Teaching Assistants, Preschool, Elementary, Middle, and Secondary School, Except Special Education

25-9042.00

Rank #552 of 923 scored · top 60% by substitution

Assist a preschool, elementary, middle, or secondary school teacher with instructional duties. Serve in a position for which a teacher has primary responsibility for the design and implementation of educational programs and services.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution25

Exposure22

Augmentation40

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

28 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

11%

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%22

panel mean rating 1.9/5 → substitution pressure 22/100

Cost vs. human wagew 15%23

panel mean rating 1.9/5 → substitution pressure 23/100

Adoption barriersw 20%inverted — strong barriers lower the score39

panel mean rating 3.4/5 (barrier strength) → substitution pressure 39/100

Sector adoption velocityw 10%15

panel mean rating 1.6/5 → substitution pressure 15/100

Task breakdown (28 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.

CI 84–97 · exposure 92 · augmentation 63 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	K–12 education, particularly in tech-forward districts and higher-income schools, is adopting automated attendance systems at a rapid pace. Integration with learning management systems and student information platforms is now common, though laggard districts and small schools still rely on manual processes.
Sector adoption velocity	claude-sonnet-5	4/5	K-12 schools have broadly adopted digital attendance and SIS platforms over the past decade, making this one of the more digitized administrative tasks in education despite the sector's overall slower AI adoption.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI assists teaching assistants and teachers by automating data capture and reducing manual entry errors, enabling staff to focus on instruction and student engagement rather than administrative overhead. The human remains in oversight, but productivity gains are substantial.
Augmentation potential	claude-sonnet-5	3/5	Digital tools assist by automating record-keeping and flagging patterns, but the assistant may still need to physically observe and confirm student presence.
Task automatability	claude-haiku-4-5-20251001	5/5	Attendance tracking is a straightforward administrative task involving data entry and record-keeping. Modern AI systems (including attendance apps with facial recognition, QR scanning, or integration with student information systems) can perform this end-to-end with significant time savings and equal or better accuracy compared to manual entry.
Task automatability	claude-sonnet-5	4/5	Attendance tracking is a structured, repetitive data-entry task that off-the-shelf systems (badge scans, apps, digital rosters) already automate substantially, though a human often still verbally confirms presence.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Some schools have contractual requirements for human attendance verification or district policies favoring human oversight, but there are no legal mandates requiring a human to take attendance. Technology adoption varies by district wealth, and some organizations prefer human verification for accountability, introducing modest friction.
Adoption barriers	claude-sonnet-5	1/5	No licensing or legal requirement mandates a human specifically take attendance; it's an administrative task with minimal liability or regulatory constraint.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	AI-based attendance systems cost pennies per student per day after initial setup, whereas a teaching assistant spending 5–10 minutes daily on attendance carries a loaded hourly wage cost; the cost advantage is at least tenfold.
Cost vs. human wage	claude-sonnet-5	5/5	Automated attendance software costs a small fraction of the labor time a teaching assistant would spend manually recording and maintaining records.
Technical feasibility today	claude-haiku-4-5-20251001	5/5	Deployed products already handle automated attendance reliably in production: facial recognition systems, mobile apps, and integration with learning management systems (Canvas, Blackboard, Google Classroom) are in active use in schools and districts at scale.
Technical feasibility today	claude-sonnet-5	5/5	Digital attendance and school information systems (e.g., PowerSchool, Google Classroom integrations) are widely deployed and reliably used in production across schools today.

Type, file, and duplicate materials.

CI 70–95 · exposure 87 · augmentation 63 · importance 3.6/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Schools are generally slow adopters of automation relative to corporate sectors; many remain paper-heavy or fragmented in digitization. While some districts have adopted RPA and document management, the sector lags compared to finance or professional services.
Sector adoption velocity	claude-sonnet-5	3/5	While office software adoption is universal, school administrative environments often lag in adopting newer AI-driven document workflows relative to fast-moving sectors like finance or tech.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can meaningfully assist by auto-sorting documents, suggesting file hierarchies, or batch-processing duplicates, raising a teaching assistant's efficiency on these routine tasks. However, augmentation is modest because the tasks are low-cognitive-demand and largely automatable rather than human-judgment-dependent.
Augmentation potential	claude-sonnet-5	4/5	AI tools like templates, autocomplete, and document generation significantly speed up an assistant's clerical work even when a human remains involved in oversight or content decisions.
Task automatability	claude-haiku-4-5-20251001	4/5	Typing, filing, and duplicating materials are largely mechanical, well-defined tasks that current AI-integrated systems and RPA can handle end-to-end. Document scanning, OCR, file organization, and print-queue management are mature automation targets, easily achieving >50% time savings.
Task automatability	claude-sonnet-5	5/5	Typing, filing, and duplicating materials are highly routine digital/administrative tasks that current AI and office automation tools can fully handle with equal or better quality and major time savings.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Schools face modest organizational friction (IT procurement, staff resistance, change management) but no legal, licensing, or liability barriers prevent automation. Human contact is not required; adoption is primarily constrained by institutional inertia rather than regulation.
Adoption barriers	claude-sonnet-5	1/5	There is no licensing, legal, or human-contact requirement for typing or duplicating materials; it's purely clerical with no regulatory protection.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	Automation cost (cloud storage, multi-function printer maintenance, RPA licensing) is substantially lower than the loaded wage of a teaching assistant performing these clerical duties full-time; order-of-magnitude savings are achievable.
Cost vs. human wage	claude-sonnet-5	5/5	Software-based document creation, filing, and duplication costs a fraction of a cent per task compared to paying a human teaching assistant's wage for the same work.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed products (document management systems, RPA platforms, cloud storage with automated sorting) reliably perform these tasks in educational settings today. Multi-function printers with job-queue automation and file-sync services are production-grade and widely available.
Technical feasibility today	claude-sonnet-5	5/5	Word processors, OCR, document management systems, and AI-assisted typing/formatting tools are mature, widely deployed products used daily in schools and offices.

Grade homework and tests, and compute and record results, using answer sheets or electronic marking devices.

CI 76–76 · exposure 75 · augmentation 75 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Learning management systems and auto-grading tools are widely deployed in K–12 and higher ed, but adoption remains uneven across districts and grade levels. Objective grading automation is common in some sectors; subjective grading assistance is still pilot-phase in many organizations.
Sector adoption velocity	claude-sonnet-5	3/5	K-12 education adopts technology unevenly; scantron-style grading has been standard for decades, but AI-based short-answer/essay grading adoption is still in pilot phase in many districts.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI assists teachers substantially by auto-grading objective items and flagging patterns, freeing human time for feedback and intervention. Teachers remain in the loop for final review and subjective items, and productivity gains are significant for the portions where AI applies reliably.
Augmentation potential	claude-sonnet-5	4/5	AI and automated marking tools significantly speed up grading and record-keeping while assistants still handle exceptions, rubric application, and entering results into gradebooks.
Task automatability	claude-haiku-4-5-20251001	4/5	Current AI systems (OCR + scoring models) can reliably grade objective homework and tests (multiple choice, fill-in-the-blank) end-to-end with >50% time savings. However, subjective written responses and partial credit decisions still require human judgment, limiting full automation to perhaps 70–80% of typical grading workload.
Task automatability	claude-sonnet-5	4/5	Grading objective items (multiple choice, fill-in-blank, numeric answers) via answer sheets/scanners is highly automatable today, and AI can also draft-score short-answer/essay responses, though open-ended grading still needs human spot-checking.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few hard regulatory barriers prevent grading automation. Schools and districts control grading policy; human contact is not legally required for this specific task. Some organizational friction exists (teacher preference for familiarity, curriculum customization), but nothing legally blocks substitution.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement for grading tasks performed by assistants, though some schools prefer human review of student work for fairness/appeals and grade disputes, creating mild friction.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	AI inference for scanning and marking objective items costs pennies per assignment; human graders cost $15–30+ per hour. All-in cost per graded item is typically 1–2 orders of magnitude cheaper with AI, even accounting for integration and oversight.
Cost vs. human wage	claude-sonnet-5	5/5	Automated scanning/grading software costs pennies per assignment versus paying a teaching assistant hourly wages to hand-grade and record scores.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Mature products (Gradescope, learning management systems with auto-grading, AI-powered assessment tools) demonstrably perform objective grading at scale in production. Some platforms begin offering rubric-based subjective grading, though accuracy and consistency remain material concerns.
Technical feasibility today	claude-sonnet-5	4/5	Scantron/OMR systems and LMS auto-grading (Google Classroom, Canvas, etc.) are already deployed at scale for objective assessments; AI essay-scoring tools exist but are used more cautiously in K-12 settings.

Plan, prepare, and develop various teaching aids, such as bibliographies, charts, and graphs.

CI 56–77 · exposure 62 · augmentation 88 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Schools and districts are exploring AI for administrative and preparation tasks, but adoption remains uneven and often piloted rather than standardized. Most teaching assistants still prepare aids manually; AI-driven workflows are emerging but not yet widespread at scale.
Sector adoption velocity	claude-sonnet-5	2/5	K-12 education is a moderately slow-adopting sector for AI tools compared to finance or tech, with pilots and individual teacher use more common than systemic deployment.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI tools significantly augment teaching assistant productivity by rapidly generating bibliography suggestions, chart templates, and graph layouts that the assistant then refines and customizes. This keeps the human in the loop while transforming speed and iteration capacity.
Augmentation potential	claude-sonnet-5	5/5	AI tools substantially speed up creation of charts, graphs, and bibliographies, letting the human refine and tailor content rather than build it from scratch.
Task automatability	claude-haiku-4-5-20251001	3/5	AI can generate bibliographies, charts, and graphs automatically from structured data or prompts, and can draft visual teaching aids with significant time savings. However, human oversight is typically needed to ensure age-appropriateness, alignment with curriculum standards, and factual accuracy, limiting full end-to-end automation to roughly half the task.
Task automatability	claude-sonnet-5	4/5	Generating bibliographies, charts, and graphs from given content is a well-structured content-generation task that current generative AI and productivity tools handle well, saving substantial drafting time.5
Adoption barriers	claude-haiku-4-5-20251001	2/5	No licensing requirement or legal mandate exists for humans to create teaching aids; schools typically prefer cost reduction. Adoption is limited mainly by institutional inertia and teaching assistant familiarity with tools, not regulatory or liability constraints.
Adoption barriers	claude-sonnet-5	1/5	There is no licensing or regulatory requirement mandating a human create these particular teaching aids, so no hard barrier prevents AI assistance or automation.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI inference and integration costs for generating these teaching aids are very low (pennies per task), while a teaching assistant's loaded hourly wage is $20–30+. The cost advantage is substantial, though not quite an order of magnitude due to integration and verification overhead.
Cost vs. human wage	claude-sonnet-5	4/5	AI-assisted drafting of aids like charts and bibliographies costs a small fraction of the loaded wage of a teaching assistant per unit of output, though some oversight cost remains.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed AI tools (ChatGPT, Claude, Canva, graph-generation software) reliably produce bibliographies, charts, and graphs in production today. Most errors are minor formatting or data issues readily corrected by humans, making this consistently usable in real classroom preparation workflows.
Technical feasibility today	claude-sonnet-5	3/5	Tools like ChatGPT, Canva, and citation generators are deployed and used by educators today, but reliable, classroom-ready outputs still require human review and customization, so full reliability is not yet universal.

Laminate teaching materials to increase their durability under repeated use.

CI 15–89 · exposure 45 · augmentation 13 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Schools and educational institutions have adopted laminating machines and outsourced lamination services extensively for decades; automated lamination is standard practice in most K–12 settings, not experimental or pilot-stage.
Sector adoption velocity	claude-sonnet-5	1/5	Educational support/physical prep tasks in schools show minimal AI adoption, as this is a low-digitization physical task.
Augmentation potential	claude-haiku-4-5-20251001	2/5	Lamination itself offers minimal opportunity for AI-assisted augmentation; the task is purely mechanical with no judgment or creativity component. A human using an automated laminator is faster, but AI does not meaningfully enhance the human's capability on this task.
Augmentation potential	claude-sonnet-5	1/5	AI offers no meaningful assistance for the physical act of laminating materials, though it could help decide what to laminate.
Task automatability	claude-haiku-4-5-20251001	5/5	Laminating teaching materials is a straightforward, repetitive mechanical task with minimal decision-making: feed materials into a laminator, set temperature/speed, and trim edges. Current laminating machines with basic automation and simple setup can perform this task end-to-end with significant time savings compared to manual lamination.
Task automatability	claude-sonnet-5	1/5	Laminating is a physical manual task requiring operating a laminating machine, feeding materials, and trimming; no current AI system can perform this physical manipulation.'
Adoption barriers	claude-haiku-4-5-20251001	2/5	There are minimal regulatory or legal barriers to automating lamination; no license is required to operate a laminator, and schools and districts widely use automated systems already. The main barrier is organizational inertia and the low cost of existing TA labor, not regulatory prohibition.
Adoption barriers	claude-sonnet-5	1/5	No licensing, liability, or regulatory barriers exist for laminating materials; it's a low-stakes clerical/physical task.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	Automated laminating equipment has low per-unit processing costs once amortized, and commercial lamination services charge modest fees per page. The cost is substantially lower than paying a teaching assistant's hourly wage to manually feed and trim materials for hours.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI-based alternative to a human or simple machine operation, so AI is not cheaper since it isn't a viable substitute at all.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Automated laminating systems and lamination services are widely deployed in schools and commercial printing facilities today, reliably processing large batches of materials. While some edge cases (unusual sizes, delicate materials) may require human oversight, standard teaching materials lamination is routine and mature in production.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product performs physical lamination; this requires robotic hardware that is not commercially deployed for this purpose.

Use computers, audio-visual aids, and other equipment and materials to supplement presentations.

CI 29–51 · exposure 38 · augmentation 63 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	K–12 schools digitize slowly and conservatively; while most schools have audiovisual capability, meaningful automation of instructional material sequencing and deployment remains rare, with adoption concentrated in higher-resourced districts and primarily in recording/asynchronous contexts rather than live classroom augmentation.
Sector adoption velocity	claude-sonnet-5	2/5	K-12 education is a relatively slow-adopting sector for classroom-level AI tools, with budget constraints, teacher training gaps, and district-level rollout lagging behind digitized industries.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI tools can assist by recommending when and what materials to deploy based on learning objectives, organizing digital libraries, and auto-generating or curating supplementary content, but a teaching assistant's judgment about real-time classroom needs remains central to effective instruction.
Augmentation potential	claude-sonnet-5	4/5	AI tools (e.g., presentation generators, interactive content creators, translation aids) meaningfully speed up preparation and enrich supplementary materials while the human still delivers and operates them in class.
Task automatability	claude-haiku-4-5-20251001	2/5	Operating audiovisual equipment and displaying digital content can be partially automated (e.g., auto-playing slides, video playback), but this task requires real-time judgment about pacing, student engagement, and when to switch materials—decisions that depend on classroom context and live interaction that current AI cannot reliably perform end-to-end without significant human oversight.
Task automatability	claude-sonnet-5	3/5	AI can help generate slides, videos, and materials to supplement lessons, but the physical setup and live operation of equipment during class requires human presence and judgment.ate half the underlying content-creation work could be automated, not the full task.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Teaching assistants are typically under contracts that define their instructional support duties; classroom integration of automated media also faces organizational inertia (teacher preference for human support, curriculum constraints, need for human judgment during live instruction) and potential liability concerns if automation causes instructional gaps.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement specifically for AV setup, but school policies, safeguarding requirements around technology use with minors, and the need for a present adult create moderate friction.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	Once learning management systems and presentation tools are in place (sunk costs in schools), the marginal cost to auto-schedule or auto-play supplementary materials is very low compared to paying a teaching assistant labor; the infrastructure cost is borne by the institution, not per-task.
Cost vs. human wage	claude-sonnet-5	3/5	AI tools for generating supplementary materials are cheap, but the teaching assistant's in-classroom role (operating equipment, adapting live) is not replaced, so overall cost savings are moderate rather than dramatic.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While presentation software and media players exist in schools, automating the orchestration of when and how to deploy these tools to supplement live instruction requires contextual awareness of student attention and pedagogical needs that deployed systems do not reliably handle; current automation is limited to playback, not instructional integration.
Technical feasibility today	claude-sonnet-5	3/5	Products like presentation generators, AI slide tools, and multimedia authoring assistants are widely deployed, but actual classroom operation of equipment still relies on the human aide, limiting reliability of full automation.

Prepare lesson materials, bulletin board displays, exhibits, equipment, and demonstrations.

CI 25–52 · exposure 38 · augmentation 63 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Schools are slow to adopt automation; resource constraints mean many still rely on human aides for material prep. Digital adoption (templating, design tools) exists but most elementary and secondary schools have not systematized or scaled AI-driven material generation in production.
Sector adoption velocity	claude-sonnet-5	2/5	K-12 education is a moderate-to-slow adopter of AI tools, with usage growing for content creation but physical classroom tasks remain largely manual and under-digitized.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can meaningfully assist by generating lesson templates, suggesting bulletin board layouts, and drafting material lists, raising aide productivity on the design phase. However, the human still selects, assembles, and customizes materials for their classroom context and students.
Augmentation potential	claude-sonnet-5	4/5	AI significantly speeds up drafting lesson materials, generating ideas for displays, and creating visual content, meaningfully boosting productivity even though the human still assembles and installs materials.
Task automatability	claude-haiku-4-5-20251001	2/5	AI can draft lesson materials and generate text-based content, but creating physical bulletin boards, exhibits, and demonstrations requires spatial judgment, aesthetic decisions, and hands-on assembly that current systems cannot perform end-to-end. Material preparation remains substantially manual.
Task automatability	claude-sonnet-5	3/5	AI can generate lesson content, worksheets, and design ideas quickly, but physical assembly of bulletin boards, exhibits, and equipment setup still requires human hands-on work, capping full end-to-end automation.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Teacher aides work directly with children in educational settings where human oversight and judgment about age-appropriate, safe materials and demonstrations are valued and often required by schools. Organizational and child-safety norms create friction against full automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human for this task, though schools may prefer teaching assistants for the hands-on classroom presence and physical setup work.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Generating design templates costs relatively little, but the labor savings are modest because physical assembly, equipment setup, and hands-on demonstration construction still require human time. All-in AI cost is not substantially lower than the wage for the full task.
Cost vs. human wage	claude-sonnet-5	3/5	AI tools are cheap for content/material generation but the physical labor portion (printing, cutting, assembling displays, setting up equipment) still requires paid human time, keeping overall cost comparable.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	AI can generate printable templates and material designs (via text-to-image models), but no production system reliably handles the full pipeline of material creation, physical assembly, and demonstration setup. Most deployments are narrow—design only, not end-to-end preparation.
Technical feasibility today	claude-sonnet-5	3/5	Products like generative AI tools and design apps (Canva AI, ChatGPT) are used in classrooms today to draft materials and visuals, but they don't handle physical display construction or equipment prep reliably.

Requisition and stock teaching materials and supplies.

CI 28–40 · exposure 25 · augmentation 50 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	School districts and educational institutions lag in adopting advanced automation; procurement and supply chain automation in schools is still limited, with most districts relying on manual or basic digital ordering systems rather than AI-driven agents.
Sector adoption velocity	claude-sonnet-5	1/5	K-12 schools, especially for support staff logistics, are slow technology adopters with limited budgets for automation of routine supply tasks.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI could usefully assist by suggesting needed supplies based on curriculum or past usage patterns, tracking inventory levels, or automating routine reorder notifications, helping a teaching assistant work more efficiently without displacing their role.
Augmentation potential	claude-sonnet-5	3/5	Digital inventory and ordering tools can help track stock levels and generate reorder lists, giving moderate assistance to the person managing supplies.
Task automatability	claude-haiku-4-5-20251001	2/5	While ordering systems and inventory tracking can be automated, this task involves discretionary decisions about what materials are needed, physical stocking, and local knowledge of classroom requirements that resist full end-to-end automation today. AI could assist with ordering workflows but cannot reliably replace the judgment and manual execution required.
Task automatability	claude-sonnet-5	2/5	Ordering and inventory tracking could be partly automated via software, but the physical stocking of shelves and materials, plus judgment about what's needed for classroom activities, still requires a human on-site.
Adoption barriers	claude-haiku-4-5-20251001	3/5	School districts typically have purchasing policies, budget controls, and vendor relationships that create moderate friction for automation, though there are no hard legal barriers requiring a human to perform requisitioning itself. Some organizational inertia around procurement processes exists.
Adoption barriers	claude-sonnet-5	1/5	No licensing or regulatory requirement restricts who can order or stock supplies; it's a low-stakes administrative task.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Integrating procurement automation and inventory systems involves significant setup and ongoing oversight costs; for routine school supply ordering, the all-in AI cost (integration, maintenance, error correction) approaches or exceeds the modest wage of a teaching assistant performing this task.
Cost vs. human wage	claude-sonnet-5	2/5	AI-driven inventory management tools have some cost, and physical stocking still requires paid labor, so overall savings versus a low-wage teaching assistant doing this task are modest.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Some e-procurement and inventory management systems exist, but they typically require substantial human input on requisition decisions, vendor selection, and physical inventory work. No mature product today fully automates the end-to-end process of determining needs, requisitioning, and stocking materials in a school context without manual intervention.
Technical feasibility today	claude-sonnet-5	2/5	Inventory/procurement software exists broadly but schools rarely deploy AI-driven requisition systems for classroom supplies specifically; most use basic spreadsheets or manual processes.

Maintain computers in classrooms and laboratories, and assist students with hardware and software use.

CI 30–35 · exposure 25 · augmentation 50 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	K–12 schools are generally slow adopters of AI-driven automation, with limited IT budgets, high risk aversion, and strong preference for human presence in student support roles. Pilots exist but production replacement of teaching assistants for tech support is rare in public school systems.
Sector adoption velocity	claude-sonnet-5	2/5	K-12 education is a slower-adopting sector for AI-driven IT support, with most schools still relying on human aides or district IT staff rather than AI systems.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can usefully assist teaching assistants by providing automated troubleshooting scripts, step-by-step guidance for common issues, and documentation—allowing them to handle more student requests faster. However, the assistance is confined to technical problem-solving; it does not address the mentoring and interpersonal dimensions of the role.
Augmentation potential	claude-sonnet-5	3/5	AI chatbots and troubleshooting guides can help TAs quickly diagnose common software issues, improving efficiency, though physical hardware fixes and student engagement still need human effort.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can assist with some technical troubleshooting and software guidance through chatbots or help systems, the task requires hands-on hardware maintenance, physical presence in classrooms/labs, and real-time interaction with diverse student needs and skill levels. Current AI cannot perform the physical and contextual elements that dominate this task.
Task automatability	claude-sonnet-5	2/5	Basic troubleshooting steps can be guided by AI, but hands-on hardware maintenance and in-person student assistance require physical presence and real-time interaction that current AI cannot fully replace.in school settings.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Schools have moderate adoption friction: IT policies, liability concerns for device access, preference for trained staff on-site, and organizational inertia in procurement. However, no hard legal requirement mandates a human for software/hardware assistance, so barriers are organizational and practical rather than regulatory.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but schools often prefer having a trusted adult present for student supervision and liability reasons, creating moderate organizational friction.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI support tools (chatbots, help systems) can reduce some overhead, but teaching assistants provide continuous in-person support that includes mentoring, troubleshooting, and classroom presence. The all-in cost of AI infrastructure plus human oversight remains comparable to or higher than a teaching assistant's direct labor in most school settings.
Cost vs. human wage	claude-sonnet-5	2/5	While software diagnostic tools are cheap, physical hardware maintenance still requires a human on-site, so overall cost savings versus a teaching assistant's wage are limited.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Some technical support chatbots and remote troubleshooting tools exist in deployed form, but they handle only narrow, scripted scenarios. They cannot reliably diagnose hardware failures, perform repairs, or adapt guidance to students with varied technical literacy and classroom contexts. Reliable end-to-end performance is not demonstrated in production.
Technical feasibility today	claude-sonnet-5	2/5	IT helpdesk chatbots and AI troubleshooting guides exist but are not deployed specifically for classroom hardware/software support at scale in K-12 settings.

Distribute tests and homework assignments and collect them when they are completed.

CI 25–35 · exposure 25 · augmentation 50 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	While some schools use LMS platforms, many elementary and middle school classrooms still rely on traditional paper-based workflows; adoption of full automation remains limited outside digitally advanced districts.
Sector adoption velocity	claude-sonnet-5	2/5	K-12 education adopts technology unevenly and slowly, especially in lower grades where physical handling of materials remains common; digital tools have penetrated some classrooms but adoption is uneven and often supplementary rather than transformative.
Augmentation potential	claude-haiku-4-5-20251001	3/5	Learning management systems and digital assignment tracking can meaningfully assist teaching assistants by automating digital distribution and collecting timestamps, reducing manual paperwork while the human still oversees classroom verification and troubleshooting.
Augmentation potential	claude-sonnet-5	3/5	Digital classroom platforms can meaningfully streamline the logistics of assigning and collecting homework/tests for teachers and aides in schools that have adopted them, though this doesn't apply uniformly across all age groups or settings.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI could help organize or track distribution of digital assignments, the physical act of distributing and collecting paper materials, plus verifying completion in a classroom setting, requires human presence and interaction that current systems cannot fully automate.
Task automatability	claude-sonnet-5	2/5	This is a physical distribution/collection task involving handing out and gathering paper or classroom materials; AI has no direct mechanism to physically distribute or collect items in a classroom, though digital assignment platforms can replace parts of it in some settings.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Schools operate under institutional workflows and often require human staff to verify student completion and identity; there are also organizational inertia and potential liability concerns around automated assignment collection without human oversight.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human for this specific subtask, but classroom supervision norms and the need for a physical presence in preschool/elementary settings create mild organizational friction.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	A teaching assistant's time on this routine task is relatively low-cost labor; deploying an AI system to partially automate it (digital tracking, LMS integration) may not achieve cost parity when considering implementation and oversight overhead.
Cost vs. human wage	claude-sonnet-5	2/5	Where digital tools apply, software costs are low, but a human (teaching assistant) is still needed for physical classroom tasks, mixed-age supervision, and paper-based work, so overall cost savings are limited.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Some learning management systems can distribute and collect digital assignments automatically, but most classroom contexts still involve physical materials and face-to-face verification, where no single deployed product reliably handles the full end-to-end task across diverse settings.
Technical feasibility today	claude-sonnet-5	2/5	Learning management systems (Google Classroom, Canvas) already handle digital assignment distribution/collection reliably, but many preschool-secondary classrooms still use physical materials requiring human handling, especially with younger students.

Observe students' performance, and record relevant data to assess progress.

CI 25–30 · exposure 25 · augmentation 63 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Education sectors are slow adopters of automation; most schools rely on human observation and manual records, with limited production deployment of automated student assessment tools beyond basic attendance tracking.
Sector adoption velocity	claude-sonnet-5	2/5	K-12 education is a comparatively slow-adopting sector for AI due to budget constraints, privacy concerns, and reliance on in-person supervision, with pilots more common than production-scale deployment.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI-powered dashboards, automated flagging of concerning trends, and data visualization can significantly assist TAs in organizing observations and spotting patterns, allowing them to focus on deeper reflection and timely intervention.
Augmentation potential	claude-sonnet-5	3/5	AI tools can help aggregate, analyze, and flag patterns in performance data (e.g., dashboards, quiz analytics) that assist the teaching assistant in tracking progress more efficiently, even though direct observation remains manual.
Task automatability	claude-haiku-4-5-20251001	2/5	AI can record structured behavioral/performance data from video or text logs, but assessing progress requires contextual judgment about individual student development trajectories, which demands human expertise. Current systems can flag patterns but cannot replace the holistic observation and interpretation teachers perform.
Task automatability	claude-sonnet-5	2/5	The physical act of observing children in real time requires human presence and judgment about behavior, engagement, and social-emotional cues that current AI cannot reliably capture in unstructured classroom settings; only the data recording/summarizing portion is automatable.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Significant barriers exist: FERPA and state privacy laws restrict automated collection and storage of student performance data, parental consent requirements, and strong institutional preference for human judgment in educational assessment and record-keeping.
Adoption barriers	claude-sonnet-5	3/5	No strict licensing barrier for this specific task, but school policies, safeguarding requirements, and the need for human judgment in interpreting child behavior create meaningful organizational friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Video monitoring systems and learning analytics platforms are moderately costly and require integration and oversight; combined with the need for teacher validation, the total cost is comparable to or exceeds a TA's time on this task.
Cost vs. human wage	claude-sonnet-5	2/5	While automated data logging tools are cheap, the human observation component still requires an aide physically present, so overall cost savings versus employing the teaching assistant are limited.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Products exist for classroom analytics and attendance tracking, but reliable automated student progress assessment at the individual level remains nascent; most deployed tools require significant human review and correction to be trustworthy.
Technical feasibility today	claude-sonnet-5	2/5	Some ed-tech products (adaptive learning platforms, attendance/behavior trackers) log quantitative performance data, but no deployed product reliably observes and interprets qualitative student behavior/progress in a live classroom at scale.

Tutor and assist children individually or in small groups to help them master assignments and to reinforce learning concepts presented by teachers.

CI 25–25 · exposure 25 · augmentation 50 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	K–12 education remains a laggard sector in AI adoption, with most classroom AI confined to administrative or supplementary use; widespread replacement of human tutors in schools is minimal. Pilot programs exist but production displacement is negligible compared to tech/finance sectors.
Sector adoption velocity	claude-sonnet-5	2/5	K-12 education is a famously slow-adopting sector for classroom-facing AI, with pilots for supplemental tools but minimal deployment replacing human aides.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI-powered tutoring tools can usefully assist teachers and aides by generating practice problems, explaining concepts via text/video, or flagging at-risk learners via analytics—moderately boosting human productivity—but the human educator's judgment, emotional attunement, and real-time adaptation remain essential and are not transformed by current AI.
Augmentation potential	claude-sonnet-5	3/5	Adaptive learning software and AI-generated practice materials can help TAs personalize reinforcement activities and track progress, meaningfully aiding but not transforming the core interpersonal tutoring task.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI tutoring systems can deliver content explanation and answer factual questions, this task critically requires real-time assessment of individual learning gaps, adaptive pacing, and responsive relationship-building with children—capabilities that current AI systems cannot reliably deliver end-to-end at 50% time savings with equal quality. The personalized reinforcement and emotional scaffolding remain beyond deployed automation.
Task automatability	claude-sonnet-5	2/5	AI tutoring tools can handle some drill-and-practice and explanation tasks, but the task requires in-person relationship building, behavior management, and physical presence with young children that current AI cannot replicate end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Schools face high organizational barriers: duty-of-care liability for child safety and learning outcomes, parental expectations for human judgment, teacher-union resistance, and state/district policies requiring human adult-child ratios. Schools are also slow to adopt novel automation and face reputational risk from over-reliance on AI for vulnerable populations.
Adoption barriers	claude-sonnet-5	4/5	Schools have strong child-safety, supervision, and duty-of-care requirements that mandate adult presence, plus parental and institutional preference for human interaction with young children.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Current AI tutoring platforms (subscriptions, LLM APIs, oversight labor) typically cost $5–20 per hour of instruction, approaching but not undercutting the all-in cost of a teaching assistant ($15–25/hr loaded). At-scale deployment would need to improve cost-effectiveness further to beat human substitution.
Cost vs. human wage	claude-sonnet-5	2/5	Software subscriptions are cheap per-student, but they cannot replace the supervisory and interpersonal role of the teaching assistant, so the true cost comparison requires retaining human staff alongside any tool.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Chatbot tutoring and adaptive learning platforms exist but operate at scale only in narrow domains (e.g., standardized test prep) and show material limitations in tracking deeper conceptual mastery across diverse learners. No production system reliably replaces small-group human tutoring with comparable child outcomes or confidence.
Technical feasibility today	claude-sonnet-5	2/5	AI tutoring products (e.g., adaptive learning apps) exist and are used in classrooms for supplemental practice, but no deployed product independently tutors and manages small groups of children reliably without an adult present.

Operate and maintain audio-visual equipment.

CI 14–35 · exposure 20 · augmentation 38 · importance 3.7/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	K–12 schools are slow adopters of automation, with low digitization in many districts, limited budgets, and conservative IT practices. Most deployments remain pilot-stage; schools typically hire teaching assistants or IT staff rather than invest in autonomous AV systems.
Sector adoption velocity	claude-sonnet-5	1/5	Educational support roles involving physical equipment maintenance show minimal AI adoption; this is a low-digitization, hands-on task.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI-assisted tools (e.g., voice commands, automated troubleshooting prompts, remote monitoring dashboards) can streamline routine equipment checks and scheduling, moderately improving a teaching assistant's efficiency without replacing their role in ad-hoc problem-solving and classroom support.
Augmentation potential	claude-sonnet-5	2/5	AI chatbots or manuals could help troubleshoot error codes or provide setup instructions, but this offers only marginal assistance to the physical task.
Task automatability	claude-haiku-4-5-20251001	2/5	Audio-visual equipment operation (e.g., projectors, speakers, video playback) can be partially automated through scheduling and remote control, but real-world classroom troubleshooting, equipment failure diagnosis, and physical setup/teardown remain largely manual. Current AI cannot reliably handle the varied environmental conditions and ad-hoc problems in school settings.
Task automatability	claude-sonnet-5	2/5	Physical setup, troubleshooting, and maintenance of AV equipment require hands-on manipulation that current AI cannot perform; only minor software-configuration aspects could be assisted remotely.'
Adoption barriers	claude-haiku-4-5-20251001	4/5	Schools face institutional barriers: IT governance requirements, liability concerns over unsupervised equipment, need for human oversight during live instruction, and organizational resistance to replacing on-site technical staff. In-person presence and quick response to failures are often valued by teachers.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but physical presence and hands-on troubleshooting create practical barriers to remote automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	The all-in cost of deploying automated AV systems (hardware, cloud integration, IT oversight, fallback support) typically exceeds the part-time labor cost of a teaching assistant performing these tasks, especially in budget-constrained schools.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI substitute performing this physical task, so cost comparison favors the human by default.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Some automation exists for basic AV playback scheduling and remote device management, but deployed school-grade systems rarely achieve end-to-end automation of operation and maintenance. Most schools still rely on manual setup, IT support calls, and teacher intervention for common failures.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously operates or physically maintains classroom AV hardware; this remains a manual, in-person task.

Present subject matter to students under the direction and guidance of teachers, using lectures, discussions, supervised role-playing methods, or by reading aloud.

CI 5–25 · exposure 13 · augmentation 50 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	K–12 education is a slow-adopting sector with fragmented IT, budget constraints, and resistance to replacing human contact in classroom instruction; pilot projects exist but production replacement of teaching assistants remains extremely rare.
Sector adoption velocity	claude-sonnet-5	2/5	K-12 education is a moderately slow-adopting sector for classroom-facing AI, with pilots for content generation but little displacement of in-person instructional roles.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI tools can assist teachers and assistants by generating lesson materials, providing read-aloud support, or suggesting discussion prompts, but the core task of presenting and engaging students in real-time remains inherently human-centered.
Augmentation potential	claude-sonnet-5	3/5	AI can help generate lesson materials, discussion prompts, or role-play scenarios that the teaching assistant then delivers, offering moderate prep-time assistance.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can generate lecture content and read text aloud, the task requires real-time student engagement, classroom management, and responsiveness to diverse learning needs under teacher direction—capabilities current AI cannot reliably handle end-to-end with 50% time savings at equal quality.
Task automatability	claude-sonnet-5	1/5	This requires in-person physical presence, classroom management, supervised interaction with children, and real-time adaptive facilitation that current AI cannot perform end-to-end in a physical classroom setting.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Schools have strong regulatory, liability, and duty-of-care requirements; teaching assistants must be present for student safety and behavioral supervision, and district policies typically require human staff for direct instruction and role-modeling.
Adoption barriers	claude-sonnet-5	4/5	Schools require adult supervision of minors, safeguarding regulations, and direct human presence in classrooms, creating strong institutional and legal barriers to full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Integration costs (classroom systems, monitoring, teacher oversight) plus the need for human-quality interaction make AI deployment comparable to or more expensive than deploying additional teaching assistants, especially in regulated educational settings.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute performing this physical, supervisory task, so cost comparison favors the human by default since AI cannot deliver the output at all.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed product reliably performs this task in live classroom settings; text-to-speech and content generation exist, but they cannot adapt to student reactions, manage group dynamics, or operate under authentic teacher supervision at scale.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product substitutes for an in-person adult presenting material and supervising role-play with children; AI tutoring tools exist but do not replace this physically embodied classroom task.

Organize and label materials and display students' work in a manner appropriate for their eye levels and perceptual skills.

CI 15–15 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Education remains a low-automation sector for classroom-level physical tasks; schools have minimal incentive and infrastructure to deploy robotic systems for material organization and display tasks.
Sector adoption velocity	claude-sonnet-5	1/5	Preschool/elementary classroom environments are low-digitization, physical-labor settings where AI adoption for physical tasks is minimal to nonexistent.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could offer limited assistance through design suggestions for age-appropriate displays or inventory management of materials, but the core physical and spatial-judgment work remains human-dependent.
Augmentation potential	claude-sonnet-5	2/5	AI could help generate label text, suggest layout ideas, or create printable materials, but it cannot assist with the actual physical arrangement and mounting of displays.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires hands-on physical manipulation of materials, spatial judgment tailored to children's specific eye levels, and aesthetic decisions about age-appropriate display—capabilities current AI systems cannot perform end-to-end in physical classrooms.
Task automatability	claude-sonnet-5	1/5	This is a hands-on physical task requiring arranging, cutting, posting, and adjusting real classroom materials and student artwork to fit specific eye levels and developmental perceptual needs, which current AI cannot physically execute.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While there are no strict legal barriers to automation, school organizational culture, teacher discretion over classroom setup, and the need for human judgment about developmental appropriateness create moderate friction to substitution.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barrier exists, but the task inherently requires physical presence and dexterity in a classroom, plus judgment about child development that limits remote or software-based substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The task involves physical labor (organizing, labeling, arranging items) where the cost of robotic systems, integration, and oversight would far exceed the wage of a teaching assistant performing the work.
Cost vs. human wage	claude-sonnet-5	1/5	AI cannot perform the physical placement and labeling required, so there is no viable AI cost comparison; a human aide remains necessary and cheaper than any hypothetical robotic solution.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI product can physically organize, label, and arrange classroom materials or assess and arrange student work displays autonomously in a real classroom environment.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs physical classroom organization and display of children's work; this remains purely a physical, in-person task.

Clean classrooms.

CI 5–24 · exposure 8 · augmentation 13 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Schools are low-digitization, budget-constrained organizations with strong preference for human staff presence. Adoption of autonomous cleaning robots in schools is minimal and remains largely experimental rather than mainstream.
Sector adoption velocity	claude-sonnet-5	1/5	Education support/janitorial functions in schools show minimal AI or robotic adoption; this is a low-digitization, physical-labor context with no meaningful automation trend.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI-powered cleaning tools (e.g., scheduling, supply tracking, quality monitoring) could offer modest assistance, but the core physical task of cleaning offers limited augmentation since teaching assistants primarily execute manual labor rather than making complex decisions.
Augmentation potential	claude-sonnet-5	1/5	AI offers no meaningful assistance for physically cleaning a classroom; there is no software or planning role that materially speeds up this manual task.
Task automatability	claude-haiku-4-5-20251001	2/5	Cleaning classrooms involves physical tasks like sweeping, wiping, and organizing in unstructured environments with many obstacles, fragile items, and variable layouts. Current AI robotic systems lack the dexterity, adaptability, and cost-effectiveness to handle this reliably at scale, and human oversight would still be required for quality assurance and safety around children.
Task automatability	claude-sonnet-5	1/5	Cleaning classrooms is a physical manual task requiring mobility, dexterity, and object manipulation that current AI systems (software-based) cannot perform; robotic cleaning solutions for general classroom tidying are not viable substitutes for this varied task.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Schools must ensure child safety, hygiene standards, and liability compliance, creating strong organizational and regulatory friction around automation. Human presence is preferred for quality control, handling hazardous materials, and maintaining the care-oriented mission of schools.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human specifically for cleaning, but practical barriers exist around safety, liability for damage/injury, and the need for judgment in handling children's belongings and hazards.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized cleaning robots capable of classroom-level work are prohibitively expensive relative to hiring teaching assistants or custodial staff, especially when factoring in maintenance, integration, and the need for human oversight in sensitive school environments.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic system that performs this full task, so any hypothetical solution (specialized robots plus human oversight) would cost far more than simply having staff clean the room.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed products reliably perform full classroom cleaning autonomously in educational settings today. While industrial cleaning robots exist, they are not production-deployed in school environments and require structured spaces and high capital investment.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs general classroom cleaning (organizing materials, wiping surfaces, tidying varied objects); commercial floor-cleaning robots address only a narrow subset and are not used for this purpose in schools.

Distribute teaching materials, such as textbooks, workbooks, papers, and pencils, to students.

CI 5–24 · exposure 8 · augmentation 13 · importance 3.7/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Educational institutions, especially K–12, have low digitization and slow adoption of novel technologies; material distribution remains a human-touch task with minimal financial pressure to automate given its low labor cost.
Sector adoption velocity	claude-sonnet-5	1/5	K-12 education support roles show minimal AI adoption for physical tasks; classroom aide work is low-digitization and slow to change.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist by generating optimized distribution lists or inventory tracking, but the core task of physically handing materials to students offers minimal productivity gain from AI assistance.
Augmentation potential	claude-sonnet-5	1/5	AI offers no meaningful assistance for physically handing out books, papers, or pencils to students.
Task automatability	claude-haiku-4-5-20251001	2/5	While material distribution is straightforward logistically, classroom context requires understanding student count, appropriate quantities per task, and physical accessibility—automating this end-to-end would need robotic systems and real-time coordination that current AI alone cannot manage without substantial hardware investment and setup.
Task automatability	claude-sonnet-5	1/5	This is a physical, in-person task of handing out materials in a classroom; no AI system can perform this physical distribution.PosixPath.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Schools have strong institutional preferences for human contact with students, safety/liability concerns around unsupervised automated systems in classrooms, and no legal mandate to automate this inherently low-skill task that builds staff-student relationships.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human specifically, but the physical nature of the task and classroom presence create a practical barrier to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The cost of implementing robotics or autonomous systems to distribute materials would far exceed the minimal wage cost of a teaching assistant or volunteer performing this task manually.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI substitute for this physical action, so AI cost is not applicable/is effectively infinite relative to a human doing it.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI system reliably performs physical material distribution in classrooms today; this requires robotic manipulation, navigation, and spatial reasoning that are research-stage outside controlled lab settings.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product physically distributes materials in classrooms; this remains entirely manual work.

Conduct demonstrations to teach skills, such as sports, dancing, and handicrafts.

CI 5–23 · exposure 13 · augmentation 38 · importance 3.8/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Educational institutions remain heavily traditional in their staffing models, with preschool and K–12 sectors showing low adoption of autonomous teaching systems. Budget constraints and conservative governance make rapid replacement of TA-led demonstrations unlikely.
Sector adoption velocity	claude-sonnet-5	1/5	Preschool and K-12 in-person instructional aide roles are among the least digitized and slowest to adopt AI due to physical, hands-on requirements and child safety norms.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can assist by generating video tutorials, providing step-by-step breakdowns of movements, or suggesting corrections for common mistakes observed in student attempts—useful scaffolding that enhances a TA's lesson preparation and feedback, though the TA remains essential for live demonstration.
Augmentation potential	claude-sonnet-5	2/5	AI could help plan lesson content or find instructional videos beforehand, but offers little real-time assistance during the physical demonstration itself.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can generate instructional videos and demonstrate techniques via video playback, conducting live demonstrations requires real-time physical presence, modeling, and adaptive response to student attempts—tasks where current AI falls significantly short of the 50% time-saving threshold. The embodied, interactive nature of teaching physical skills limits automation substantially.
Task automatability	claude-sonnet-5	1/5	This requires physical demonstration of motor skills to children in person, which no current AI system can perform end-to-end; it is inherently embodied and interpersonal.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Schools require in-person adult supervision for safety reasons, and parents expect human instructors for skill-building activities involving children. Regulatory expectations around child safety and school staffing practices create meaningful friction against full automation, even where technically conceivable.
Adoption barriers	claude-sonnet-5	4/5	Working with children requires supervision, safety oversight, and human presence; schools have strong organizational and safeguarding requirements for direct instruction involving minors.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	The cost of deploying robotic systems capable of live physical demonstration (hardware, maintenance, safety compliance) far exceeds the loaded wage of a teaching assistant, making AI substitution economically infeasible for this specific task.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute for physical, in-person skill demonstration, so cost comparison favors the human by default.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed product reliably conducts live in-person skill demonstrations for groups of students. Video tutorials and AI-generated instructional content exist, but they cannot replace the real-time observation, correction, and physical modeling that human TAs currently provide in classroom settings.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product enables an AI to physically demonstrate sports, dance, or handicrafts skills to students in a classroom setting.

Teach social skills to students.

CI 0–10 · exposure 5 · augmentation 38 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Education sectors, particularly K–12 public schools, remain highly resistant to AI-driven replacement of instructional staff due to regulatory, union, and parental concerns. Adoption of AI in teaching assistants' social-skills work remains negligible in production.
Sector adoption velocity	claude-sonnet-5	1/5	K-12 education, especially direct student interaction roles like teaching assistants, is a low-digitization sector with minimal AI deployment for interpersonal skill-building.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can assist by drafting lesson plans, suggesting behavioral intervention strategies, or providing reference materials on social-skill development, improving preparation efficiency. However, in-classroom delivery and student interaction remain human-dependent, limiting the breadth of augmentation.
Augmentation potential	claude-sonnet-5	2/5	AI can provide scripted social stories, role-play scenarios, or behavioral tracking tools that a TA might use as supplementary resources, but the core teaching interaction remains human-led.
Task automatability	claude-haiku-4-5-20251001	1/5	Teaching social skills fundamentally requires real-time interaction, adaptive responses to individual student behavior, and relationship-building that current AI cannot perform end-to-end. While AI might assist with content delivery, the core task—modeling, observing, correcting, and reinforcing social behaviors in a live classroom context—requires human judgment, emotional intelligence, and in-person presence.
Task automatability	claude-sonnet-5	1/5	Teaching social skills to children requires real-time human interaction, modeling, emotional presence, and relationship-building that current AI cannot replicate in a classroom setting.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Schools are legally and organizationally required to provide in-person instruction and supervision; parents and policy expect human educators to manage student social development. Liability, duty-of-care requirements, and the legal standing of teachers as custodians create hard barriers to automation.
Adoption barriers	claude-sonnet-5	4/5	Working with children on social-emotional development involves safeguarding concerns, school policy, parental trust, and often requires supervised, credentialed staff presence.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Current AI tools for social-skill content generation remain expensive relative to the marginal cost of a teaching assistant's time, especially when factoring in the need for human oversight, customization to individual students, and correction of AI limitations.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute performing this task, so cost comparison favors the human entirely; any AI attempt would require extensive human oversight negating savings.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed AI system reliably teaches social skills to students in production classroom settings. Existing tools can generate lesson content or provide supplementary materials, but none autonomously or reliably handle the interpersonal and behavioral dimensions central to this task.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously teaches social skills to students in classrooms; this remains firmly a human interpersonal function.

Discuss assigned duties with classroom teachers to coordinate instructional efforts.

CI 0–10 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Education, especially at preschool and K–12 levels, shows slow and lagging AI adoption; the sector remains primarily human-centric with low digitization of interpersonal coordination workflows.
Sector adoption velocity	claude-sonnet-5	1/5	K-12 education support staff roles show minimal AI agent adoption for interpersonal coordination tasks; this sector lags in deploying AI for face-to-face staff communication.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could marginally assist by summarizing prior lesson plans or suggesting coordination talking points, but the core task of live discussion and agreement-making remains inherently human, limiting meaningful augmentation.
Augmentation potential	claude-sonnet-5	2/5	AI tools like shared calendars, lesson-plan summarizers, or note-taking assistants could support preparation for such discussions, but they don't meaningfully transform the core interpersonal coordination act itself.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires real-time interpersonal negotiation, contextual understanding of classroom dynamics, and collaborative decision-making that current AI cannot perform autonomously. Even with task setup, AI cannot genuinely coordinate with teachers or adapt to their pedagogical preferences in a meaningful way.
Task automatability	claude-sonnet-5	1/5	This requires real-time, in-person interpersonal dialogue and relationship-building with a specific teacher about classroom-specific plans; no AI system performs this coordination task end-to-end.dotenv
Adoption barriers	claude-haiku-4-5-20251001	5/5	This task is deeply embedded in human organizational relationships and professional accountability; schools legally and operationally require a human TA present and able to discuss duties with a licensed teacher directly, creating a hard human-contact requirement.
Adoption barriers	claude-sonnet-5	3/5	No licensing barrier exists specifically, but strong organizational and interpersonal norms mean coordination discussions are expected to occur between the actual staff members present, creating natural friction against AI substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The cost of an AI system to mediate or simulate coordination conversations would exceed the minimal labor cost of a TA meeting briefly with a teacher, making automation economically pointless.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI service performing this discussion task, so no meaningful cost comparison to a human teaching assistant exists; the human is the only current option.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably performs teacher-TA coordination discussions; this requires nuanced human judgment, relationship-building, and accountability for instructional decisions that fall outside AI's current production capabilities.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product substitutes for the human-to-human conversation about duty coordination in a classroom setting; this remains entirely research-stage or nonexistent as a product function.

Collect money from students for school-related projects.

CI 0–10 · exposure 0 · augmentation 25 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Schools have shown minimal adoption of AI for student money collection; the task remains human-managed with manual ledgers or simple digital forms, with no production AI systems displacing this function.
Sector adoption velocity	claude-sonnet-5	1/5	K-12 school administrative support roles are low-digitization, low AI-adoption environments with minimal automation of physical cash-handling tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could marginally assist by automating simple record-keeping or sending payment reminders, but the core activity of receiving money and verification still requires a human, limiting practical augmentation value.
Augmentation potential	claude-sonnet-5	2/5	Digital payment apps or spreadsheet tools can help track and record collected funds, offering minor efficiency gains, but AI does not meaningfully assist the core physical collection task.
Task automatability	claude-haiku-4-5-20251001	1/5	Collecting money from students involves physical cash or payment handling, verification of amounts, and reconciliation—tasks that require human presence, handling of physical materials, and interaction with minors that cannot be automated by current AI systems.
Task automatability	claude-sonnet-5	1/5	This is a physical cash/check handling task requiring direct interaction with children and record-keeping; current AI systems cannot physically collect money or manage in-person transactions.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Hard barriers exist: handling student money involves fiduciary responsibility, legal accountability for funds, parental consent requirements, and the necessity for an authorized human staff member to be accountable for financial transactions involving minors.
Adoption barriers	claude-sonnet-5	3/5	While no license is strictly required, handling money from minors involves school policy, accountability, and trust considerations that create organizational friction against any automation attempt.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The task is simple and requires minimal setup or cost to perform manually by a teaching assistant; automating it would require payment infrastructure, verification systems, and oversight that would exceed the cost of manual collection.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI substitute performing this physical task, so cost comparison favors the human by default since AI cannot do the task at all.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No AI product deployed in schools today autonomously collects money from students; this task inherently requires human interaction, physical payment processing, and direct accountability with minors.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product performs physical collection of money from students; at most, digital payment platforms exist but do not replace this in-person task.

Organize and supervise games and other recreational activities to promote physical, mental, and social development.

CI 0–9 · exposure 8 · augmentation 38 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Schools operate under stringent child-safety regulations and cultural expectations that demand human supervisors. Adoption of AI automation for child supervision is negligible because it is legally and organizationally prohibited, not merely slow.
Sector adoption velocity	claude-sonnet-5	1/5	Preschool/school childcare and recreational supervision is a low-digitization, physically-embedded sector with essentially no AI displacement occurring.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI could moderately assist by suggesting age-appropriate games, activity plans, or developmental milestones to track, but the human teaching assistant remains central. Such tools would improve planning, but the live supervision and interpersonal work remain unchanged.
Augmentation potential	claude-sonnet-5	2/5	AI could help plan activity ideas or curricula in advance, but offers little real-time assistance during actual supervision of games and play.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI could theoretically suggest games and activities, the core requirement—real-time supervision of children, ensuring safety, and dynamically responding to their emotional and social needs—demands human judgment and presence. AI cannot reliably manage the unpredictable behavior and welfare oversight that defines this task.
Task automatability	claude-sonnet-5	1/5	This requires live physical presence, real-time supervision of children, safety management, and spontaneous social facilitation that current AI cannot perform end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Schools have strict legal and regulatory requirements that adults directly supervise and are responsible for children's safety during activities. Liability, duty-of-care standards, and mandatory human oversight create hard barriers to automation.
Adoption barriers	claude-sonnet-5	5/5	Child supervision requires background-checked, often certified adult presence for safety and legal liability reasons, creating a hard barrier to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	AI cannot replace the core function (live supervision), so any AI tool would merely assist at the margins (suggesting activities). The loaded cost of a teaching assistant—who must be physically present—far exceeds the marginal value of activity-generation AI.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute performing this physical supervisory task, so any comparison favors the human worker entirely.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product can autonomously supervise children's recreational activities or manage the safety and developmental aspects of group play. This task inherently requires human presence and real-time adaptation that current AI systems cannot achieve.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product supervises children's physical recreational activities; this remains firmly in the physical, in-person domain outside AI product capability.

Participate in teacher-parent conferences regarding students' progress or problems.

CI 0–5 · exposure 0 · augmentation 38 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	K-12 education remains a low-digitization, human-centric sector with strong institutional resistance to replacing in-person staff communication about student progress, especially in sensitive parent conferences.
Sector adoption velocity	claude-sonnet-5	1/5	K-12 education, especially support staff roles, shows slow AI adoption for direct human-to-human relational tasks like parent conferences.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could potentially assist with preparing summary notes or flagging data points before a conference, but the core task—listening, responding, and building rapport with parents—offers minimal opportunity for meaningful AI augmentation while the human remains in the loop.
Augmentation potential	claude-sonnet-5	3/5	AI can help prepare notes, summarize student progress data, or draft talking points beforehand, but it does not participate in or replace the live conference interaction itself.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires real-time interpersonal engagement, empathy, and adaptive responses to sensitive family dynamics and individual student contexts. Current AI cannot meaningfully participate in or conduct parent-teacher conferences that meet professional and relational standards.
Task automatability	claude-sonnet-5	1/5	This requires live, relational, in-person interaction with parents about a specific child's development, involving trust-building and sensitive judgment calls that AI cannot perform end-to-end.'
Adoption barriers	claude-haiku-4-5-20251001	5/5	School regulations, parental expectations, and educational policy require licensed or trained human staff to participate in parent-teacher conferences. Legal liability, duty of care, and the interpersonal trust required create hard barriers to substitution.
Adoption barriers	claude-sonnet-5	4/5	Schools require staff to represent the institution in parent communications, and there are strong norms and often policy/liability reasons requiring a human staff member's direct involvement, though not a strict licensing mandate.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The infrastructure, training, and human oversight required to deploy an AI system for parent-teacher conferences would far exceed the cost of a teaching assistant conducting the conference directly.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute performing this task, so no cost comparison favors AI; the human must be present.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably conducts or participates in parent-teacher conferences as a substitute for human staff. The task involves nuanced communication, trust-building, and situational judgment that remains beyond current AI capabilities in production settings.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product substitutes for a teaching assistant's presence and interpersonal engagement in parent conferences; this remains entirely a human role in practice.

Attend staff meetings and serve on committees, as required.

CI 0–5 · exposure 0 · augmentation 38 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Teaching assistants work in highly regulated, physically present-based environments with strong institutional norms around human participation in staff governance. Adoption of AI to replace attendance is virtually non-existent in schools.
Sector adoption velocity	claude-sonnet-5	1/5	K-12 education support staff roles show minimal AI adoption for governance/participation functions like committee service, reflecting slow digitization in this specific task type.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could modestly assist by preparing meeting agendas, summarizing prior decisions, or drafting committee notes, but the core task of attendance and active participation cannot be augmented—it must be performed by a human.
Augmentation potential	claude-sonnet-5	3/5	AI can help teaching assistants prepare notes, summarize prior meeting minutes, or draft talking points before meetings, offering moderate productivity support without touching the core attendance requirement.
Task automatability	claude-haiku-4-5-20251001	1/5	Attending staff meetings and serving on committees requires real-time presence, active participation in dynamic discussions, and often voting or consensus-building on decisions. Current AI cannot substitute for human attendance and participation in these organizational settings.
Task automatability	claude-sonnet-5	1/5	Attending meetings and serving on committees requires physical/virtual presence, real-time interpersonal interaction, and institutional representation that AI cannot substitute for today.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Schools legally and organizationally require actual staff members to attend meetings and participate in governance. Union contracts, school policies, and state education codes typically mandate human participation. Substituting AI for this role faces hard legal and contractual barriers.
Adoption barriers	claude-sonnet-5	4/5	Committee membership and staff meeting participation typically require formal employment status, institutional accountability, and human judgment/representation, creating strong organizational and role-based barriers.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Even if AI could hypothetically summarize or assist with meeting prep, the core task is human attendance and participation, which has near-zero marginal cost when the person is already employed. AI cannot replace the need for a human body and voice in these settings.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI substitute performing this role, so the comparison is moot—the human cost is the only viable cost for actual attendance and participation.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI system can reliably attend and participate in staff meetings or committee work as a substitute for a human presence. This requires organizational standing and accountability that only a human can provide.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product attends staff meetings or serves as a committee member in place of a human employee; this is not a product category that exists.

Supervise students in classrooms, halls, cafeterias, school yards, and gymnasiums, or on field trips.

CI 0–0 · exposure 0 · augmentation 25 · importance 4.6/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Schools remain low-digitization, human-contact-required settings. Adoption of even limited monitoring tools is slow, and parent/community resistance to AI-only supervision is high.
Sector adoption velocity	claude-sonnet-5	1/5	Education sector physical supervision roles show essentially no AI adoption or displacement; this is a low-digitization, high-physical-presence task category.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist via anomaly detection or incident flagging in controlled spaces (cafeteria cameras, playground monitoring), but meaningful augmentation is limited because the core task—active, responsive human presence—cannot be substantially enhanced by AI.
Augmentation potential	claude-sonnet-5	2/5	AI could support scheduling, monitoring via cameras for incident review, or communication logistics, but offers minimal direct assistance to the core act of physically supervising students in real time.
Task automatability	claude-haiku-4-5-20251001	1/5	Supervision requires real-time human presence, situational awareness, and immediate intervention in safety-critical situations. Current AI systems cannot operate as autonomous physical supervisors in dynamic school environments or field trips.
Task automatability	claude-sonnet-5	1/5	Physical supervision of children requires embodied presence, real-time safety judgment, and physical intervention capability that no current AI system possesses; this task is inherently non-automatable by software.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Strong legal and duty-of-care barriers exist: schools have explicit custodial responsibility; institutional liability for student harm falls on the organization; human supervision is legally mandated or expected in most jurisdictions.
Adoption barriers	claude-sonnet-5	5/5	Legal and regulatory requirements mandate adult human supervision ratios for child safety, liability concerns are severe, and physical presence is a hard, non-negotiable requirement.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Any AI infrastructure (cameras, monitoring systems, alerts) would layer atop, not replace, human supervision due to liability. The all-in cost would exceed the wage of a teaching assistant.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI substitute performing this physical supervisory task, so cost comparison is moot; a human must be present, making AI effectively infinitely more 'expensive' in the sense of being nonfunctional.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably performs student supervision today. Vision-based monitoring exists only in narrow, controlled settings and cannot replace human judgment, behavioral response, and duty of care.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product provides physical supervision of students in real-world settings like classrooms, halls, or field trips; this remains firmly outside current product capability.

Enforce administration policies and rules governing students.

CI 0–0 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	K–12 education adopts digital tools slowly and remains resistant to AI-driven discipline or student monitoring; adoption of AI in this context is negligible and faces significant institutional, legal, and parental opposition.
Sector adoption velocity	claude-sonnet-5	1/5	K-12 school environments are slow to adopt AI for direct student supervision and enforcement due to safety, legal, and ethical constraints.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist minimally by logging violations or flagging patterns, but the task's core—making judgment calls on enforcement—resists augmentation because the human must remain fully in control of each decision for liability and ethical reasons.
Augmentation potential	claude-sonnet-5	2/5	AI could help track infractions, generate reports, or provide reminders of policy details, but it offers minimal assistance in the live act of enforcing rules with students.
Task automatability	claude-haiku-4-5-20251001	1/5	Enforcing policies and rules governing students requires real-time judgment about context, intent, proportionality, and individual circumstances—all involving human discretion and relationship dynamics. Current AI cannot reliably perform this task end-to-end because it demands immediate situational awareness, behavioral interpretation, and social calibration that AI systems lack.
Task automatability	claude-sonnet-5	1/5	This requires physical presence, real-time behavioral judgment, and interpersonal authority with children that AI cannot perform end-to-end today.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Strong legal and organizational barriers exist: school districts employ teaching assistants to act as responsible adults in loco parentis, with accountability for student safety and welfare that cannot be legally delegated to an AI system. Licensing, liability, and mandatory human presence in disciplinary decisions create hard barriers.
Adoption barriers	claude-sonnet-5	5/5	Supervising and disciplining minors involves legal responsibility, safeguarding duties, and institutional policy requiring an authorized adult present, making substitution essentially barred.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	AI systems capable of continuous classroom monitoring, behavioral inference, and rule-application would require expensive computer vision, sensor infrastructure, and continuous human oversight—likely exceeding the cost of an actual teaching assistant's salary.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute performing this task, so any 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 enforces school policies as a primary actor today; any automation is narrow (e.g., flagging attendance) rather than handling the full task of rule enforcement. The core work—observing violations, deciding whether to intervene, selecting proportionate responses—remains human-dependent in production.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product enforces school rules and disciplinary policies with students in a classroom; this remains entirely a human, in-person responsibility.

Instruct and monitor students in the use and care of equipment and materials to prevent injuries and damage.

CI 0–0 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Educational institutions are slow to adopt automation for core safety functions, and no sector pattern shows meaningful AI displacement of safety monitoring roles in schools.
Sector adoption velocity	claude-sonnet-5	1/5	K-12 in-person supervisory roles show minimal AI adoption; this is a physical, low-digitization task in a sector slow to automate direct child care and safety functions.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could theoretically flag unusual equipment handling via video analysis or generate safety reminders, but the task is primarily about live presence and hands-on prevention, leaving little room for augmentation that maintains human control.
Augmentation potential	claude-sonnet-5	2/5	AI could help create safety instructions, checklists, or training materials in advance, but offers negligible real-time assistance during actual supervision and monitoring.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires real-time physical presence, attention to individual student behavior, and immediate intervention to prevent injury—capacities current AI systems lack entirely. No AI can currently monitor students in a classroom, detect unsafe behavior, and physically intervene.
Task automatability	claude-sonnet-5	1/5	This requires live physical presence, real-time supervision of children, and immediate intervention to prevent injury—none of which AI can perform end-to-end today.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Schools have legal duty of care; a human educator must be physically present and responsible for student safety. Liability, duty-of-care laws, and the requirement for immediate physical intervention create hard barriers to full automation.
Adoption barriers	claude-sonnet-5	5/5	Child safety supervision in schools involves strict duty-of-care, liability, and often licensing/certification requirements mandating a responsible adult be physically present.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Even if a vision system could partially automate monitoring, the cost of hardware, integration, ongoing oversight, and liability management would exceed the wage of a teaching assistant.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute providing physical supervision, so cost comparison favors the human by default since AI cannot deliver the output at all.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product performs live classroom safety monitoring and intervention. While cameras with detection exist, they cannot reliably interpret nuanced student behavior or ensure equipment/material care in real-world educational settings.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product supervises children's physical safety and equipment use in classrooms; this remains purely a human, in-person responsibility.

Assist in bus loading and unloading.

CI 0–0 · exposure 0 · augmentation 0 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	This task is fundamentally physical and supervisory in nature, performed in the institutional education sector where automation adoption is minimal due to regulatory and safety constraints.
Sector adoption velocity	claude-sonnet-5	1/5	Education support roles involving physical childcare are among the least digitized and slowest to see AI adoption for hands-on safety tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	AI offers no meaningful assistance for the core duties of physically supervising and assisting children during bus loading and unloading.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no meaningful assistance to a human physically helping children board or exit a bus.
Task automatability	claude-haiku-4-5-20251001	1/5	Bus loading and unloading requires physical presence, supervision of children, real-time safety decisions, and responsiveness to behavioral issues—tasks that current AI cannot perform end-to-end in the physical world.
Task automatability	claude-sonnet-5	1/5	This is a physical supervision and safety task requiring presence at bus doors to help children on/off safely; no AI system can physically perform this today.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Schools have strict legal duty-of-care obligations and liability requirements for child supervision during transport; only licensed and authorized staff can be responsible for this function.
Adoption barriers	claude-sonnet-5	5/5	Child safety and supervision duties carry strong legal, liability, and duty-of-care requirements mandating a responsible adult physically present.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	There is no AI alternative to perform this task; human staff costs remain entirely necessary and non-substitutable by any current technology.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI substitute for physical presence, so cost comparison favors the human by default since AI cannot perform the task at all.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product can autonomously manage the physical and supervisory demands of this task; it remains entirely dependent on human staff for safety and duty of care.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs physical child supervision during bus loading; this remains entirely research-stage or nonexistent for AI.

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.