Substitute Teachers, Short-Term
25-3031.00Median wage $41,670/yr524,770 employed (US)Rank #637 of 923 scored · top 69% by substitution
Teach students on a short-term basis as a temporary replacement for a regular classroom teacher, typically using the regular teacher's lesson plan.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution21
Exposure21
Augmentation37
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
18 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
6%
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%23
panel mean rating 1.9/5 → substitution pressure 23/100
Technical feasibility todayw 20%18
panel mean rating 1.7/5 → substitution pressure 18/100
Cost vs. human wagew 15%22
panel mean rating 1.9/5 → substitution pressure 22/100
Adoption barriersw 20%inverted — strong barriers lower the score26
panel mean rating 4.0/5 (barrier strength) → substitution pressure 26/100
Sector adoption velocityw 10%12
panel mean rating 1.5/5 → substitution pressure 12/100
Task breakdown (18 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.
Take class attendance and maintain attendance records.
77CI 67–86 · exposure 80 · augmentation 75 · importance 4.5/5 · click for rater detail
Take class attendance and maintain attendance records.
77| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Attendance automation is deeply embedded in K-12 digital infrastructure; the vast majority of public schools have already adopted automated or digitized attendance systems, representing near-universal adoption in the education sector. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | K-12 education is a moderate-to-slow adopter of digitization overall, though attendance-tracking software is fairly common and growing, especially in larger districts. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | Automated attendance systems significantly augment substitute teacher productivity by eliminating the manual roll-call burden, allowing more instructional time and reducing cognitive load, while the human retains review and oversight capability. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Digital attendance systems significantly reduce the substitute's administrative burden and improve record accuracy while the teacher still confirms and manages the class. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Attendance recording is a straightforward data entry and record-keeping task. Current AI systems can reliably perform this through automated attendance systems (facial recognition, RFID, or simple roster input), saving substantial time compared to manual roll-call, though integration and fallback human oversight add minor friction. |
| Task automatability | claude-sonnet-5 | 4/5 | Attendance-taking is a simple data-entry task easily handled by digital rosters, scanning systems, or apps, meeting the time-saving threshold when digital tools are in place. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Schools have existing policies and systems mandating human attendance sign-off and record-keeping compliance (often legal for enrollment/funding), and some districts prefer manual verification by staff; however, the task itself is not legally restricted to licensed professionals. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement restricts this task to a certified teacher, though schools may retain human oversight for accuracy and truancy reporting compliance. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Once a school's attendance system is deployed, marginal cost per attendance record is near zero (amortized software cost), vastly cheaper than paying a substitute teacher to spend 5-10 minutes daily on manual roll-call. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated attendance systems (barcode scanners, apps) cost very little per use compared to manual teacher time, though some setup and hardware costs exist. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Mature, deployed products already perform this reliably in production: school management systems (Infinite Campus, PowerSchool, Google Classroom) with integrated attendance modules are standard in most K-12 settings. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Many schools already use deployed attendance software (e.g., scanning ID cards, app-based check-ins) that reliably tracks attendance in production today. |
Grade students' assignments and exams.
69CI 51–87 · exposure 70 · augmentation 88 · importance 3.6/5 · click for rater detail
Grade students' assignments and exams.
69| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | K–12 and higher education sectors are actively adopting AI grading tools (Gradescope, Canvas, Blackboard integrations) as standard infrastructure; adoption is measurable and accelerating, particularly for standardized and objective assessments. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderately slow-adopting sector for AI, with grading automation growing but still uneven and dependent on district policy, especially for temporary staff who lack system familiarity. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically augments teacher productivity by automating routine grading, providing instant feedback to students, and flagging outliers for human review, allowing teachers to focus on high-value instruction and personalized intervention. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly speed up grading of objective and semi-objective assignments, and even provide feedback drafts for essays, letting the substitute teacher review and finalize faster than manual grading alone. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | AI systems can automatically grade objective assessments (multiple choice, short-answer matching) at scale and with high accuracy, and can provide rubric-based scoring for essays and longer-form work, easily meeting the 50% time-saving threshold at equal or better quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can grade objective assignments (multiple choice, short-answer, math) reliably, but essay grading and nuanced judgment on student work still require human oversight for accuracy and fairness, especially in a short-term substitute context where teacher rubrics may not be shared. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal legal or regulatory barriers exist; grading is not a licensed function, and many schools already use automated grading systems, though some districts retain human review requirements and parents may prefer human oversight. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Grading often requires final sign-off by a certified teacher for grade accuracy and fairness, and schools may have policies restricting automated grading, but no hard licensing barrier explicitly bars using AI tools for grading assistance. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI grading costs pennies per assignment after initial setup, while a substitute teacher's loaded wage for grading time is typically $15–30/hour; the cost ratio is multiple orders of magnitude in AI's favor. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Once set up, AI grading tools cost pennies per assignment compared to a substitute's hourly wage, though integration overhead for a short-term substitute limits full realization of savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products (Gradescope, learning management systems with AI grading modules, and specialized EdTech platforms) perform this task in production at scale; minor limitations exist for highly subjective or context-dependent assignments, but objective grading is mature. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Gradescope, Turnitin, and various AI grading tools are deployed in schools for standardized grading, but for a substitute teacher stepping into an unfamiliar classroom, using these tools reliably without established rubrics or system access is inconsistent. |
Restock teaching materials or supplies.
50CI 24–76 · exposure 53 · augmentation 13 · importance 3.6/5 · click for rater detail
Restock teaching materials or supplies.
50| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | K-12 schools remain low-tech relative to corporate or warehouse environments. Robotic restocking is rarely deployed in schools, and adoption is minimal. Most schools rely on human staff or volunteers for this task. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially routine physical classroom management tasks, sees minimal AI-driven automation or displacement in practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist with inventory tracking and demand forecasting for supplies, reducing search time for substitute teachers. However, the physical restocking itself offers limited augmentation opportunity once the human is already moving materials. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for physically locating, retrieving, and organizing teaching supplies. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Restocking teaching materials or supplies is a straightforward logistical task—identifying items, counting inventory, and moving stock to designated locations. Autonomous systems (robotic arms, mobile robots, or automated inventory management) can perform this end-to-end with significant time savings and equal or better accuracy. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical logistics task (locating, moving, and organizing supplies) with no digital content generation component, so current AI systems cannot meaningfully perform it end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers exist for automating supply restocking in schools. Main friction is organizational (classroom access, staff preference for human workers, minimal robotics adoption in K-12 budgets), but nothing prevents substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier prevents automating this specific task, but it's embedded within a broader human role and requires physical presence in a classroom. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated restocking (robotics or AI-driven inventory systems) has substantially lower per-unit cost than paying substitute teachers or support staff for material handling, especially at scale across multiple classrooms or schools. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for physically restocking supplies, so any AI-based approach (e.g., robotics) would be far more costly than a human doing it directly. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Robotic restocking and inventory management systems are deployed in warehouses and large institutional settings today. However, classroom-specific restocking (navigating crowded spaces, handling diverse item types, respecting classroom layouts) remains less standardized, limiting consistent production deployment in school environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product restocks physical classroom materials; this requires physical manipulation that only robotics could address, and no such robotics deployment exists in classrooms. |
Organize and supervise games or other recreational activities.
34CI 0–69 · exposure 41 · augmentation 50 · importance 4.3/5 · click for rater detail
Organize and supervise games or other recreational activities.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 education is a laggard sector for AI adoption; budgets are tight, technical infrastructure is inconsistent, and cultural preference for human supervision remains strong, resulting in slow and shallow actual deployment despite conceptual feasibility. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially in-person physical supervision roles, is a low-digitization sector with minimal AI adoption for direct child oversight tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly assist substitute teachers by suggesting age-appropriate activities, tracking participation and safety metrics in real time, and auto-generating incident reports, substantially raising teacher productivity while keeping humans responsible for judgment and enforcement. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help plan or suggest game ideas and activity structures in advance, but offers no real-time assistance during the actual supervision task. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Organizing and supervising recreational games can be substantially automated through AI-driven scheduling systems, activity selection algorithms, safety monitoring (via computer vision), and automated rule enforcement—delivering equivalent or better organization with minimal human oversight, meeting the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical presence, real-time supervision of children, and dynamic responses to safety and behavior issues that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Schools have liability concerns and prefer human presence for child safety and duty of care; regulatory requirements often mandate staff supervision of minors, and institutional risk aversion creates friction, though no strict legal bar prevents automation of task components. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Child supervision requires a legally responsible, background-checked adult present; liability, safety regulations, and duty-of-care requirements make this a hard barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI infrastructure for activity organization and monitoring (software licensing, camera systems, oversight) is significantly cheaper per session than paying substitute teachers hourly wages, especially when amortized across multiple sessions and classrooms. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the physical supervisory presence required, so there is no viable AI cost comparison—human presence is mandatory. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist for activity scheduling and AI-powered supervision dashboards, but real-world deployment in schools remains limited; vision-based safety monitoring and autonomous activity management face technical limitations and institutional adoption barriers, though pilots demonstrate feasibility. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product supervises or organizes in-person physical recreational activities for students; this remains firmly outside current AI product capabilities. |
Distribute or collect tests or homework assignments.
34CI 25–44 · exposure 33 · augmentation 38 · importance 4.3/5 · click for rater detail
Distribute or collect tests or homework assignments.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While some schools use digital assignment systems, many K-12 institutions—especially those relying on substitute teachers—still operate largely on paper-based workflows. Adoption remains slow in the broader substitute teaching context, where consistency and integration challenges persist. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a famously slow-adopting sector for AI-driven classroom logistics, especially in physical tasks, though digital assignment platforms have moderate penetration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | LMS platforms and assignment tracking tools meaningfully assist teachers by automating digital tracking and archival, reducing manual grading load. However, the physical distribution and collection aspects remain teacher-dependent, limiting overall productivity gain. |
| Augmentation potential | claude-sonnet-5 | 2/5 | LMS tools can help organize and track assignments, offering some assistance, but this specific physical/logistical task sees limited AI augmentation for a substitute physically present in class. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI systems cannot physically distribute or collect physical materials in a classroom setting. While digital distribution of assignments is automatable, most K-12 classroom contexts still involve physical handouts and paper collection, which requires human presence and cannot be meaningfully automated to meet the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 3/5 | The physical act of distributing/collecting papers requires embodiment, but if assignments are digital, AI/LMS platforms can already automate distribution and collection with high time savings.'Half automatable' depends heavily on paper vs digital format. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Schools have institutional policies on assignment handling, grading workflows, and data management that constrain rapid automation adoption. Additionally, teacher unions and standard practice still expect human teachers to manage classroom materials directly, creating organizational and cultural friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No legal or licensing requirement mandates a human specifically hand out papers, but classroom supervision requirements and the substitute's on-site physical role create moderate structural barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Implementing an LMS or assignment management tool requires upfront institutional cost and integration overhead. For a substitute teacher performing ad-hoc distribution and collection, the per-task AI cost (including setup and maintenance) exceeds the low marginal cost of a human performing the task once. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | For physical distribution, no AI system replaces the human's physical presence, so cost comparison favors the human already on-site; digital tools are cheap but require a human still present for classroom management. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Learning management systems can automate digital assignment distribution and collection, but these are narrow in scope and don't address the physical handling requirement in most K-12 classrooms. Deployed products exist for digital workflows but do not cover the full task as it occurs in typical classroom practice. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Physical classrooms still largely rely on paper handouts and human distribution; digital LMS systems (Google Classroom, Canvas) do this reliably but are not universally deployed for all substitute teaching contexts. |
Operate equipment such as computers or audio-visual aids to supplement presentations.
33CI 14–52 · exposure 41 · augmentation 50 · importance 4.1/5 · click for rater detail
Operate equipment such as computers or audio-visual aids to supplement presentations.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K-12 education adoption of classroom automation is slow; most schools lack the IT infrastructure, budget, and digitization maturity of information-sector firms, and teacher unions and institutional inertia further slow deployment of such systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a slow-adopting, physically-grounded sector with minimal AI-driven automation of in-classroom equipment operation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by automatically cueing slides, managing audio levels during live instruction, or suggesting troubleshooting steps, meaningfully raising teacher efficiency on the mechanical parts of equipment operation while the teacher remains in control of pacing and content delivery. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can help prepare digital materials or troubleshoot software issues in advance, offering moderate assistance, though the hands-on operation during class remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Operating standard classroom equipment (projectors, audio systems, computers) is largely mechanical and procedural; AI agents can reliably power on devices, load presentations, adjust volumes, and troubleshoot common issues, achieving significant time savings. However, live troubleshooting of unexpected technical failures and adaptive responses to classroom context still require human judgment, preventing a full 5 rating. |
| Task automatability | claude-sonnet-5 | 2/5 | Operating classroom equipment requires physical presence, real-time troubleshooting, and adapting to students in a room, which current AI cannot perform end-to-end.4 Only ancillary aspects like generating slides or content could be offloaded, not the physical operation itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | School districts have strict IT policies, device lockdown, curriculum-approval processes, and union/employment rules that limit automation; teachers are also required by contracts to be present and responsible for instruction, creating legal and organizational barriers to full substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing specifically covers equipment operation, but school supervision policies and the need for an adult present in the room create organizational and safety-related friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI automation solutions for classroom tech require upfront licensing, integration, and IT oversight costs that often exceed the modest hourly wage of a substitute teacher, especially when accounting for setup and per-classroom deployment. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for physically operating equipment in a classroom, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist (e.g., classroom automation systems, AI-driven presentation tools) but most are not yet mature or deployed at scale in schools; most substitute teachers still manually operate equipment rather than using AI-assisted systems, and reliability gaps remain in diverse classroom environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product operates physical classroom AV equipment or computers on behalf of a substitute teacher; this remains a hands-on, in-person task. |
Tutor or assist students individually or in small groups.
33CI 25–40 · exposure 25 · augmentation 63 · importance 4.3/5 · click for rater detail
Tutor or assist students individually or in small groups.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | EdTech adoption is growing, and many schools pilot AI tutoring tools, but replacement of in-person small-group tutoring remains limited. Most adoption is supplementary or in high-digitization (online learning) contexts rather than deep displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a comparatively slow-adopting sector for classroom-replacing AI, though AI tutoring aids are gaining pilot usage; substitute teaching itself remains largely unaffected. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can effectively assist human tutors by generating personalized problems, explaining concepts, and offering diagnostic hints. Many teachers use AI to prepare lessons and respond to student questions faster, materially raising their productivity. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can help a substitute teacher plan quick lessons, generate practice problems, or explain material differently to individual students, providing useful in-the-moment support. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Tutoring requires real-time interaction, pedagogical judgment, and responsiveness to individual student needs. While AI can generate explanations and practice problems, it cannot reliably match human tutors' ability to diagnose gaps, adjust pacing, and provide motivation—current systems fall well short of the 50% time-saving-at-equal-quality bar. |
| Task automatability | claude-sonnet-5 | 2/5 | AI tutoring tools can handle some content explanation, but managing classroom dynamics, in-person rapport, and adapting to specific children's needs in real time is beyond current end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Schools and parents often prefer human interaction for trust, accountability, and relationship-building. There is no hard legal barrier, but organizational friction and perceived quality gaps create meaningful adoption friction. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Substitute teachers require certification/background checks in most jurisdictions, must be physically present for supervision and safety, and schools have strong liability and child-safety requirements preventing full AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI tutoring inference is very cheap (cents per session) compared to a human tutor's loaded wage ($25–50+/hour). Even with integration and oversight overhead, AI is substantially cheaper per student-session. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI tutoring software is cheap per interaction, it cannot replace the physical presence and supervisory function required, so an AI-only substitute is not a viable comparison to the human's full cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed tutoring AI products exist (e.g., ChatGPT, specialized edtech), but they lack robust assessment of learning, struggle with complex problem-solving, and often require close human monitoring. No production system reliably tutors students without material error rates or significant adult oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tutoring products (e.g., Khanmigo) exist and are deployed in some schools, but they are supplementary tools, not substitutes for a substitute teacher's in-person, physical, and behavioral management role. |
Answer students' questions.
28CI 23–32 · exposure 25 · augmentation 63 · importance 4.7/5 · click for rater detail
Answer students' questions.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational institutions adopt change slowly; substitute teaching remains a heavily human role with high trust/credentialing barriers. Adoption of AI as the primary question-answerer in classrooms is minimal and limited to pilot programs. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a comparatively slow-adopting sector for classroom-replacing AI, with pilots more common in supplemental tutoring than in live substitute-teaching roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist substitute teachers by providing instant, accurate answers to factual questions (science, history, math definitions), freeing the teacher to focus on classroom management and deeper explanation, thereby improving their productivity and student experience. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can help substitute teachers quickly look up answers, explanations, or lesson content to support responding to student questions, offering moderate productivity assistance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Answering factual student questions is partly automatable (LLMs can provide accurate information), but classroom context—differentiating explanation by student level, addressing misconceptions, assessing understanding, and managing behavioral/emotional dimensions—requires human judgment and adaptation that current AI struggles to reliably perform end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | Answering diverse student questions requires real-time classroom presence, behavior management, and adaptive judgment that current AI cannot autonomously replicate in a live classroom setting, though it can handle narrow factual Q&A. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: schools require licensed or credentialed staff to lead classes, parents and districts expect human teachers for safety/accountability, and liability for student welfare rests on humans. Regulatory and organizational friction prevent AI substitution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Schools require an adult physically present and responsible for students, and there are strong liability, safeguarding, and certification requirements that prevent full AI substitution for this in-person role. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI inference for answering questions is extremely cheap (pennies per interaction), while a substitute teacher's loaded wage is $150–250+ per day. Even accounting for oversight and integration, the cost ratio heavily favors AI. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI query costs are cheap, deploying it as a substitute for live in-person question answering still requires a human present for supervision and liability, so effective cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Chatbots and educational AI tools exist and can answer factual questions, but they lack the real-time classroom responsiveness, personalization, and credibility needed for live teaching. No mainstream product reliably replaces human question-answering in a substitute-teacher role at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tutoring products (chatbots, homework helpers) can answer academic questions but are not deployed as the primary in-classroom responder for a substitute teacher's real-time, mixed-topic, behaviorally-contextualized questions. |
Distribute teaching materials, such as textbooks, workbooks, papers, and pencils, to students.
13CI 10–15 · exposure 0 · augmentation 0 · importance 4.2/5 · click for rater detail
Distribute teaching materials, such as textbooks, workbooks, papers, and pencils, to students.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools remain low-digitization, physically-anchored environments with minimal automation of classroom operations; adoption of physical task automation in education is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially physical classroom logistics, is a low-digitization, slow-adopting sector for physical task automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Material distribution is a straightforward physical task with no meaningful way AI could assist a human in performing it more productively. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical act of distributing textbooks, workbooks, and pencils to students. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This is a physical task requiring in-person presence in a classroom to hand out materials to students. Current AI systems cannot perform physical distribution of objects, and the task offers no remote or automation-friendly alternative. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring a human to be present in a classroom to hand out physical objects to students; no AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | The in-person requirement and classroom safety/supervision duties create a mild barrier, though no formal licensing or legal requirement explicitly prevents substitution—only practical constraints. |
| Adoption barriers | claude-sonnet-5 | 3/5 | There's no licensing requirement specifically for handing out papers, but physical presence in a classroom with children creates practical and supervisory expectations that favor humans. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying any hypothetical physical automation system would vastly exceed the minimal wage cost of a substitute teacher performing this routine task for a few minutes each class period. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for physical distribution, so any hypothetical robotic solution would be far more expensive than simply having a human do it. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products perform this task. The requirement for physical manipulation and classroom presence makes it outside the scope of existing automation technology. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product distributes physical classroom materials to students; this remains entirely research-stage or nonexistent for this specific use case. |
Teach social skills to students, such as communication, conflict resolution, and etiquette.
9CI 0–19 · exposure 13 · augmentation 50 · importance 4.3/5 · click for rater detail
Teach social skills to students, such as communication, conflict resolution, and etiquette.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools remain among the slowest-adopting sectors for automation. Budget constraints, regulatory oversight, union agreements, and cultural resistance to replacing teachers with AI mean substitute-teacher automation has seen virtually no real-world deployment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially substitute teaching, is a low-digitization, in-person sector with minimal AI agent deployment for direct instructional delivery. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist a substitute teacher by providing lesson planning templates, social-skills activity prompts, or real-time communication tips, moderately raising their effectiveness. However, the human teacher remains essential to modeling, coaching, and adapting to live classroom interaction. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help a substitute teacher prepare lesson materials, role-play scripts, or conflict-resolution scenarios in advance, offering moderate assistance despite not performing the live task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Teaching social skills requires real-time interaction, modeling, and adaptation to individual student responses—capabilities current AI systems lack in embodied classroom settings. While AI could generate lesson materials or scripts, the core task of interactive skill-building and behavioral feedback cannot be fully automated today. |
| Task automatability | claude-sonnet-5 | 1/5 | Teaching social skills requires live interpersonal modeling, classroom management, and real-time responsiveness to student behavior that current AI cannot replicate end-to-end in a physical classroom setting. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal and regulatory requirements mandate that a licensed or certified educator must directly supervise classroom instruction and student welfare. Schools have duty-of-care obligations and contractual commitments to employ human staff, creating hard barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Schools require in-person, often certified or background-checked adults physically present with children, creating strong regulatory, safety, and child-supervision barriers to AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Deploying an AI system to physically substitute for a teacher—with necessary hardware, integration, and oversight—would cost more than paying a substitute teacher, especially for short-term assignments. Real-time video monitoring and intervention systems remain expensive. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-only substitute performing this task, so cost comparison favors the human by default since the AI alternative doesn't functionally exist for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably teaches social skills in real classrooms as a substitute. Chatbots and educational software exist but are narrow, lack embodied presence, and cannot manage classroom dynamics or provide credible modeling of emotional/social competencies. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a substitute teacher delivering in-person social skills instruction; AI tools exist only as supplementary content generators, not classroom-facilitating agents. |
Follow lesson plans designed by absent teachers.
7CI 0–14 · exposure 8 · augmentation 25 · importance 4.6/5 · click for rater detail
Follow lesson plans designed by absent teachers.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | K–12 education has low AI adoption for core instruction; schools are hesitant to automate teacher presence due to safety, social, and regulatory concerns. No meaningful production deployments exist. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a low-digitization, in-person sector with minimal AI-driven displacement of physical classroom supervision roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist a human substitute teacher by providing quick answers to student questions or helping grade assignments, but the task itself—managing a live classroom—requires a human presence and judgment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help a substitute quickly understand or clarify lesson plan content beforehand, but offers little real-time assistance during actual classroom delivery. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Following a lesson plan requires real-time classroom management, student engagement, and responsiveness to unexpected situations that current AI cannot perform in a physical classroom setting. AI cannot simultaneously monitor multiple students, manage behavior, read the room, and adapt delivery—the core of this task. |
| Task automatability | claude-sonnet-5 | 2/5 | Following a lesson plan requires live classroom management, real-time student interaction, and adaptive judgment that current AI cannot perform end-to-end in a physical classroom setting.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Schools have strong legal and duty-of-care requirements that an adult must supervise students; substitute teachers must hold credentials in most jurisdictions, and liability for student safety is non-delegable to AI systems. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Schools require background-checked, often licensed or credentialed adults physically supervising minors, plus child safety and liability regulations that block full AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of an AI system that could manage a classroom (hardware, deployment, oversight, liability) would far exceed the hourly wage of a substitute teacher, which is typically $75–$150 per day depending on region. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI system replacing the human presence needed, 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 substitutes for a human classroom instructor today. While AI tutoring systems exist, they operate in one-on-one or small-group settings with pre-structured content, not as a classroom instructor managing 20–30+ students through a lesson plan. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for an in-person teacher executing another teacher's lesson plan with students; this remains a human physical-presence task. |
Counsel students with adjustment or academic problems.
4CI 0–9 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Counsel students with adjustment or academic problems.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Education remains a laggard sector for high-stakes AI automation, and student counseling is among the most human-contact-intensive, emotionally sensitive tasks in schools. Actual production deployment of AI for student counseling is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially substitute teaching, is a low-digitization, in-person sector with minimal AI adoption for direct student counseling tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with minor administrative scaffolding (e.g., summarizing notes, suggesting resources) but offers limited practical augmentation for the core counseling task, which depends on trust, emotional attunement, and human judgment that AI assistants cannot substantially enhance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help a substitute teacher access background info or suggest general strategies for handling adjustment issues, but this is peripheral, low-impact assistance for a task centered on human interaction. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Counseling students requires empathetic listening, individualized assessment of emotional/academic issues, and adaptive interpersonal judgment that current AI cannot meaningfully replicate. The task demands real-time relationship-building and situational responsiveness that go far beyond what today's systems can execute end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | Counseling students on personal adjustment or academic problems requires nuanced human judgment, empathy, trust-building, and situational awareness that current AI cannot replicate end-to-end for a substitute teacher's in-person role. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal, ethical, and regulatory barriers exist: schools have duty-of-care obligations, counseling often touches on mental health (requiring licensed professionals), parental expectations strongly favor human contact, and liability for AI-driven counseling errors creates hard organizational friction. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Schools have strong duty-of-care obligations, child safeguarding policies, and liability concerns that require a responsible adult to handle student welfare issues directly. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While inference costs are low, the integration, oversight, and liability exposure for an AI system handling student counseling would be substantial and likely exceed the marginal cost of a substitute teacher's time for this task component. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since AI cannot reliably perform this task, there is no viable cost comparison—any attempt would require human oversight negating cost savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs student counseling for adjustment or academic problems as a substitute for human counselors or teachers. Chatbots exist but lack the contextual understanding, safety accountability, and trust necessary for this sensitive task in school settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs student counseling autonomously in a classroom setting; chatbot-based mental health tools exist but are not integrated into substitute teacher workflows for this purpose. |
Attend professional meetings, educational conferences, or teacher training workshops to improve professional competence.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.5/5 · click for rater detail
Attend professional meetings, educational conferences, or teacher training workshops to improve professional competence.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task involves intrinsic human participation that cannot be displaced. Sectors have not and cannot adopt AI to attend meetings on behalf of humans, as the activity is fundamentally about human professional growth. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Substitute teaching and in-person professional development are low-digitization, low-AI-adoption contexts with minimal movement toward automation of attendance-based activities. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide modest assistance by summarizing conference materials, scheduling attendance, or drafting reflection notes afterward, but these are peripheral to the core task of attending and engaging directly. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help summarize conference materials, suggest relevant workshops, or provide follow-up notes, but it doesn't meaningfully enhance the act of attending and engaging in person. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Attending meetings, conferences, and workshops is fundamentally a human-presence activity requiring physical or synchronous participation. AI cannot replace the experiential, networking, and reflective dimensions that define professional development attendance. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically or interactively attending meetings and training sessions to build professional competence is an experiential, human-presence activity that AI cannot perform on someone's behalf. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Professional development attendance is often mandated by employment contracts, state licensing requirements, and institutional policies. Human presence and documented participation are legal or regulatory prerequisites in many jurisdictions. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Professional development often has institutional, certification, or continuing-education requirements tied to the individual human attending, creating strong structural barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task inherently involves human participation costs (time, travel, registration fees). AI systems have no role in attending, so comparative cost analysis is moot; human attendance is mandatory. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task, so cost comparison favors the human by default since AI cannot deliver the outcome at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can physically attend events or participate as a human attendee. AI systems cannot substitute for the embodied presence required at conferences and training workshops. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product attends conferences or workshops as a substitute for a human professional; this remains outside current AI product scope. |
Enforce school and class rules to maintain order in the classroom.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.7/5 · click for rater detail
Enforce school and class rules to maintain order in the classroom.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | K–12 education operates in heavily regulated environments with strong norms around in-person instruction and adult supervision; adoption of autonomous AI discipline is negligible and faces deep institutional resistance. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially in-person classroom management, is a low-digitization, human-presence-dependent sector with minimal AI displacement of this specific function. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist a teacher via monitoring systems that flag disruptive events or provide suggestions, but current systems offer minimal real-time assistance for the dynamic judgment required in active classroom management. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI tools (e.g., behavior tracking apps, communication aids) offer minor support in logging incidents or notifying staff, but do not meaningfully enhance real-time rule enforcement. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Classroom discipline requires real-time judgment, situational awareness, and adaptive responses to student behavior—tasks involving social interaction, emotional intelligence, and physical presence that current AI systems cannot perform end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | Maintaining classroom discipline and order requires physical presence, real-time authority, and interpersonal judgment that current AI cannot replicate or perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Schools and districts have legal, custodial, and duty-of-care obligations that mandate a licensed/designated human adult physically present and responsible for classroom management and student safety. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Supervising children and enforcing conduct rules requires an authorized, legally responsible adult present in the room; schools mandate certified/vetted personnel for this duty. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A substitute teacher performs this task in person as part of their core role; the cost of any AI monitoring system plus human oversight would far exceed the wages already paid for human classroom presence. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this task, so cost comparison favors the human by default since AI cannot perform it at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably enforces classroom rules autonomously; this requires a human authority figure physically present in the classroom, which no AI system can substitute for today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product manages in-person classroom behavior enforcement; this remains entirely a human, physically-present task. |
Provide students with disabilities with assistive devices, supportive technology, or assistance accessing facilities, such as restrooms.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.5/5 · click for rater detail
Provide students with disabilities with assistive devices, supportive technology, or assistance accessing facilities, such as restrooms.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational institutions remain low in AI adoption for direct student services, and this task requires human contact and physical intervention—sectors and task types showing minimal AI displacement today. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially physical in-person care tasks, is a low-digitization sector with minimal adoption of AI for hands-on student assistance. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could offer limited assistance, such as helping identify which assistive technologies suit specific disabilities via decision-support or alerting staff to accessibility compliance gaps, but it does not substantially transform a substitute teacher's ability to perform direct provision of devices or facility access. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-powered assistive technology (e.g., communication devices, screen readers) can support students with disabilities, but the substitute teacher's direct physical assistance role sees little augmentation from AI tools. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical presence, real-time responsiveness to individual student needs, and direct assistance or device provision that cannot be meaningfully automated by current AI systems. It fundamentally depends on human interaction and physical capability. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires direct physical assistance and in-person supervision of students with disabilities, which current AI cannot perform as it has no physical embodiment or presence in the classroom. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task has hard legal and regulatory barriers: educators must comply with IDEA and ADA requirements, and a qualified human must directly verify accessibility needs and provide/facilitate use of accommodations. Duty-of-care and liability requirements mandate human oversight. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Legal requirements under IDEA/ADA and school district policy mandate qualified, supervised human staff for student safety, physical assistance, and disability accommodations, making this a hard barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems cannot execute the physical and interpersonal aspects of this task, making the cost-per-equivalent impossible to achieve at any meaningful discount relative to human labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the physical human presence required, so there is no viable AI cost comparison; a human must be present regardless. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs this task end-to-end; current systems cannot independently provide physical devices, escort students to facilities, or make real-time accommodations decisions in an educational setting. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product provides hands-on physical assistance to students, escorts them to facilities, or handles assistive devices in real classrooms. |
Supervise students during activities outside the classroom, such as recess, lunch, and field trips.
0CI 0–0 · exposure 0 · augmentation 13 · importance 4.4/5 · click for rater detail
Supervise students during activities outside the classroom, such as recess, lunch, and field trips.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Schools operate under strict regulatory and liability frameworks that prevent substitution of human supervision with technology; adoption of AI for this task remains near zero across all sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a low-digitization sector with no adoption of AI for physical child supervision, and none is anticipated given safety and legal constraints. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Limited assistance possible—AI cameras could flag unusual behavior for human review, but supervisors need immediate judgment and intervention capability, so augmentation remains marginal. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the moment-to-moment physical supervision and safety monitoring this task requires. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Supervising students requires real-time physical presence, active hazard detection, behavioral intervention, and duty-of-care accountability that current AI cannot discharge. No meaningful end-to-end automation is feasible for this safety-critical task. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical, real-time supervision of children outside the classroom requires embodied presence, safety monitoring, and immediate physical intervention capability that no current AI system possesses. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal duty-of-care requirements, liability exposure, state education codes, and mandatory adult-student ratios create hard barriers requiring a licensed or authorized human to physically supervise students. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Schools have strict legal, licensing, and liability requirements mandating adult human supervision of minors, especially off-campus during field trips. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems offer no cost substitute for a human supervisor because the task fundamentally requires physical presence and legal accountability; the all-in cost of any partial AI tool would exceed that of a human's wages. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute offering any cost comparison since the task requires physical presence and legal responsibility for child safety. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably supervises children during unstructured activities or assumes liability for their safety. This task remains entirely dependent on human presence and judgment in production settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product supervises children during recess, lunch, or field trips; this remains entirely a human physical-presence task. |
Assist students with boarding or exiting school buses.
0CI 0–0 · exposure 0 · augmentation 0 · importance 4.3/5 · click for rater detail
Assist students with boarding or exiting school buses.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This is a physical, in-person task in a heavily regulated sector (K–12 education) with strong human-contact and duty-of-care requirements. Adoption of AI for bus supervision is essentially nonexistent and infeasible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education, especially physical safety supervision roles, shows minimal AI adoption for hands-on student safety tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI cannot meaningfully assist a human in the act of supervising physical boarding/exiting of students. The task is inherently human-dependent and does not lend itself to AI augmentation. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical act of helping children on/off buses; this is an inherently manual, in-person duty. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical presence and direct interaction with children during a safety-critical moment. Current AI systems cannot physically assist students boarding or exiting buses, nor can they supervise the physical environment in real-time to prevent accidents. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical supervisory task requiring real-time presence to guide, assist, and ensure child safety near vehicles; no AI system can physically assist students boarding buses. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | School transportation involves child safety and duty-of-care obligations. Adults must legally supervise boarding and exiting; liability and regulatory requirements around student safety are high barriers to any non-human alternative. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Child safety, supervision liability, and legal requirements for adult oversight of minors near vehicles create hard barriers against any automation of this task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task is not automatable by current AI, making any cost comparison moot. A human monitor is legally and practically necessary, and AI offers no substitute. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for physical presence, so any AI cost comparison is moot—human labor is the only functional option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can perform or assist with the physical boarding/exiting of students from buses. This task fundamentally requires embodied, in-person human supervision. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical student supervision or assistance at bus loading zones; this remains purely a human task. |
Teach a variety of subjects, such as English, mathematics, and social studies.
0CI 0–0 · exposure 0 · augmentation 38 · importance 4.2/5 · click for rater detail
Teach a variety of subjects, such as English, mathematics, and social studies.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | K–12 education is a laggard sector for AI automation adoption. Substitute teaching occurs in deeply human-centric, regulated institutional settings where school boards, unions, and legal frameworks strongly prefer licensed humans. No material displacement by AI is evident in production data. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | K-12 education is a low-digitization, highly regulated, in-person sector with minimal AI-driven displacement of classroom supervision roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can marginally assist a substitute teacher by generating lesson plans, pre-made materials, or grading rubrics, but these supports are peripheral to the core act of teaching—live instruction, classroom presence, and student engagement. Augmentation is limited because the human still bears full instructional and supervisory burden. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help a substitute teacher prepare lesson materials, explanations, or quizzes on the fly across subjects, but does not touch the core supervisory/delivery task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Teaching a classroom of students requires real-time interaction, classroom management, adaptive pedagogical judgment, and emotional presence that current AI systems cannot replicate end-to-end. While AI can generate lesson materials or grade assignments, it cannot supervise, engage, assess, and guide diverse learners simultaneously in a live classroom environment. |
| Task automatability | claude-sonnet-5 | 1/5 | Substitute teaching requires live classroom management, supervision of minors, and physical presence, none of which current AI systems can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal and regulatory barriers are high: school districts require a licensed human teacher (or state-certified substitute) to be present and responsible for instruction, duty of care, and safeguarding. Child welfare, legal liability, and mandatory reporting duties create strict licensing and human-presence requirements. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Schools require certified/background-checked adults to supervise minors on-site, a hard legal and safety barrier that cannot be replaced by software. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The all-in cost of a substitute teacher (typically $100–$200/day) remains far below the cost of developing, hosting, maintaining, and overseeing a reliable AI classroom system with necessary oversight, technical support, and liability coverage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI product replacing this in-person supervisory/instructional role, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably teaches a full classroom of students across multiple subjects with the breadth and responsiveness required. Chatbots and tutoring systems exist but operate in narrow, one-on-one, asynchronous contexts—not managing classroom dynamics, attendance, behavioral issues, and heterogeneous learning needs. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a human's physical presence and behavioral management in a K-12 classroom setting today. |
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.