presence and participation, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | AI systems can reliably perform asynchronous meeting participation, agenda processing, and committee documentation through deployed tools and agents; however, live interactive participation or real-time decision-making in meetings remains limited by current agent capabilities in production systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a human attending and participating in staff meetings or committee service; AI note-takers exist but do not replace the attendance/participation requirement itself. |
Use computers, audio-visual aids, and other equipment and materials to supplement presentations.
35CI 35–35 · exposure 25 · augmentation 75 · importance 3.5/5 · click for rater detail
Use computers, audio-visual aids, and other equipment and materials to supplement presentations.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Self-enrichment instruction remains largely human-centered; while some institutions experiment with recorded or AI-assisted content, real-time supplementation of live presentations is not widely being automated in the sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Self-enrichment/adult education is a low-digitization, small-scale sector where AI tool adoption for instructional support is still nascent and inconsistent. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist teachers by auto-generating presentation slides, suggesting relevant media, managing audio-visual timing cues, and organizing materials, thereby raising teacher productivity while the instructor retains control of content and delivery. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly help teachers prepare slides, videos, and interactive materials in advance, meaningfully boosting the quality and efficiency of supplementary content creation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help generate or organize audio-visual content, the core task of integrating equipment into live presentations requires real-time judgment about pacing, audience engagement, and technical troubleshooting that current systems cannot reliably do end-to-end without human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | The physical act of using computers and AV equipment during a live class requires human presence, coordination, and real-time adaptation, though AI can help prepare the materials beforehand.arametrized on-time execution isn't automatable end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Self-enrichment instruction typically does not require licensed credentials or formal legal barriers, but organizations and students often prefer human instructors for engagement and personalization, creating some adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for using AV equipment, but the human-contact nature of live teaching creates organizational and pedagogical friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI content generation has become cheaper, but the labor cost of a self-enrichment teacher is relatively modest, and integration still requires human setup and oversight, keeping overall cost savings minimal. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools for generating slides or media are cheap, but the in-class operation and integration still requires the teacher's paid time, so overall cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some AI tools exist for generating or editing content (slides, audio), but no deployed product reliably handles the full task of selecting appropriate equipment, troubleshooting failures, and adapting presentations in real time as a self-enrichment teacher would. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously operates classroom AV equipment and integrates it into live instruction; existing tools only assist with content creation prior to class. |
Adapt teaching methods and instructional materials to meet students' varying needs and interests.
35CI 25–45 · exposure 39 · augmentation 63 · importance 4.4/5 · click for rater detail
Adapt teaching methods and instructional materials to meet students' varying needs and interests.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Self-enrichment and adult education settings are slower to adopt automation compared to information-intensive sectors; uptake remains largely at the pilot stage with learning management system integrations. Most institutions have not moved beyond experimental use of adaptive AI in classroom practice. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | ... |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist teachers by generating multiple content variations, suggesting scaffolding strategies, or flagging students who may need intervention, thereby reducing planning burden. However, the effectiveness is moderate because final decisions about adaptations require teacher judgment and domain knowledge that remains difficult for AI to enhance reliably. |
| Augmentation potential | claude-sonnet-5 | 4/5 | ... |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate alternative instructional materials or suggest pedagogical approaches based on student profiles, adapting methods in real-time requires understanding individual learning trajectories, motivation, and classroom dynamics—nuanced judgment that AI struggles to perform end-to-end without significant human oversight. Current systems lack reliable assessment of students' underlying needs and interests to drive meaningful, sustainable adaptations. |
| Task automatability | claude-sonnet-5 | 2/5 | Adapting live teaching to individual students in real time requires interpersonal responsiveness, physical presence, and on-the-spot judgment that current AI cannot fully replicate end-to-end."},"feasibility":{"rating":2,"rationale":"Adaptive learning platforms exist for content personalization but do not perform full in-class instructional adaptation for self-enrichment/hobby-style teaching contexts."},"cost_ratio":{"rating":2,"rationale":"Since a human instructor must still deliver most in-person adaptive teaching, AI tools add cost as a supplement rather than replacing the labor cost."},"barriers":{"rating":3,"rationale":"No licensing requirement typically exists for self-enrichment teachers, but customer preference for human interaction and the interpersonal nature of instruction create moderate friction."},"adoption_velocity":{"rating":2,"rationale":"Self-enrichment/adult education is a low-digitization, small-provider sector where AI tool adoption is still nascent compared to corporate or tech-driven training sectors."},"augmentation":{"rating":4,"rationale":"AI can help generate differentiated materials, suggest alternative explanations, and personalize practice content, meaningfully aiding teachers in tailoring instruction."}}eval:excess key removed automatically? No—must format correctly.The assistant recognizes formatting error and corrects.{ |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teaching roles are subject to state credentialing, institutional accountability for student outcomes, and parental/community expectations that a licensed educator oversee instruction. Liability and professional standards create friction against wholesale automation, and stakeholder preference for human judgment in educational adaptation remains strong. |
| Adoption barriers | claude-sonnet-5 | 3/5 | :. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Implementing AI-driven personalization requires curriculum design, platform setup, ongoing curation of learning materials, and integration with existing systems. These integration and oversight costs, combined with the need for a human teacher to implement and refine adaptations, make the total cost comparable to or higher than traditional human-led instruction. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | : |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Educational platforms exist that offer adaptive learning paths and material recommendations, but they typically operate on narrow, pre-defined competency models and rarely capture the full scope of varying interests or social-emotional factors. No mature product reliably performs the full task of comprehensive pedagogical adaptation across diverse student populations in production settings. |
| Technical feasibility today | claude-sonnet-5 | 5/5 | AdI-ives Adiu-personalization content, but engaging supplemation and adaptation, materials generation, and adaptive suggestions . |
Prepare and implement remedial programs for students requiring extra help.
30CI 30–30 · exposure 25 · augmentation 63 · importance 3.6/5 · click for rater detail
Prepare and implement remedial programs for students requiring extra help.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K-12 education is a slow-adopting sector with limited digitization, tight budgets, and strong cultural preference for human teachers. While some districts pilot adaptive platforms, broad production adoption of AI-driven remedial programs remains rare and shallow. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Self-enrichment and tutoring services are a fragmented, small-business-heavy sector with slower, uneven AI tool adoption compared to large enterprise sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist teachers by generating personalized practice problems, flagging struggling students via data analysis, and providing content suggestions. These aids boost teacher productivity, but the human remains essential for relationship-building, motivation, and nuanced intervention decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by generating diagnostic quizzes, personalized practice sets, and progress tracking, significantly boosting a teacher's efficiency in designing remedial plans. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help generate remedial content and assessments, the task fundamentally requires diagnosing individual student needs, adapting teaching in real-time to student responses, and providing motivational/emotional support—all requiring human judgment and presence. AI might automate 20-30% of prep work but cannot replace the core implementation. |
| Task automatability | claude-sonnet-5 | 2/5 | Designing and delivering personalized remedial instruction requires ongoing diagnosis of a specific student's struggles, motivation, and relationship-building that current AI cannot fully replace end-to-end.','rating_note': |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Regulations vary by jurisdiction, but many districts require certified teachers to oversee or deliver remedial instruction. There is moderate friction around AI replacing in-person support, especially for vulnerable student populations, though not an absolute legal prohibition. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement strictly mandates a human, but parents/institutions often expect direct human instruction for struggling students, creating moderate adoption friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tutoring platforms have meaningful costs per student-hour, and integration with existing curricula plus necessary human oversight adds overhead. When accounting for quality assurance and personalization, the all-in cost approaches or exceeds that of a part-time remedial instructor. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cheaply generate practice content, but the human tutor's time for delivery, motivation, and adjustment remains the dominant cost, keeping overall savings modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some AI tutoring systems exist (e.g., adaptive learning platforms), but they operate narrowly (specific subjects, standardized formats) and typically require human oversight. No deployed product reliably implements full remedial programs independently; human teachers remain essential in production settings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Adaptive learning platforms exist and can generate practice materials, but no deployed product autonomously runs a full remedial program with assessment, adjustment, and student engagement reliably at scale. |
Monitor students' performance to make suggestions for improvement and to ensure that they satisfy course standards, training requirements, and objectives.
29CI 23–35 · exposure 25 · augmentation 50 · importance 4.2/5 · click for rater detail
Monitor students' performance to make suggestions for improvement and to ensure that they satisfy course standards, training requirements, and objectives.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Self-enrichment/continuing education sectors show slower digital maturity than higher ed or corporate training; adoption remains concentrated in online platforms with minimal automation of actual performance assessment and suggestion-making. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Self-enrichment education (hobby, recreational, personal development classes) is a low-digitization, small-provider sector with minimal reported AI deployment for student monitoring. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist teachers by auto-grading objective components, surfacing at-risk students, and drafting feedback prompts, but the core task of evaluating satisfaction of standards and crafting improvement suggestions benefits from but does not fundamentally transform with current AI tools. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers track attendance, quiz results, and generate suggested feedback drafts, offering moderate assistance while the instructor retains primary responsibility for actual monitoring and coaching. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Monitoring quantitative performance metrics (test scores, assignment completion) could be partially automated, but making meaningful suggestions for improvement and evaluating satisfaction of subjective course standards requires nuanced human judgment and knowledge of individual student context that current AI systems struggle with reliably at scale. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can track quiz scores and flag gaps in structured digital platforms, but assessing holistic performance (e.g., in dance, music, hobby classes) and giving nuanced improvement suggestions typically requires in-person observation and judgment AI cannot fully replicate. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Self-enrichment education often occurs in informal settings with strong preferences for human mentorship and personalized guidance; accreditation bodies and institutions typically require human instructors to certify student progress, creating meaningful legal and organizational friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement generally applies, but strong customer preference for a live instructor's personalized feedback and physical/skill-based demonstration creates meaningful friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration with course systems, maintaining accurate rubrics, and required human review of AI-generated suggestions mean all-in costs remain substantial; savings are offset by oversight labor needed to ensure quality feedback. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Building or licensing performance-tracking AI plus required human oversight for personalized feedback still costs comparably to an instructor's time, especially in small-class, in-person settings typical of self-enrichment courses. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While learning management systems can track grades and flag low performers, no deployed product reliably generates personalized improvement suggestions or validates course standard satisfaction without significant human oversight and correction. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Adaptive learning platforms exist for academic subjects but self-enrichment courses (art, fitness, crafts, personal development) rarely have deployed AI monitoring tools integrated into instruction today. |
Instruct students individually and in groups, using various teaching methods, such as lectures, discussions, and demonstrations.
28CI 25–30 · exposure 25 · augmentation 50 · importance 4.5/5 · click for rater detail
Instruct students individually and in groups, using various teaching methods, such as lectures, discussions, and demonstrations.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Self-enrichment and adult learning sectors show slower digitization than corporate training; adoption remains mostly in online tutoring niches rather than broad classroom instruction replacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Self-enrichment education (hobby classes, adult continuing ed) is a low-digitization, small-provider sector with slow AI adoption compared to corporate or tech-driven industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist instructors by generating example content, providing discussion prompts, and personalizing learning materials for review, but the live teaching relationship and group dynamics remain human-driven. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers prepare lecture materials, generate discussion prompts, or create supplementary content, but doesn't materially change the live, hands-on instructional interaction itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate lecture content and demonstrations, the core task of real-time instruction with adaptation to individual student needs, classroom management, and dynamic engagement requires human presence and responsiveness. AI falls short of the 50% time-saving threshold for the full instructional task. |
| Task automatability | claude-sonnet-5 | 2/5 | Live instruction involving real-time interpersonal engagement, physical demonstrations, and adaptive group dynamics cannot be fully replicated by current AI end-to-end, though some content delivery portions could be augmented. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Self-enrichment teaching often requires human credibility, liability coverage, and customer preference for live interaction; many organizations face regulatory or accreditation requirements that a licensed/qualified human instructor must deliver or oversee the class. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing typically required for self-enrichment teachers, but strong customer preference for human interaction, physical demonstration needs, and social/motivational aspects of teaching create moderate organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Developing and maintaining AI instruction systems (infrastructure, content curation, oversight) remains expensive relative to hiring part-time self-enrichment instructors, whose labor costs are often modest in the gig/volunteer space. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Replacing an instructor's live, adaptive, multi-modal teaching with AI would require significant hardware/software investment while still needing human oversight, making costs comparable or higher for genuine equivalence. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Products exist for content generation and tutoring, but no deployed system reliably replaces a live instructor across diverse groups with variable learning needs. Tutoring bots work in narrow, structured domains; live group instruction remains human-dependent in practice. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tutoring products exist for narrow subjects online, but no deployed system reliably conducts in-person or hybrid group instruction with discussion and demonstration across self-enrichment topics (e.g., dance, art, hobbies). |
Conduct classes, workshops, and demonstrations, and provide individual instruction to teach topics and skills, such as cooking, dancing, writing, physical fitness, photography, personal finance, and flying.
28CI 25–30 · exposure 25 · augmentation 75 · importance 3.9/5 · click for rater detail
Conduct classes, workshops, and demonstrations, and provide individual instruction to teach topics and skills, such as cooking, dancing, writing, physical fitness, photography, personal finance, and flying.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Self-enrichment teaching remains a highly personalized, localized sector with limited digitization and slow AI adoption. While online courses have grown, live-instruction replacement lags significantly behind information-sector adoption; most demand still centers on human instructors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Self-enrichment education is a small, fragmented, in-person-oriented sector with limited enterprise AI adoption compared to fast-moving digital-first industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist instructors by generating lesson plans, creating demonstration videos, providing personalized practice problems, and offering administrative support, thereby raising instructor productivity and student engagement—yet the human instructor remains essential for real-time correction, motivation, and interpersonal trust. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist instructors by generating lesson plans, personalized practice materials, instructional videos, and supplementary explanations, enhancing instructor productivity even though it doesn't replace live teaching. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate lesson plans, demonstrations, and instructional content, conducting live classes and workshops with real-time feedback, physical correction (in dance/fitness), and interpersonal interaction requires human presence. The task fundamentally depends on responsive instruction adapted to individual student needs and emotional engagement, which current AI systems cannot replicate end-to-end at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Live instruction involves real-time demonstration, physical coaching, feedback on skill execution, and interpersonal motivation that current AI cannot fully replicate end-to-end, though some content delivery (e.g., written lessons) could be automated. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: liability concerns in high-risk domains (flying, physical fitness), regulatory requirements for certain certifications (pilot instruction), strong customer preference for human interaction and feedback, and organizational friction in credentialing AI instructors. Insurance and legal frameworks typically require human accountability. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Some sub-skills (flying lessons) require licensed instructors and certification, while others (cooking, dance) have looser requirements but strong customer preference for in-person human interaction and hands-on correction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI infrastructure, content creation, integration, and human oversight required to deliver equivalent instruction across diverse topics (cooking, dancing, flying) remains comparable to or higher than hiring self-enrichment instructors, especially for skill-based domains requiring safety or personalized correction. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI-generated video or chatbot content is cheap, replicating personalized in-person coaching with equipment, physical spaces, and safety supervision (e.g., flying lessons) still requires human labor, keeping costs comparable or higher when quality matters. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI-powered video tutorials and some pre-recorded instructional content exist, but deployed products cannot reliably teach complex practical skills requiring immediate feedback, hands-on correction, or adaptive pacing. Chatbots and AI tutoring systems lack the embodied knowledge and real-time responsiveness needed for cooking, dancing, or flying instruction. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tutoring products exist for narrow academic subjects but there is no deployed product reliably teaching hands-on skills like cooking, dancing, or flying at scale in place of human instructors. |
Plan and conduct activities for a balanced program of instruction, demonstration, and work time that provides students with opportunities to observe, question, and investigate.
25CI 20–30 · exposure 20 · augmentation 75 · importance 3.6/5 · 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.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational institutions, particularly self-enrichment programs, have been slow to adopt AI-driven instruction delivery at scale; most adoption remains at the planning and material-generation stage rather than full replacement of live teaching. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Adult/enrichment education is a small, fragmented, low-digitization sector with limited AI adoption for actual class delivery, though planning tools see some pickup. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly assist by generating diverse activity ideas, providing real-time scaffolding suggestions, analyzing student responses to guide discussion, and automating administrative prep, thereby freeing the teacher to focus on live facilitation and personalized interaction. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully help teachers brainstorm activities, structure lesson flow, and generate discussion prompts, enhancing planning even though delivery remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help generate activity ideas, lesson structures, and even draft materials, the core task—conducting live instruction, demonstration, and facilitating real-time student questioning and investigation—requires human presence and responsiveness to adapt to students' immediate needs and engagement. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help generate lesson plans and activity ideas but the live facilitation, adaptive demonstration, and hands-on work-time supervision require physical presence and real-time responsiveness that current systems cannot replicate. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational contexts (especially those requiring demonstrated expertise and student safeguarding) carry implicit expectations and often regulatory requirements that a human instructor be present; parents and institutions typically demand human contact and accountability, creating strong friction against pure automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing typically required for self-enrichment teachers, but student/parent expectations of human interaction and hands-on demonstration create meaningful adoption friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-generated lesson plans and materials reduce prep time, but the labor cost of a qualified self-enrichment teacher conducting the actual instruction remains far below the infrastructure needed for AI-driven delivery with adequate oversight and customization per student needs. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply assist with planning content, but since the actual conducting of the class still requires a human instructor, the overall cost savings versus a human teacher's full task are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI tools can assist with planning and content generation, but no deployed product reliably executes the full task of conducting interactive instruction and managing live student inquiry. Chatbots and lesson generators exist but do not replace a teacher's real-time classroom facilitation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously plans and conducts in-person instructional sessions with demonstration and supervised work time; existing tools only support planning materials, not execution. |
Plan and supervise class projects, field trips, visits by guest speakers, contests, or other experiential activities, and guide students in learning from those activities.
23CI 20–25 · exposure 20 · augmentation 63 · importance 3.5/5 · click for rater detail
Plan and supervise class projects, field trips, visits by guest speakers, contests, or other experiential activities, and guide students in learning from those activities.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational institutions have low digital automation velocity for core instructional and supervision tasks; adoption of AI for planning aids exists but is slow and limited to specific sectors like well-resourced districts. Supervision and experiential learning remain firmly human-centered. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Self-enrichment education is a small, less digitized sector with limited AI adoption for hands-on activity management, though some planning tools are used. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist teachers by generating project ideas, creating field trip itineraries, drafting reflection prompts, and organizing logistics, meaningfully reducing preparation burden. However, the real-time guidance and supervision remain human-dependent, limiting augmentation to the planning phase. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist in generating project ideas, drafting itineraries, sourcing speakers, creating contest rubrics, and preparing learning guides, improving planning efficiency significantly. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with planning logistics (scheduling, itinerary drafting), the core task requires real-time supervision, safety oversight, and adaptive guidance during activities. Current AI cannot meaningfully supervise in-person activities or respond to emergent classroom dynamics, limiting automation to planning components only. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help plan logistics and generate ideas but cannot physically supervise students, coordinate real-world field trips, or manage in-person guest speaker visits and contests, which are the core deliverables of this task.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational institutions typically require licensed educators or trained staff to supervise students, especially during field trips and activities involving safety and duty-of-care responsibilities. Liability, duty-of-care law, and organizational policies create substantial barriers to substitution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Duty-of-care, liability for student safety during field trips and contests, and institutional requirements for adult supervision create strong barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI planning tools are inexpensive, but they cannot replace the human labor cost of actual supervision and on-site guidance. The economic benefit is limited to optional pre-planning work, not the core delivery, making overall cost savings minimal. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply assist with planning documents and itineraries, but the supervisory and in-person components still require paid human staff, limiting overall cost savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI systems can draft project plans and field trip itineraries, but no deployed product reliably handles end-to-end execution including supervision, student engagement, and adaptive pedagogical decisions in live settings. Existing tools support planning but not the supervision and guidance components. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs on-site supervision or in-person facilitation of experiential learning activities; this remains inherently a human physical-presence task. |
Observe students to determine qualifications, limitations, abilities, interests, and other individual characteristics.
16CI 5–26 · exposure 13 · augmentation 38 · importance 4.3/5 · click for rater detail
Observe students to determine qualifications, limitations, abilities, interests, and other individual characteristics.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Self-enrichment teaching (art, music, hobby classes) occurs in fragmented, often small-scale settings with low digitization. Adoption of formal observation AI is laggard; most teachers still rely on direct, informal observation rather than data-driven systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Self-enrichment education (arts, hobbies, personal development classes) is a low-digitization, high-physical-presence sector with minimal AI adoption for this kind of task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by highlighting performance patterns (grade trends, attendance flags, skill progression) that prompt teachers to investigate further, but the core task of interpreting individual characteristics requires the teacher's judgment and direct interaction. Augmentation is useful but not transformative. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help teachers track attendance, quiz results, or written feedback, but it offers little assistance in the nuanced real-time observation of a student's interests and abilities during instruction. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can partially automate surface-level observation (e.g., analyzing attendance, assignment completion, test scores), but determining deep qualifications, limitations, and individual characteristics requires nuanced human judgment of context, motivation, and social-emotional factors that current systems struggle to capture reliably. The task lacks the structured data and objective metrics needed for meaningful ≥50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time, in-person observation of a live human's behavior, engagement, and physical/emotional cues, which current AI cannot perform end-to-end in typical self-enrichment class settings. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational institutions face significant barriers: parental consent and privacy regulations (FERPA, GDPR), liability concerns around algorithmic bias in student assessment, and strong organizational and cultural preference for human teachers as primary observers of student characteristics. Overreliance on AI observation also faces institutional and legal resistance. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing barrier for observation itself, but the inherently interpersonal, in-person nature of self-enrichment instruction creates strong organizational and customer-preference friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The all-in cost of AI observation systems (software licensing, data infrastructure, oversight, and integration) typically exceeds the loaded wage of an individual self-enrichment teacher conducting direct observation, especially in smaller or non-institutional settings. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this observational task, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While learning analytics products exist to track student performance metrics, no deployed system reliably performs the full observational task—assessing individual characteristics, abilities, interests, and limitations—with the holistic accuracy expected in educational settings. Most products are narrow data-collection aids rather than end-to-end observation systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously observes students in physical classes (e.g., dance, art, music) to assess individual characteristics; this remains outside current product capabilities. |
Instruct and monitor students in the use and care of equipment and materials to prevent injury and damage.
15CI 5–25 · exposure 13 · augmentation 38 · importance 3.9/5 · click for rater detail
Instruct and monitor students in the use and care of equipment and materials to prevent injury and damage.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Self-enrichment education (hobby, arts, recreation classes) is fragmented across small studios, community centers, and independent instructors with low digital maturity and high preference for in-person instruction; adoption of monitoring AI remains nascent in these sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Self-enrichment/adult education and hands-on instruction sectors show minimal AI adoption for physical safety supervision, which remains an inherently in-person function. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by pre-generating safety checklists, video tutorials on equipment care, or automated alerts for hazardous conditions detected via camera, but the instructor must still deliver live instruction and make real-time safety judgments, so augmentation is meaningful but not transformative. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help create safety guides, checklists, or instructional videos ahead of time, but offers little real-time assistance during actual equipment use and monitoring. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could generate instructional content and safety guidelines, the task critically requires real-time monitoring of physical equipment use and dynamic assessment of individual student behavior and safety—tasks that demand embodied presence and immediate intervention that current AI systems cannot perform reliably without human supervision. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time physical presence to demonstrate equipment use, observe students' actual handling behavior, and intervene to prevent injury—AI cannot physically monitor or stop unsafe actions in a classroom or studio setting. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: duty of care and liability law typically require a responsible adult physically present to monitor student safety; negligence liability falls on institutions if automated systems fail to prevent injury; and direct human-student interaction for safety instruction is often mandated by safety regulations and organizational policy. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Liability for student injury, insurance requirements, and duty-of-care obligations in instructional settings create strong practical and legal barriers to removing human supervision. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Deploying AI monitoring (cameras, sensors, analysis) plus the required human oversight still costs substantially more than a single self-enrichment teacher managing a class in situ, and does not replace the need for human judgment and intervention. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the human safety-monitoring function at all, so there is no viable cost comparison—the human is required regardless of AI cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably instructs and monitors students in equipment safety end-to-end; video monitoring systems exist but cannot reliably detect all unsafe behaviors or intervene, and generating safety instruction content is not the core bottleneck—live observation and correction is. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs live physical supervision and safety intervention for students using equipment; this remains squarely a human physical-presence task. |
Organize and supervise games and other recreational activities to promote physical, mental, and social development.
13CI 5–20 · exposure 8 · augmentation 38 · importance 3.3/5 · click for rater detail
Organize and supervise games and other recreational activities to promote physical, mental, and social development.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Self-enrichment and recreation sectors are typically lower-tech, smaller organizations with limited digitization; adoption of AI tools is slow and remains experimental in planning support rather than task automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Self-enrichment/recreational instruction is a low-digitization, physically grounded sector with minimal AI agent deployment for supervisory tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist with activity scheduling, rule generation, progress tracking, and personalized recommendation of games/exercises, enabling enrichment teachers to serve more participants or design better sessions while remaining actively engaged. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help with planning activity curricula or generating game ideas, but offers little assistance during the actual real-time supervision and interaction. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI cannot directly supervise physical activities or manage real-time group dynamics, though it could assist with scheduling, rule documentation, and activity design. The core task of in-person supervision and adaptation to participant needs remains human-dependent. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence, real-time supervision, and interpersonal engagement with participants (often children), which current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: duty of care and liability for participant safety legally require a responsible human; organizations face regulatory requirements around child/adult supervision; insurance and risk management create friction against full substitution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Supervision of activities, especially involving minors, typically requires human presence for safety, liability, and duty-of-care reasons, creating strong practical and sometimes regulatory barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for activity planning and scheduling are inexpensive, but they cannot replace the loaded labor cost of a supervisor who must be physically present and responsible for participant safety and engagement. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the physical supervisory role at all, so there is no viable cost comparison—human labor is required. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably organizes and supervises recreational activities end-to-end; this requires physical presence, real-time judgment, safety oversight, and interpersonal responsiveness that current AI systems cannot provide in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product organizes and supervises in-person recreational activities; this remains entirely a human physical-presence task. |
Prepare students for further development by encouraging them to explore learning opportunities and to persevere with challenging tasks.
11CI 5–16 · exposure 0 · augmentation 38 · importance 4.4/5 · click for rater detail
Prepare students for further development by encouraging them to explore learning opportunities and to persevere with challenging tasks.
11| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Self-enrichment teaching is delivered in small groups and one-on-one settings where human relationship is the core asset; adoption of AI for this task remains negligible because the educational model is built on human interaction. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Self-enrichment education is a small, fragmented, low-digitization sector with limited AI agent deployment for motivational/mentorship functions specifically. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist marginally by providing teachers resource recommendations or tracking student progress, but it offers no meaningful boost to the core task of motivating and encouraging perseverance, which depends on human presence and judgment. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can suggest resources, personalized learning paths, and challenge-appropriate materials that a teacher can use to encourage students, providing moderate assistance to the core task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires sustained human relationship-building, emotional intelligence, and adaptive mentoring to motivate individual students—capabilities that current AI cannot replicate in real pedagogical settings. No off-the-shelf system can replace the personalized encouragement and presence needed to foster student perseverance. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires building motivation, rapport, and personalized encouragement through in-person human relationship, which current AI cannot replicate end-to-end at equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: students and parents typically expect human teachers in enrichment contexts; there is high liability for failures in student motivation and development; and the task inherently involves human trust and presence that cannot be legally substituted by AI. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing barrier specifically for this task, but strong customer/parent preference for human mentorship and organizational norms in self-enrichment education create real friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of implementing and maintaining AI systems to attempt this task (plus human oversight for quality assurance) exceeds the modest loaded wage of a self-enrichment teacher, especially given high failure and dropout rates in autonomous systems. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI chat tools are cheap per interaction, they cannot substitute for the sustained human relationship-building needed, so the effective cost of replacement is high given quality gaps. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs the core emotional and relational aspects of this task: genuine encouragement, adaptive motivation, and personalized challenge calibration. AI chatbots lack the credibility, presence, and accountability that student-teacher dynamics require. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously performs the human motivational coaching and mentorship this task requires; AI tutoring products assist but don't replace this interpersonal function. |
Meet with other instructors to discuss individual students and their progress.
11CI 5–16 · exposure 0 · augmentation 38 · importance 3.6/5 · click for rater detail
Meet with other instructors to discuss individual students and their progress.
11| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational institutions, particularly self-enrichment programs, lag in AI adoption and heavily value human-to-human instructor coordination. Few organizations have even piloted AI meeting replacement in this sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Self-enrichment/education settings (often small studios, community programs) show low AI adoption for interpersonal coordination tasks, lagging behind corporate knowledge work sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by generating pre-meeting student summaries or flagging at-risk students for discussion, but the core value of instructor meetings—collaborative problem-solving and peer insight—remains firmly human-centered and augmentation opportunities are limited. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by summarizing student progress data, drafting talking points, or transcribing/summarizing the meeting afterward, aiding preparation and follow-up even though it can't replace the discussion. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires nuanced human judgment about individual student progress, interpersonal dynamics, and real-time responsiveness to instructor insights. Current AI cannot independently conduct productive peer discussions or synthesize educator expertise in ways that replace human conversation. |
| Task automatability | claude-sonnet-5 | 1/5 | This is an interpersonal, real-time discussion between colleagues requiring shared judgment and relationship context; AI cannot conduct this human meeting end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational settings have strong cultural and organizational norms favoring direct instructor collaboration, professional autonomy in curriculum decisions, and trust-based peer assessment. Replacing instructor deliberation with AI would face significant institutional and interpersonal resistance. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing barrier to using AI notetaking tools, but the core task is inherently collaborative human dialogue reliant on professional trust and shared observation, creating natural friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI systems that could transcribe, analyze, and generate meeting summaries would likely exceed the instructor time saved, especially given the lightweight nature of brief coordination meetings in self-enrichment contexts. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Since AI cannot perform the actual meeting, the relevant cost comparison doesn't favor AI; any AI role is a minor add-on (e.g., notetaking) rather than a substitute for the labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs autonomous instructor-to-instructor meetings about individual students. While AI can summarize student records or assist with documentation, it cannot actually conduct or meaningfully replace the discussion itself. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for instructors meeting to discuss students; at best AI tools might summarize notes afterward, not conduct the meeting itself. |
Meet with parents and guardians to discuss their children's progress and to determine their priorities for their children.
9CI 5–13 · exposure 0 · augmentation 38 · importance 3.7/5 · click for rater detail
Meet with parents and guardians to discuss their children's progress and to determine their priorities for their children.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Self-enrichment instruction is often delivered by independent educators, small organizations, and community centers with low digitization; adoption of AI-mediated parent meetings in these sectors is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Self-enrichment/education services sectors show slower AI adoption for interpersonal engagement tasks, with pilots limited mostly to administrative support rather than replacing parent conferences. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with note-taking or summary generation after a meeting, but offers minimal augmentation during the live conversation itself, which depends on human empathy, real-time responsiveness, and relationship trust. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help teachers prepare progress reports, summarize student data, and draft talking points beforehand, meaningfully aiding preparation even though the live meeting itself remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires genuine two-way dialogue, relationship-building, understanding individual family contexts, and responsive judgment about priorities unique to each child and family—all deeply contextual and interpersonal work that current AI cannot conduct autonomously with acceptable quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires live, relational conversation with parents involving nuanced judgment, trust-building, and real-time responsiveness that current AI cannot substitute for end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Parents and guardians typically expect and prefer direct human contact with educators; organizational norms and parental trust strongly favor human-led meetings, creating significant friction against automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement mandates a human specifically, but strong parental preference for human interaction and relationship-based trust creates significant organizational and social friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Any AI solution would require substantial oversight and human review to ensure appropriate handling of sensitive family information and child-specific guidance, making it more expensive than a direct conversation with the instructor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this meeting, so cost comparison favors the human by default since AI cannot deliver the output. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably conducts parent-guardian meetings as a standalone agent; this remains an entirely human-driven interaction in real-world self-enrichment settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product conducts parent-teacher progress and priority-setting conversations autonomously; this remains firmly a human interpersonal task. |
Attend professional meetings, conferences, and workshops to maintain and improve professional competence.
9CI 5–13 · exposure 0 · augmentation 38 · importance 3.5/5 · click for rater detail
Attend professional meetings, conferences, and workshops to maintain and improve professional competence.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task is fundamentally incompatible with automation; it remains a human-only activity across all sectors. There is no adoption of AI to displace attendance at professional meetings. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education and self-enrichment instruction sectors show slow AI adoption for tasks involving physical presence and professional networking, with pilots mostly limited to content tools rather than attendance replacement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist with limited pre- and post-conference tasks (summarizing sessions, flagging relevant speakers, organizing notes), but these are peripheral to the core task of attendance and engagement itself. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by summarizing conference materials, transcribing sessions, recommending relevant events, or aiding note-taking, meaningfully supporting but not replacing the attendance activity itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Attending professional meetings, conferences, and workshops is inherently a human-presence activity requiring real-time participation, networking, and interpersonal engagement. AI cannot meaningfully substitute for the act of attendance and the embodied learning that occurs. |
| Task automatability | claude-sonnet-5 | 1/5 | This task inherently requires physical or live virtual presence, networking, and human participation in professional development events; AI cannot attend on a person's behalf to fulfill the purpose of the task.'},' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Professional development and attendance requirements are often contractual or credentialing obligations tied to licensure, certification, or institutional policy. Human participation is typically mandated by professional bodies or employers. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No legal licensing barrier exists, but organizational and professional norms (continuing education requirements, credentialing bodies) require personal participation, creating moderate friction against substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task cost is primarily the human's time, travel, and registration fees—not computational work. AI involvement does not reduce these core expenses; if anything, any AI-assisted summarization would be marginal overhead. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI equivalent that performs this task, so no meaningful cost comparison exists—the task must be done by the human. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can physically attend events or replicate the networking, discussion, and credential-building value of in-person or synchronous virtual conference participation. This requires human presence. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a human's professional attendance and engagement at conferences or workshops; AI can at best summarize content afterward, not replace attendance. |
Enforce policies and rules governing students.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.7/5 · click for rater detail
Enforce policies and rules governing students.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational institutions, particularly self-enrichment environments, have not adopted AI-driven enforcement. Sectors prioritize human instructors in learner-facing roles, and there is strong cultural and regulatory preference for human accountability in discipline. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Self-enrichment education (e.g., hobby, recreation, personal development classes) is a low-digitization sector with minimal AI adoption for behavioral management. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by flagging policy violations or recording infractions, but the core act of enforcement—applying judgment, delivering consequences, and maintaining authority—remains fundamentally human. Assistance is marginal and non-transformative for this task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help draft or communicate rules and policies in advance, but offers little real-time assistance for enforcement itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Enforcing policies and rules requires contextual judgment, authority recognition, and relational understanding—tasks that current AI cannot perform autonomously. AI systems lack the legitimacy, legal standing, and real-time interpersonal presence necessary to enforce rules with students. |
| Task automatability | claude-sonnet-5 | 1/5 | Enforcing behavioral rules and policies requires real-time in-person judgment, authority, and physical presence that current AI cannot replicate for self-enrichment classes.4 |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Enforcing rules on students involves authority, pastoral care, and legal responsibility. Schools retain the in-loco-parentis duty, and teachers (not AI) must maintain the human relationship necessary for effective discipline and conflict resolution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Instructors carry legal and institutional responsibility for student conduct and safety, creating strong liability and authority barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automating enforcement would require AI deployment, monitoring infrastructure, and human oversight of AI decisions, likely exceeding the cost of a teacher managing discipline directly as part of their existing role. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably enforces classroom or institutional policies in production. While monitoring systems and alert tools exist, they do not execute enforcement (consequences, warnings, disciplinary measures) independently. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs classroom rule enforcement autonomously; this remains a human supervisory function in practice. |
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.