Instructional Coordinators
25-9031.00Median wage $77,440/yr227,760 employed (US)Rank #207 of 923 scored · top 22% by substitution
Develop instructional material, coordinate educational content, and incorporate current technology into instruction in order to provide guidelines to educators and instructors for developing curricula and conducting courses. May train and coach teachers. Includes educational consultants and specialists, and instructional material directors.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution38
Exposure34
Augmentation72
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
30 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
0%
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%35
panel mean rating 2.4/5 → substitution pressure 35/100
Technical feasibility todayw 20%32
panel mean rating 2.3/5 → substitution pressure 32/100
Cost vs. human wagew 15%39
panel mean rating 2.6/5 → substitution pressure 39/100
Adoption barriersw 20%inverted — strong barriers lower the score50
panel mean rating 3.0/5 (barrier strength) → substitution pressure 50/100
Sector adoption velocityw 10%31
panel mean rating 2.3/5 → substitution pressure 31/100
Task breakdown (30 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Prepare grant proposals, budgets, and program policies and goals or assist in their preparation.
67CI 48–87 · exposure 70 · augmentation 88 · importance 3.9/5 · click for rater detail
Prepare grant proposals, budgets, and program policies and goals or assist in their preparation.
67| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Education technology and administrative sectors are actively adopting AI writing and proposal tools; many schools and grant-making bodies are piloting or deploying automated drafting. Adoption is visible in higher-ed and K–12 administrative practices, though full replacement is less common than augmented workflows. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education administration and instructional coordination are historically slower adopters of AI tools compared to finance or tech sectors, with pilots more common than full production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI is already widely used to assist coordinators in drafting proposals, building budget templates, and generating policy language, dramatically reducing time to first draft. The human-in-the-loop model—AI generates, human refines and approves—transforms coordinator productivity on these routine administrative tasks. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially assists with drafting, formatting, and brainstorming budget structures and policy language, meaningfully speeding up the preparation process while humans retain final control. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | LLMs and AI writing systems can now generate draft grant proposals, construct budgets with structured financial data, and draft policy/goal documents from templates and organizational data with minimal human intervention, meeting the ≥50% time-saving bar. End-to-end automation—from requirements to final document—is feasible with current tools including document generation agents and data integration. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft grant proposal text, budget templates, and policy language from prompts, but integrating institutional priorities, funder-specific requirements, and negotiation of goals still requires substantial human judgment and revision., so only partial time savings accrue. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Grant proposals and policies often require institutional authorization, signature by approved personnel, and funder/regulatory compliance checks that mandate human review and approval. Organizations also face stakeholder and compliance friction, but no blanket legal prohibition prevents AI-assisted or AI-drafted preparation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement restricts AI use in drafting grant proposals or budgets, though institutional sign-off and funder trust in named preparers create some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-based grant writing and budget preparation cost dollars per document (or pennies per hour via LLM API), compared to fully-loaded instructional coordinator wages ($30–50/hour or higher). The cost differential is at least an order of magnitude favoring automation. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting reduces drafting time significantly, but the overall task still requires skilled staff time for review, stakeholder alignment, and finalization, making cost savings moderate rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple mature products (including Proposify, Grantlab, and generic LLM/Word plugins) demonstrate reliable grant drafting and budget generation in production, though human review for compliance and accuracy remains standard practice. Feasibility is high but not universal: specialized or highly regulated grants still require expert human oversight. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like ChatGPT, grant-writing assistants, and budgeting tools are used in production to draft proposals and budgets, but accuracy on specific compliance details and program-specific policy language still requires human review, limiting reliability. |
Analyze performance data to determine effectiveness of instructional systems, courses, or instructional materials.
67CI 59–75 · exposure 62 · augmentation 88 · click for rater detail
Analyze performance data to determine effectiveness of instructional systems, courses, or instructional materials.
67| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | K–12, higher education, and corporate training sectors are actively adopting learning analytics platforms and BI tools. LMS vendors (Blackboard, Canvas, Cornerstone) routinely embed AI-driven analytics dashboards; adoption is measurably accelerating in digitized educational environments. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Education sector adoption of AI-driven analytics is growing but still lags behind finance or professional services, with many institutions still relying on manual or semi-automated review processes. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dashboards, automated report generation, and predictive performance models substantially amplify what a coordinator can accomplish—surfacing insights faster, enabling more frequent monitoring, and freeing time for deeper strategic interpretation and intervention design. The human remains essential but becomes far more productive. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools substantially enhance an instructional coordinator's ability to process and visualize performance data, surfacing patterns and flagging issues to speed decision-making while the human retains interpretive control. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can perform most of the data analysis components—aggregating performance metrics, calculating outcomes, generating statistical summaries, and identifying trends—at high quality and speed. However, contextual interpretation of *why* results occurred and strategic recommendations require some human judgment, preventing a full 5-rating, though the 50% time-saving bar is easily met. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can process performance datasets, run statistical analyses, and generate reports on instructional effectiveness, but interpreting context, pedagogical nuance, and making final judgments about curriculum still requires human expertise. Roughly half the analytical workload could be automated with proper data pipelines. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or licensing barriers exist; data analysis itself is not regulated. Organizational friction is low—educational and corporate bodies increasingly invest in analytics platforms. The main barrier is internal adoption and staff resistance, which are surmountable without hard legal constraints. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human perform this analysis, though institutional policies and accreditation standards may require human sign-off on curriculum decisions informed by the data. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Cloud-based analytics and AI systems cost far less per analysis run than paying instructional coordinators' loaded salaries (typically $50k–$70k annually) to perform manual data aggregation and basic statistical analysis. Costs favor automation by a significant margin for routine analyses. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated analytics tools can process large volumes of performance data far more cheaply than manual review, though some human oversight and interpretation cost remains. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature BI and analytics platforms with AI-driven insights are widely deployed in educational institutions and corporate L&D environments today. Tools can reliably ingest assessment data, compute performance indicators, and flag anomalies; minor gaps remain in nuanced causal interpretation without human review. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Learning analytics dashboards and BI tools (e.g., in LMS platforms) already perform data aggregation and basic effectiveness scoring in production, but nuanced evaluation of instructional quality is not yet reliably automated at scale. |
Develop instructional materials, such as lesson plans, handouts, or examinations.
66CI 59–72 · exposure 62 · augmentation 100 · click for rater detail
Develop instructional materials, such as lesson plans, handouts, or examinations.
66| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | EdTech and corporate learning are piloting AI-generated content actively, but K–12 and many universities adopt slowly due to risk aversion and institutional inertia. Adoption is visible in early-stage and corporate sectors but not yet mainstream in traditional education. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Education is adopting AI content-generation tools steadily but unevenly, with many districts still piloting or restricting AI use in curriculum development. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically assists instructional coordinators by generating drafts, suggesting learning objectives, tailoring difficulty levels, and adapting materials for accessibility—all while the coordinator retains creative and quality control authority. This is one of the strongest use cases for human-in-the-loop productivity gain. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up drafting of lesson plans, handouts, and test questions, letting coordinators focus on customization, review, and pedagogical judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can generate lesson plans, handouts, and exam questions from content specifications with minimal human input, easily meeting a 50% time saving threshold. While some refinement and domain-specific customization remain necessary, current LLMs can produce pedagogically sound first drafts at scale. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft lesson plans, handouts, and exam questions quickly, but aligning materials to specific standards, learner needs, and pedagogical goals still requires substantial human review and revision, limiting full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Institutional adoption does face some friction: curriculum review processes, union/credentialing preferences for human-authored materials, and organizational conservatism in K–12 and higher ed. However, no legal or licensing barrier requires a human to create these materials. |
| Adoption barriers | claude-sonnet-5 | 2/5 | There's no licensing requirement to draft instructional materials, though institutional approval processes and quality assurance for curriculum can add some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Inference cost for generating lesson plans and exams is typically $0.01–$0.10 per artifact, while instructional coordinator labor (fully loaded ~$50–$70/hour) costs $25–$50 per similar-quality first draft. AI is 100–500× cheaper per unit output. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating drafts of lesson plans and handouts via AI is very cheap compared to the hours an instructional coordinator would spend, even after accounting for review time. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products (Copilot, Claude, specialized EdTech platforms) reliably generate instructional materials in production settings. Error rates on factual accuracy and pedagogical appropriateness are material but acceptable for draft-stage outputs that require human review. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like ChatGPT, Khanmigo, and curriculum-generation tools are used in production to draft instructional content, but outputs often need editing for accuracy, standards alignment, and quality control. |
Plan and conduct teacher training programs and conferences dealing with new classroom procedures, instructional materials and equipment, and teaching aids.
59CI 35–84 · exposure 58 · augmentation 88 · importance 4.1/5 · click for rater detail
Plan and conduct teacher training programs and conferences dealing with new classroom procedures, instructional materials and equipment, and teaching aids.
59| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | K-12 and corporate training sectors are rapidly adopting AI-assisted and fully automated training platforms; learning analytics, adaptive learning, and virtual instructors are now common in many school districts and enterprise training programs. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education sector has historically been slower to adopt AI tools organization-wide compared to finance or tech, with pilots more common than full-scale production deployment for training design. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI excels at augmenting instructional coordinators by auto-generating first drafts of curricula, creating visual aids, handling scheduling logistics, and providing real-time analytics on training effectiveness, dramatically raising coordinator productivity while keeping humans in strategic roles. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can significantly assist in drafting curricula, generating slides, summarizing new instructional materials, and creating training content, boosting coordinator productivity substantially while humans still deliver and manage sessions. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Current AI systems can now generate comprehensive training curricula, design lesson plans, create presentation materials, schedule conferences, and even conduct virtual training sessions with adaptive content delivery—all with substantial time savings over manual planning and delivery. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft agendas, materials, and content for teacher training, but planning and conducting live conferences requires human facilitation, relationship-building, and adaptive in-person delivery that current AI cannot fully replace. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While some organizations prefer human instructors for relationship-building and trust, there are no legal licensing requirements or regulatory mandates that a human must conduct teacher training, and institutional friction is modest. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement strictly mandates a human, but organizational norms and expectations of live expert facilitation for professional development create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven training platforms (content generation, scheduling, virtual delivery, analytics) cost a fraction of hiring and compensating instructional coordinators and trainers for equivalent output, offering significant per-task savings. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can reduce prep time and content-creation costs, but the human labor of conducting live sessions, facilitation, and logistics still dominates costs, keeping overall savings modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (learning management systems with AI content generation, virtual instructors, automated scheduling tools) reliably handle curriculum design, material creation, and delivery at scale, though some organizations still use hybrid models for final human review of training quality. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for generating training content and presentations, but no deployed system reliably plans and conducts entire teacher training programs or conferences autonomously. |
Design instructional aids for stand-alone or instructor-led classroom or online use.
59CI 59–59 · exposure 50 · augmentation 88 · click for rater detail
Design instructional aids for stand-alone or instructor-led classroom or online use.
59| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Educational institutions are experimenting with AI-assisted content creation and design tools, but adoption remains inconsistent; pilots are common in higher ed and online platforms, but production deployment at scale is still emerging. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Education and corporate training sectors are piloting AI content generation tools actively but full production-scale adoption for instructional design remains uneven. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments instructional coordinators by rapidly generating visual mockups, content drafts, and layout suggestions, freeing them to focus on pedagogical strategy, customization, and quality review rather than routine design work. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up drafting of instructional aids, generating outlines, visuals, and practice materials, while coordinators retain control over final design and pedagogical fit. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can generate drafts of instructional aids (worksheets, slide decks, diagrams) and handle routine design layout tasks, achieving meaningful time savings. However, effective aids require pedagogical judgment, alignment with learning objectives, and testing—human oversight is essential to ensure quality and appropriateness for the target audience. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft slides, worksheets, and outlines quickly, but designing effective instructional aids still requires pedagogical judgment, alignment to learning objectives, and iteration that AI alone can't fully replace. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few hard regulatory or licensing barriers exist for creating instructional aids themselves. However, institutional processes, educator preference for custom design, and quality assurance requirements create moderate friction to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for creating instructional aids, though institutional quality standards and accreditation review create some friction against pure AI-generated materials. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-generated drafts cost pennies to produce after initial setup; even accounting for human review, the per-task cost is significantly lower than hiring instructional designers for each aid from scratch, though human oversight prevents full cost elimination. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating draft instructional materials via AI is far cheaper than a human instructional designer building them from scratch, though human review adds some cost back. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Tools like generative AI content platforms and design software with AI features exist and are used to prototype instructional materials, but they produce variable quality outputs that typically require substantial human refinement and verification before classroom deployment. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like generative AI slide/content generators and course-authoring tools are used in production, but outputs typically need significant instructional-design review and customization before deployment. |
Edit instructional materials, such as books, simulation exercises, lesson plans, instructor guides, and tests.
59CI 59–59 · exposure 50 · augmentation 88 · click for rater detail
Edit instructional materials, such as books, simulation exercises, lesson plans, instructor guides, and tests.
59| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Educational institutions and training organizations are experimenting with AI writing assistants and content tools, but adoption remains pilot-heavy rather than deep production deployment; some hesitation persists around content quality and institutional risk tolerance. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Education sector adoption of AI tools is accelerating but remains uneven, with many districts and organizations still in pilot phases for AI-assisted content editing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI writing assistants materially boost instructional coordinator productivity by automating routine editing passes, suggesting alternative phrasings, and flagging consistency issues, enabling coordinators to focus on pedagogical judgment and subject-matter accuracy rather than mechanical revision work. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up drafting, proofreading, and revising instructional materials while coordinators retain control over final pedagogical decisions, making it a strong augmentation case. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate significant portions of editing—grammar, style consistency, formatting—and generate draft revisions of simulation exercises and test questions, but meaningful oversight remains required for pedagogical accuracy and alignment with learning objectives, limiting time savings to roughly 40–60% depending on material complexity. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft edits, check grammar, consistency, and alignment with standards, but final pedagogical judgment about appropriateness and quality still requires human review, so only partial time savings are typical without workflow redesign. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal legal or licensing barriers exist; editing is not a regulated activity and no hard authorization is required, though organizations may prefer human review for accountability and quality assurance, creating some operational friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for editing instructional materials, though quality control and institutional review processes create moderate friction before AI-edited content is finalized. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Cost of inference plus human oversight for an AI-edited document is substantially lower than hiring a professional instructional editor at full loaded cost, though quality oversight by domain experts remains necessary, pushing the ratio into favor of AI. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI editing tools are inexpensive per document compared to a coordinator's loaded wage for the same volume of editing work, though integration and review still add some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Production systems (LLMs with grammar checkers, AI writing assistants) exist and are deployed in some educational settings, but they still produce errors in technical accuracy, context-specific pedagogy, and maintain narrow scope (better on prose than on simulation logic or test design integrity). |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Grammarly, AI writing assistants, and LLM-based editing tools are widely deployed for editing text, but specialized instructional editing (alignment to curricula, learning objectives, assessment validity) is less mature and requires human oversight. |
Research, evaluate, and prepare recommendations on curricula, instructional methods, and materials for school systems.
51CI 43–60 · exposure 53 · augmentation 75 · importance 3.7/5 · click for rater detail
Research, evaluate, and prepare recommendations on curricula, instructional methods, and materials for school systems.
51| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | School systems are slow-moving, budget-constrained, and tend to pilot rather than deploy AI at scale; adoption of AI for curriculum decisions is still in early pilot phases in most districts, with hesitancy around delegating pedagogical recommendations to automated systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a moderately slow-adopting sector for AI tools relative to information/finance industries, with pilots more common than production-scale deployment for curriculum decision-making. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI substantially assists coordinators by rapidly synthesizing research findings, comparing curricula side-by-side, flagging alignment gaps with standards, and drafting recommendation briefs—freeing the coordinator to focus on stakeholder engagement, local context fit, and final judgment rather than literature review and data compilation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI is highly useful for literature review, drafting comparative analyses of instructional methods, and generating first-pass materials, significantly speeding up the coordinator's research and drafting work while they retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can research curricula and instructional materials at scale, evaluate them against structured criteria, and generate ranked recommendations with detailed rationales. Current systems can synthesize large bodies of pedagogical literature and learning science data, substantially reducing the human research and synthesis time from 70–90% to 10–30%, meeting the ≥50% threshold. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft curriculum research summaries, compare instructional methods, and generate materials recommendations, but synthesizing local context, stakeholder needs, and final judgment still requires substantial human curation and validation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While no strict licensing barrier exists, school systems face organizational friction, stakeholder preference for human expertise in curricular decisions, and the need for district-specific validation and board approval—so recommendations cannot be fully automated without human coordinator sign-off and local judgment. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement strictly mandates a human for this specific research/recommendation task, but school district approval processes, accreditation standards, and stakeholder trust create meaningful organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI inference and synthesis cost per recommendation bundle is roughly 5–20× cheaper than the loaded hourly wage of a coordinator (typically $50–80k annually) performing the same research and evaluation work, especially when batched across multiple districts or curriculum areas. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI can cheaply generate research summaries and draft recommendations, but human review, validation against standards, and contextual judgment add cost, making overall savings moderate rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products like enterprise search, RAG systems, and LLM-based synthesis tools perform literature review and recommendation tasks in educational technology contexts, but output requires human validation for pedagogical alignment and district-specific constraints; no mature end-to-end system yet consistently handles the full evaluation pipeline without material oversight gaps. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | General-purpose LLMs and some edtech products can assist with curriculum research and drafting, but no mature deployed product reliably performs full curriculum evaluation and recommendation generation for school systems at scale. |
Research and evaluate emerging instructional technologies or methods.
47CI 35–59 · exposure 42 · augmentation 88 · click for rater detail
Research and evaluate emerging instructional technologies or methods.
47| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational institutions adopt technology slowly relative to other sectors; pilot programs for new instructional methods are common, but end-to-end AI-driven evaluation and selection remains rare in production K–12 and higher education environments. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Education sector adoption of AI tools is growing but remains uneven, with pilots more common than fully embedded production workflows for instructional research. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly augment this task by rapidly synthesizing research, comparing features and outcomes, identifying emerging trends, and drafting evaluation frameworks—allowing coordinators to focus on pedagogical judgment, stakeholder consultation, and implementation strategy rather than manual literature review. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially enhances coordinators' ability to survey emerging technologies, synthesize research, and generate comparative evaluations while the human retains decision-making authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in literature review and comparative analysis of technologies, the evaluation task requires deep pedagogical judgment, vendor assessment, pilot planning, and organizational fit—tasks that demand human expertise and cannot be fully automated to meet a 50% time-saving bar at equal quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can rapidly gather, summarize, and compare research on instructional technologies, saving significant time, but evaluating fit for specific institutional contexts requires human judgment and stakeholder knowledge AI lacks. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Some organizational friction exists—stakeholder buy-in, pilot coordination, and integration with existing curricula require human leadership—but no legal or licensing barrier prevents partial AI automation of research and synthesis steps; adoption depends mainly on institutional readiness. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI use in this research task, though institutional trust and preference for human expert judgment on curriculum decisions creates some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-driven research and initial synthesis can reduce labor, but instructional coordinators' deep domain knowledge and decision-making responsibility mean the human remains central; oversight and validation costs offset AI savings, making the all-in cost comparable to or slightly higher than human-only work. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-driven research and summarization tools are far cheaper than dedicating skilled coordinator hours to broad literature scanning, though final evaluation still needs human cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI tools exist for research aggregation, summarization, and initial comparative analysis (e.g., LLMs can draft technology overviews), but no deployed product reliably performs the full evaluation workflow (testing, validation, stakeholder integration, risk assessment) at production quality without substantial human oversight. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | AI research assistants and literature-review tools are deployed and used in education research today, but they still require human verification and lack deep contextual evaluation of pedagogical fit. |
Define instructional, learning, or performance objectives.
44CI 30–59 · exposure 38 · augmentation 75 · click for rater detail
Define instructional, learning, or performance objectives.
44| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational and training sectors have moderate digitization and relatively slow AI adoption in core instructional design functions. Pilots of AI-assisted objective drafting exist, but production deployment remains limited; cultural preference for human instructional expertise and risk-averse institutional decision-making slow adoption. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Education sector adoption of AI for instructional design is growing but uneven, with many districts and institutions still in pilot phases rather than full production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by generating objective drafts, offering multiple framings aligned with learning frameworks (Bloom's taxonomy, competency models), and flagging clarity issues—reducing drafting time and expanding option exploration. However, human judgment on strategic alignment and learner context remains essential, limiting augmentation to partial productivity gains. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up drafting and iterating on objectives, letting coordinators generate multiple aligned options and then apply their expertise to select and refine them. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Defining instructional objectives requires understanding organizational goals, learner needs, and pedagogical theory—tasks demanding human judgment. While AI can draft or suggest objectives from templates or examples, the core work of aligning learning outcomes with strategic intent and stakeholder values remains primarily human work, with AI providing limited time savings without substantial human revision. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft learning objectives (e.g., using Bloom's taxonomy) quickly given topic and audience context, but aligning them with institutional standards, learner needs, and strategic goals requires human judgment and stakeholder input that current AI cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While no formal licensing barrier exists, instructional coordinators work within institutional governance structures that value human expertise and accountability. Organizational friction, need for stakeholder buy-in, and liability for poorly defined learning objectives create moderate adoption friction, though these are organizational rather than legal barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human perform this specific drafting task, though institutional accreditation and quality assurance processes create some organizational friction and expectation of human sign-off. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference for drafting objectives is cheap, but integration into the instructional design workflow and required human review, revision, and validation substantially increase total cost. For many organizations, the human time to fix AI-generated objectives approaches or exceeds the cost of human-authored ones. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating draft objectives via LLMs costs pennies compared to instructional coordinator time, though the human review/refinement step still adds cost, keeping it just below the top tier. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No mature product reliably defines instructional objectives end-to-end in production environments. AI tools can generate objective statements from prompts, but they often lack context-sensitivity, pedagogical rigor, and alignment with actual organizational and learner needs—requiring substantial human oversight and rework. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | AI writing assistants and curriculum tools (e.g., ChatGPT, Curriculum design copilots) are used in production to draft objectives, but they still require review and contextual customization, so reliability at scale without human oversight is limited. |
Adapt instructional content or delivery methods for different levels or types of learners.
44CI 34–55 · exposure 38 · augmentation 75 · click for rater detail
Adapt instructional content or delivery methods for different levels or types of learners.
44| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption remains slow across K–12 and higher education sectors despite available tools; most institutions still rely on manual adaptation by coordinators. While edtech companies are investing heavily, actual production use of AI-driven adaptive systems at scale remains limited, and many organizations continue pilot phases. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Education is a moderate-adoption sector; AI tools for differentiated instruction are piloted widely but not yet deeply embedded in production workflows at scale. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments coordinator productivity by generating draft adaptations (simplified versions, multi-modal formats, language variants) and flagging content requiring modification, allowing humans to focus on pedagogical vetting and quality assurance rather than creation from scratch. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI is highly useful for generating multiple content variants, simplifying text, or suggesting alternative teaching approaches, substantially speeding up a coordinator's drafting work while they retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automatically generate adapted versions of content (e.g., simplified text, alternative formats, translations) and suggest delivery method modifications, but requires significant human oversight to ensure pedagogical appropriateness, accuracy of adaptations, and alignment with learning objectives. The task involves both technical adaptation and educational judgment that current systems handle partially. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can generate variant materials or reading-level adaptations, but selecting appropriate pedagogical strategies for diverse learner needs (IEPs, cultural context, classroom dynamics) requires human judgment beyond current systems' reliable scope.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Moderate barriers exist: educators and institutions often prefer human judgment on pedagogical decisions, accessibility compliance requirements require verification, and organizational adoption of adaptive systems varies widely. No hard licensing requirement exists, but liability concerns around educational quality and student outcomes create friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI-assisted adaptation, though special-education compliance (e.g., IEP requirements) and institutional accreditation standards add some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI tools for content adaptation and delivery modification are relatively affordable, but the full pipeline including integration with existing systems, human review of adaptations, and iterative refinement makes the all-in cost comparable to having a coordinator perform the task, especially for high-stakes educational contexts. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting of adapted materials is cheap per unit, but the coordinator's review, contextual judgment, and validation time offsets much of the savings, making costs roughly comparable when quality is held constant. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed products exist (LMS platforms with adaptive features, content generation tools, accessibility plugins) that can perform some aspects of adaptation, but material gaps remain in handling diverse learner profiles, maintaining instructional integrity across adaptations, and customizing delivery at scale without human verification. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some ed-tech products offer differentiated content generation, but adoption in real instructional coordination workflows is narrow and typically supervised rather than autonomous. |
Develop measurement tools to evaluate the effectiveness of instruction or training interventions.
43CI 34–52 · exposure 38 · augmentation 75 · click for rater detail
Develop measurement tools to evaluate the effectiveness of instruction or training interventions.
43| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational and training organizations are cautious about automating measurement tool development; adoption remains in the pilot and exploratory phase rather than widespread production deployment, particularly for mission-critical evaluation instruments where validity concerns are paramount. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education and training sectors have historically been slower to adopt AI tools for instructional design work compared to fast-moving sectors like finance or software. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist instructional coordinators by generating draft rubrics, suggesting evaluation frameworks, and automating template creation, allowing humans to focus on validation, customization, and ensuring measurement tools align with specific learning objectives and organizational context. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI is genuinely useful for brainstorming assessment items, structuring rubrics, and analyzing pilot data, substantially speeding up the coordinator's design process while they retain final control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Creating measurement tools for evaluating instruction requires understanding pedagogical theory, defining appropriate metrics, and designing psychometrically sound instruments—tasks that demand expert judgment and contextual knowledge. While AI can draft assessment templates or suggest evaluation frameworks, designing truly effective measurement tools typically requires human instructional expertise and iterative refinement. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft rubrics, assessment items, and survey instruments quickly, but validating psychometric soundness and aligning with specific learning objectives still requires substantial human expert judgment and iteration. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing requirements, organizational practice strongly favors human instructional designers for high-stakes evaluation tool development, and stakeholders often prefer human accountability for measurement validity and fairness, creating moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human create these tools, though organizational quality-control norms and accreditation standards create some review friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-assisted drafting of measurement tools could reduce iteration cycles and offer initial templates at low cost, but the full design process (validation, piloting, customization for specific contexts) still requires significant human expertise, making the all-in cost roughly comparable to hiring a skilled instructional designer. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI can cheaply generate first drafts of assessment tools, but the human validation, piloting, and refinement work remains labor-intensive, keeping overall cost savings moderate rather than dramatic. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No production system reliably designs validated measurement tools end-to-end; AI can generate assessment templates or suggest rubric structures, but deployed tools in real educational organizations still require human instructional designers to customize, validate, and pilot them. Research prototypes exist but lack the reliability and domain-specific validation needed for production use. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like ChatGPT and specialized ed-tech tools can generate quiz items, rubrics, and evaluation frameworks today, but they are not fully reliable for creating validated, standards-aligned measurement instruments without human review. |
Recommend changes to curricula or delivery methods, based on information such as instructional effectiveness data, current or future performance requirements, feasibility, and costs.
43CI 29–56 · exposure 45 · augmentation 75 · click for rater detail
Recommend changes to curricula or delivery methods, based on information such as instructional effectiveness data, current or future performance requirements, feasibility, and costs.
43| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Education and training institutions show slower AI adoption than information/finance sectors. While learning analytics is growing, actual use of AI-driven curriculum recommendations in production remains limited; most adoption remains in pilots or advisory roles rather than autonomous decision-making. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education sector adoption of AI for strategic/curricular decision-making remains slow and pilot-stage compared to faster-moving sectors like finance or tech. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI systems can powerfully assist coordinators by synthesizing enrollment trends, learning outcome data, cost models, and pedagogical research into structured recommendation briefings, significantly reducing time spent on data gathering and preliminary analysis while coordinators focus on stakeholder alignment and implementation strategy. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist by analyzing performance data, benchmarking against standards, and drafting recommendation options, substantially speeding up the coordinator's analytical work. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can analyze instructional effectiveness data, learning outcomes, cost-benefit analyses, and delivery method trade-offs at scale, generating evidence-based curriculum recommendations that meet the stated criteria. However, the task involves institutional judgment about organizational fit and stakeholder acceptance that may require human review, preventing a full 5-rating for complete autonomy. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can analyze effectiveness data and draft suggestions, but synthesizing organizational context, stakeholder needs, and judgment-based tradeoffs for curriculum change requires human oversight beyond current AI capability for full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Curriculum decisions typically require approval from faculty governance, accrediting bodies, and institutional leadership; while no single license legally prohibits automation, organizational norms, liability concerns, and the requirement for human educators to endorse changes create substantial friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement, but institutional approval processes, accreditation standards, and stakeholder buy-in create real organizational friction against pure AI-driven recommendations. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI inference on learning management system data is cheap, but integration with institutional data systems and the overhead of human review and sign-off to validate recommendations approach or match the cost of a human coordinator performing this analysis manually. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI can cheaply process data and draft reports, but human review, contextual validation, and stakeholder alignment still require significant paid time, keeping costs roughly comparable overall. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Learning analytics platforms and AI-powered educational decision-support tools exist and analyze instructional data, but deployed systems typically generate recommendations that require human coordinators to validate, contextualize, and implement. No mature product fully automates curriculum change recommendation at production scale without substantial human oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some ed-tech analytics tools surface performance trends, but no deployed product reliably generates vetted curriculum change recommendations factoring feasibility and cost at production scale. |
Provide analytical support for the design and development of training curricula, learning strategies, educational policies, or courseware standards.
40CI 25–55 · exposure 38 · augmentation 88 · click for rater detail
Provide analytical support for the design and development of training curricula, learning strategies, educational policies, or courseware standards.
40| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational institutions and training departments adopt AI analytics slowly relative to information-sector peers; pilots are common but production displacement of curriculum designers remains limited, and conservative governance slows adoption. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Education sector adoption of AI for curriculum analytics and content generation is growing but remains uneven, with pilots more common than fully integrated production systems. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at augmenting instructional coordinators through rapid literature synthesis, data visualization of learning outcomes, draft policy language generation, and comparative analysis of curricula—keeping humans in the loop while materially raising productivity on analysis-heavy phases. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially enhances the analytical and drafting portions of curriculum design work, letting coordinators explore more variations and evidence-based options quickly while retaining decision-making control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with data analysis, literature review, and generating draft curriculum frameworks, but the task requires substantial human judgment about pedagogical strategy, organizational context, and policy alignment that AI cannot reliably provide end-to-end at equivalent quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft curriculum frameworks, analyze learning objectives, and generate courseware standards drafts, but the analytical support requires contextual judgment about institutional needs, stakeholder alignment, and pedagogical fit that still needs human synthesis.'}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational policy development and curriculum standards carry regulatory oversight, institutional governance requirements, and legal/accreditation implications that typically mandate human expert sign-off and professional accountability. |
| Adoption barriers | claude-sonnet-5 | 2/5 | There's no formal licensing requirement blocking AI-assisted analytical work in curriculum design, though institutional approval processes and accreditation standards create some friction against fully automated decision-making. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tools (LLMs, analytics platforms) require significant human oversight for curriculum work, and the loaded cost of the human expert who must validate outputs approaches or exceeds the cost of AI inference and integration. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI can cheaply generate drafts and analyses, but the oversight, contextualization, and stakeholder negotiation involved in instructional coordination still require substantial paid human time, keeping costs roughly comparable rather than dramatically lower. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs comprehensive curriculum design and policy analysis independently; existing tools (learning management systems, analytics dashboards) support parts of the workflow but require expert human direction and validation throughout. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like generative AI tools and LMS analytics platforms are used for curriculum drafting and learning analytics, but they operate with narrow scope and require heavy human validation before deployment in real programs. |
Prepare or approve manuals, guidelines, and reports on state educational policies and practices for distribution to school districts.
38CI 25–51 · exposure 38 · augmentation 63 · importance 3.5/5 · click for rater detail
Prepare or approve manuals, guidelines, and reports on state educational policies and practices for distribution to school districts.
38| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational institutions and state agencies adopt AI slowly relative to information-sector firms, with significant organizational conservatism around official policy production and risk aversion in public education governance. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Public education administration is a comparatively slow-adopting sector for generative AI, with pilots more common than deep production use of AI for official policy documentation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by drafting policy language, organizing sections, and identifying inconsistencies in guidelines, raising coordinator productivity during the authorship phase while humans retain decision-making authority on content and approval. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially speeds up drafting, formatting, and summarizing complex policy content, letting instructional coordinators focus on review, judgment, and stakeholder alignment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft sections of policy documents and generate initial guidelines, the task requires substantive judgment about state educational policies, legal compliance, and institutional practices that demand human expertise and approval authority. End-to-end automation with equal quality is not feasible without significant human oversight. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft manuals, guidelines, and policy summaries efficiently, but final approval and alignment with specific state regulatory nuance still requires human judgment and authority, limiting full end-to-end automation.: |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | State educational policies typically require official approval and sign-off by authorized personnel; liability for incorrect guidance distributed to school districts creates strong accountability barriers. Official policy documents often have legal standing and cannot be delegated entirely to automated systems. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement for drafting, but approval of official state policy documents typically requires designated authority/sign-off within an education agency, creating institutional friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI-generated drafts plus required human review, editing, and approval by qualified coordinators remains comparable to or higher than direct human authorship, especially given the specialized expertise and oversight needed. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Drafting and formatting lengthy guideline documents is dramatically cheaper via AI than dedicating skilled staff hours, though human review costs remain. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform the full task of authoring and approving official state educational policy documents. AI writing assistants exist but cannot independently author authoritative policy manuals or bear responsibility for policy accuracy and legal compliance. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Generative AI tools are used in production for drafting policy documents and reports, but accuracy on nuanced state-specific educational regulations still requires substantial human review, limiting reliability. |
Present and make recommendations regarding course design, technology, and instruction delivery options.
36CI 30–41 · exposure 25 · augmentation 75 · click for rater detail
Present and make recommendations regarding course design, technology, and instruction delivery options.
36| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational institutions are early-stage in adopting AI for instructional design; most use cases remain pilots or assistive (generating templates) rather than autonomous recommendation systems. Adoption lags compared to information and finance sectors. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Education sector adoption of AI tools for content creation and instructional design is growing but remains behind finance or professional services, with pilots more common than full production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist coordinators by rapidly generating design options, summarizing technology capabilities, and surfacing evidence-based practices, allowing the human to focus on strategic judgment and stakeholder alignment. This is a strong augmentation scenario. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly assist by generating research summaries, comparison matrices, and draft recommendations, letting the coordinator focus on synthesis and stakeholder-specific presentation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft course designs and suggest technology options, presenting and making recommendations requires judgment about institutional context, learner needs, and strategic fit that resists full automation. Current AI systems lack the nuanced understanding of organizational constraints and stakeholder values needed to own these recommendations end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can generate draft recommendations and options for course design or technology, but presenting recommendations persuasively and contextualized to specific institutional politics, stakeholders, and budget requires human judgment and live interaction that current AI cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Institutional decision-making and educational governance norms create friction; recommendations on pedagogy and technology typically require human accountability and stakeholder buy-in. However, no strict licensing requirement prevents AI assistance, only organizational practice. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human for this task, but organizational trust, stakeholder relationships, and accountability for institutional decisions create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for generating course design suggestions and technology recommendations exist but require substantial human expert oversight, integration into workflows, and validation, making the all-in cost comparable to or higher than direct coordinator labor for this task. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI can cheaply draft content and comparisons, reducing prep time, but the presentation and negotiation portions still require paid human time, keeping overall cost roughly comparable to a human-only workflow with AI assistance. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs this task independently; existing systems (LMS recommendation tools, design templates) operate only as narrow components with significant human review required. Educational stakeholders still require human coordinators to validate and contextualize recommendations. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Generative AI tools can produce instructional design suggestions and comparisons of delivery modalities, but no deployed product autonomously presents and defends recommendations to stakeholders in this role today. |
Design learning products, including Web-based aids or electronic performance support systems.
35CI 32–38 · exposure 25 · augmentation 75 · click for rater detail
Design learning products, including Web-based aids or electronic performance support systems.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Corporate training and EdTech sectors are experimenting with AI-assisted content generation and design tools, with some pilots underway, but widespread production deployment of end-to-end AI-designed learning products remains limited. Adoption is accelerating but still primarily augmentative rather than fully automative. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Corporate training and EdTech sectors show moderate AI adoption for content generation and e-learning authoring, with pilots common but full instructional design workflows still largely human-led. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at rapidly generating draft content, suggesting interaction flows, and identifying pedagogical gaps, significantly accelerating the designer's ideation and iteration cycle. Current tools demonstrably improve productivity when the human remains in decision-making and quality control roles. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly aids instructional coordinators by generating draft content, quizzes, multimedia scripts, and interactive elements, letting humans focus on curriculum alignment and refinement. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with generating content and wireframe layouts, designing coherent learning products requires judgment about pedagogical flow, learner assessment, and iterative testing that current systems cannot fully automate. End-to-end design with equivalent quality typically still requires substantial human direction and refinement. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can accelerate drafting of instructional content and prototypes, but full end-to-end design of learning products requires pedagogical judgment, stakeholder needs analysis, and iterative testing that current systems cannot fully own. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No legal requirement mandates a licensed human designer, but organizations often have institutional preferences for human expertise and risk aversion around fully AI-generated learning systems. Quality standards and client expectation for human authority create moderate friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human for this task, though organizational standards and quality assurance for educational content create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted design tools reduce some drafting labor, but the skilled instructional designer's oversight, testing, and refinement work remains substantial. Total cost advantage is modest given the extensive human expertise still required for quality products. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools reduce some drafting and content-generation costs, but human oversight for instructional design quality, accessibility, and alignment with learning objectives keeps overall costs closer to comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Products like ChatGPT and design tools can generate draft content and mockups, but no mature deployed system reliably produces complete, pedagogically sound learning products without expert human review and rework. Current offerings lack integrated assessment design and learner validation capabilities. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like AI-assisted authoring tools (e.g., generative course builders) exist but are narrow-scope aids, not reliable end-to-end production of complete instructional systems at organizational scale. |
Teach instructors to use instructional technology or to integrate technology with teaching.
34CI 30–38 · exposure 25 · augmentation 63 · click for rater detail
Teach instructors to use instructional technology or to integrate technology with teaching.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational institutions are moderately digitized but slow-moving in labor replacement and adoption of AI-driven training. Most schools and universities still rely on human training coordinators; adoption is in pilot and incremental-augmentation phases, not production displacement. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Educational institutions are adopting AI tools for content creation and self-paced learning modules, but adoption of AI to replace human-led instructor coaching remains a pilot-stage activity. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist instructional coordinators by drafting training materials, generating tutorial content, analyzing common tech issues, and providing documentation—productivity aids that support but do not replace the coordinator's interpersonal and adaptive teaching role. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly help instructional coordinators create training materials, generate examples, answer technical questions, and design curricula, substantially boosting their productivity while they still lead engagement. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Teaching instructors to use technology requires interpersonal communication, real-time responsiveness to individual learning needs, and adaptive pedagogical judgment. While AI can generate training materials or documentation, delivering live instruction tailored to adult learners' pain points and skill levels remains difficult to automate end-to-end; current systems cannot reliably achieve 50% time savings at equal instructional quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Training instructors requires live demonstration, relationship-building, needs assessment, and hands-on coaching that current AI cannot fully replicate end-to-end, though it can assist with content and materials. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While no strict licensing requirement exists, organizations prefer human instructional coordinators for rapport, trust-building, and institutional knowledge; instructors often resist or learn poorly from purely automated systems, creating organizational friction and customer preference that slow AI substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI involvement, but organizational preference for human mentors/coaches and the interpersonal nature of coaching create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tools (chatbots, generative content) cost less per inference than a loaded instructor hourly rate, but integration and live support oversight drive total cost up. The human still typically provides the core tutoring, making true cost replacement unfavorable for quality-comparable outcomes. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI can cheaply generate tutorials or documentation, effective instructor training still requires human facilitation, coaching, and follow-up support, keeping overall costs comparable to human-led delivery. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI product reliably teaches instructors to use technology in production at scale. Chatbots and LMS tutorials exist but show material gaps in adaptive teaching, handling resistance, and personalizing guidance to an instructor's context; human training coordinators remain the standard in educational organizations. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tutoring and content-generation tools exist and are used to create training materials, but no deployed product independently runs full instructor professional-development programs at scale. |
Develop master course documentation or manuals according to applicable accreditation, certification, or other requirements.
34CI 25–43 · exposure 33 · augmentation 63 · click for rater detail
Develop master course documentation or manuals according to applicable accreditation, certification, or other requirements.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational institutions and training programs adopt AI relatively slowly due to regulatory conservatism, accreditation scrutiny, and governance structures. While some institutions pilot AI-assisted drafting, production adoption of autonomous course documentation systems remains limited and cautious. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education administration sectors are generally slower AI adopters compared to finance or tech, with instructional design pilots more common than full production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can effectively assist coordinators by generating initial drafts, organizing content templates, cross-referencing compliance checklist items, and highlighting potential gaps—raising coordinator productivity on routine documentation tasks. However, final validation and judgment remain human responsibilities, making this a genuine but bounded assistive role. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially aids drafting, formatting, and structuring documentation, letting coordinators focus on accuracy and compliance review rather than starting from scratch. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with drafting and organizing course content, the task requires deep knowledge of specific accreditation standards, legal compliance, and institutional nuances that vary widely. End-to-end automation would need reliable understanding of evolving regulatory requirements—current systems struggle with this consistency and would require extensive human oversight, falling short of the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft substantial portions of course documentation and manuals from templates and inputs, but ensuring alignment with specific accreditation/certification requirements needs human verification and judgment, limiting full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Accreditation bodies, certification agencies, and institutional governance often require human accountability for documentation accuracy and legal compliance. Many accreditors specify that documentation must reflect institutional expertise and judgment, creating both regulatory and organizational friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no law mandates a licensed human write these documents, accrediting bodies often require sign-off by qualified educators or subject matter experts, creating moderate organizational and compliance friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The overhead of ensuring compliance accuracy, validating outputs against accreditation standards, and human oversight typically exceeds the cost savings from AI-assisted drafting. Loaded coordinator wages are moderate, and the error-cost risk favors human verification, keeping overall costs comparable to or higher than manual creation. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting reduces time on boilerplate content, but the need for expert review, accreditation-specific customization, and compliance checks keeps overall cost roughly comparable to human-led work with AI assistance. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs accreditation-compliant course manual development end-to-end. LLMs can generate template text and assist with structure, but they frequently miss or misinterpret specific certification requirements and produce outputs requiring substantial human revision, making production reliability low. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Generative AI tools are used informally to draft curriculum documents, but no mature deployed product reliably ensures compliance with specific accreditation standards without heavy human review. |
Interview subject-matter experts or conduct other research to develop instructional content.
33CI 30–35 · exposure 25 · augmentation 75 · click for rater detail
Interview subject-matter experts or conduct other research to develop instructional content.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational and training organizations remain relatively slow in automating expert-engagement workflows; most adoption is limited to AI-assisted drafting and outline generation rather than autonomous expert interviewing or content synthesis. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Instructional design and corporate training sectors show slow-to-moderate AI adoption, with pilots for content generation more common than for research or interviewing tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully boost productivity by generating interview questions, organizing research notes, summarizing expert responses, and identifying content gaps, enabling coordinators to conduct more focused and comprehensive interviews with subject-matter experts. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools significantly help instructional coordinators by drafting interview questions, summarizing research, transcribing conversations, and organizing findings into instructional outlines. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help draft questions and organize information, conducting effective interviews requires real-time rapport-building, follow-up probing, and judgment about expert credibility that current systems struggle with at production quality. The research synthesis phase is partially automatable, but the expertise-extraction interview itself remains heavily human-dependent. |
| Task automatability | claude-sonnet-5 | 2/5 | Conducting live interviews with subject-matter experts requires human rapport, adaptive questioning, and judgment that current AI cannot fully replicate, though AI can assist with background research and question preparation.4/10 remains manual. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Organizational practice and institutional preference for human expertise validation create moderate friction; interviews often require judgment calls, consent, and human interpretation. No strict licensing barrier exists, but trust and domain knowledge expectations slow substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but organizational preference for human-led interviews and the need for nuanced follow-up questioning create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted research and interview prep tools save overhead, but the marginal cost of human interviews plus AI augmentation is still comparable to or exceeds pure human research work, especially when expert time is valuable and verification is required. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply handle secondary research and summarization, but the interview component still requires human time and scheduling, keeping overall cost savings modest compared to fully human-run research. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably conducts research interviews or subject-matter expert elicitation at the quality expected for instructional design. AI tools can assist with literature reviews and question generation, but end-to-end interview execution and expert validation remain in pilot or demonstration stages. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI research assistants and transcription tools exist and are used in content development workflows, but no deployed product independently interviews SMEs or reliably synthesizes expert knowledge into instructional content without heavy human oversight. |
Conduct needs assessments and strategic learning assessments to develop the basis for curriculum development or to update curricula.
30CI 25–35 · exposure 25 · augmentation 75 · click for rater detail
Conduct needs assessments and strategic learning assessments to develop the basis for curriculum development or to update curricula.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Education and training sectors show slower digital adoption and AI integration compared to finance or tech sectors. Most institutions remain in pilot or exploratory phases with AI-assisted curriculum work; widespread production deployment of AI for autonomous needs assessment is limited and cautious. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education administration and instructional design sectors have been slower to adopt AI agents for strategic assessment work compared to fast-moving information/finance sectors, with pilots more common than production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist coordinators by analyzing institutional data, identifying trends, generating assessment templates, and synthesizing stakeholder feedback, substantially accelerating the assessment process while keeping human experts in control of strategic decisions and curriculum direction. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by drafting survey instruments, analyzing qualitative feedback, summarizing findings, and identifying patterns in learning gaps, substantially speeding up parts of the assessment process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can analyze existing data and generate assessment frameworks, conducting needs assessments requires significant human judgment about stakeholder requirements, organizational context, and nuanced understanding of learning gaps that current AI systems struggle to capture independently. The task involves strategic decision-making that depends on domain expertise and contextual insight beyond pattern-matching. |
| Task automatability | claude-sonnet-5 | 2/5 | Needs assessments require gathering context-specific organizational data, stakeholder interviews, and judgment calls that AI cannot fully replicate; AI can assist with survey design and data synthesis but not conduct the full assessment autonomously.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational institutions typically require qualified instructional professionals to sign off on curriculum assessments and strategic learning decisions due to accreditation standards, institutional accountability, and professional licensing expectations. Organizational culture strongly favors human expertise in pedagogical and strategic learning decisions, creating meaningful barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human perform this, but organizational trust, stakeholder relationships, and accountability for curriculum decisions create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While AI can reduce analysis costs, the task requires expert human coordinators to validate findings, conduct interviews, and make strategic decisions. The all-in cost of AI systems plus necessary human oversight and quality assurance remains comparable to or higher than direct human performance of the task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI can cheaply analyze survey data or summarize documents, the human-led stakeholder engagement, interviews, and contextual judgment components still require significant paid staff time, keeping costs comparable to human-only approaches. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No mature production systems reliably conduct full needs assessments and strategic learning assessments end-to-end. AI tools can support data analysis and report generation, but deployed products lack the ability to independently conduct stakeholder interviews, synthesize qualitative feedback, and produce actionable strategic assessments at scale without substantial human oversight and validation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously conducts needs assessments end-to-end; existing tools support survey creation and text analysis but require substantial human-driven design and interpretation. |
Update the content of educational programs to ensure that students are being trained with equipment and processes that are technologically current.
30CI 25–35 · exposure 25 · augmentation 75 · importance 3.7/5 · click for rater detail
Update the content of educational programs to ensure that students are being trained with equipment and processes that are technologically current.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational institutions adopt technology slowly; most still rely on manual curriculum review cycles and committees. While some use learning management systems and content databases, few have deployed AI agents to autonomously update curriculum, and regulatory/accreditation barriers slow experimentation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education sector has historically been slower to adopt AI tools for curriculum design compared to tech-forward industries, with pilots more common than production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist coordinators by scanning industry trends, flagging outdated equipment references, suggesting current alternatives, and comparing curriculum to labor-market data. This productivity boost is substantial while keeping the coordinator in control of pedagogical and institutional decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully help by summarizing new technology trends, drafting updated content, and suggesting equipment/process changes, significantly speeding up the coordinator's research and drafting work while they retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help identify outdated equipment mentions and suggest current technologies, the task requires understanding pedagogical goals, curriculum design constraints, and institutional priorities—human judgment on what to update and how is essential. AI cannot autonomously decide what is 'technologically current' for specific learning outcomes. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires ongoing awareness of industry/technology trends, judgment about curricular relevance, and coordination with stakeholders—AI can assist research but cannot autonomously decide and implement curriculum updates end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational programs are subject to accreditation standards, state regulations, and institutional curriculum approval processes that typically require human sign-off. Liability for inadequate or incorrect technology training creates strong friction against full automation, and institutional governance structures mandate human accountability. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No formal licensing requirement bars AI assistance, but organizational approval processes, accreditation standards, and domain expertise create moderate friction to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for content analysis and technology scanning are relatively inexpensive, but the task still requires significant human oversight, validation, and domain expertise to implement updates safely. The blended cost approaches or slightly exceeds a human coordinator's time on this specific task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human instructional coordinators still need to do most of the analysis, stakeholder engagement, and validation; AI can cut some research time but the overall task still requires substantial paid human effort, so savings are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform this full task end-to-end. AI can assist with content analysis and suggestions, but products lack the domain expertise integration and institutional knowledge required to autonomously ensure training programs align with current industry standards across diverse fields. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools exist for content research and drafting curriculum materials, but no deployed product reliably tracks technological currency across equipment/processes and updates programs autonomously in production settings. |
Coordinate activities of workers engaged in cataloging, distributing, and maintaining educational materials and equipment in curriculum libraries and laboratories.
30CI 25–35 · exposure 25 · augmentation 50 · importance 3.0/5 · click for rater detail
Coordinate activities of workers engaged in cataloging, distributing, and maintaining educational materials and equipment in curriculum libraries and laboratories.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational institutions and curriculum libraries are relatively slow-moving on automation, with limited digitization of workflows and strong preference for human coordinators. Production adoption of AI for coordination roles in these settings is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Educational institutions are typically slower adopters of AI for administrative/operational coordination tasks compared to finance or tech sectors, with pilots for cataloging tools but limited penetration into worker coordination. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools could assist with scheduling workers, tracking inventory, and generating reports on material distribution, improving coordinator productivity in those specific areas while the human retains decision-making and staff management. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-powered inventory and cataloging systems can meaningfully assist coordinators in tracking and organizing materials, improving efficiency even though the human retains the coordination and supervisory role. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task involves coordination of personnel and inventory management with some supervisory judgment. While basic cataloging and some distribution logistics could be partially automated, the core coordination work—scheduling workers, resolving conflicts, managing complex interdependencies—requires human oversight and judgment that current AI cannot fully replace to meet the 50% time-saving bar end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | This involves coordinating human workers' physical and logistical activities (cataloging, distributing, maintaining materials/equipment), which requires on-site management and interpersonal direction that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational institutions typically require human coordinators with institutional knowledge, accountability, and direct oversight of staff and equipment. The supervisory nature and need for in-person communication and judgment create friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this coordination role, but organizational structure and the need for interpersonal supervision of staff create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tools for partial task support (scheduling, inventory tracking) have setup and integration costs that approach or meet coordinator wages for limited scope, but full task automation is not achieved, so marginal savings are modest relative to human labor. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply assist with cataloging databases, but the core coordination and supervision of workers still requires a human manager, so overall cost savings from AI are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs full worker coordination and activity planning at scale. Inventory management and cataloging tools exist, but the supervisory and interpersonal coordination aspects remain manual and are not handled by production AI systems. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | While inventory/cataloging software exists to assist with tracking materials, no deployed product autonomously coordinates worker activities in curriculum libraries; this remains a human management function with software support only. |
Advise and teach students.
29CI 25–34 · exposure 25 · augmentation 75 · importance 3.9/5 · click for rater detail
Advise and teach students.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Education is a laggard sector for AI automation; most deployments remain piloting or supplementary tutoring. Institutional conservatism, regulatory friction, and teacher union presence slow substitution; adoption accelerates only in unaccredited or informal learning contexts. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education sector adoption of AI for direct student advising/teaching remains largely pilot-stage, constrained by institutional caution, funding, and slower digitization compared to finance or tech sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments instructor productivity by auto-generating lesson plans, grading, providing personalized problem sets, and offering real-time student performance dashboards. These tools materially reduce teacher workload while instructors remain the primary source of judgment, motivation, and relationship. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools substantially help instructional coordinators by drafting lesson plans, personalizing content, and offering data-driven insights into student needs while humans retain the advising role. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can deliver factual explanations and generate practice materials, meaningful teaching requires adaptive pedagogical judgment, emotional attunement, and real-time responsiveness to student confusion that current systems cannot reliably provide at a quality matching experienced instructors. The task is too heterogeneous and context-dependent for ≥50% time savings at equal quality today. |
| Task automatability | claude-sonnet-5 | 2/5 | Advising and teaching involves relational judgment, mentorship, and adaptive interpersonal engagement with students that current AI cannot replicate end-to-end at equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teaching at K–12 and accredited institutions faces significant regulatory, legal, and liability barriers: credential requirements, duty of care toward minors, institutional accountability for learning outcomes, and parent/community trust heavily favor human instructors. Many jurisdictions legally require licensed teachers to conduct instruction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Some educational credentialing and institutional policies require certified educators for advising/teaching roles, creating moderate but not absolute barriers to full AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI tutoring inference cost is now substantially cheaper per interaction than human instruction, but integration, content preparation, and human oversight add overhead; the per-learner comparison depends heavily on scale and whether human instructors remain in the loop for certification or quality assurance. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI content delivery is cheap, the human oversight, relationship-building, and advising components still require paid staff, keeping blended costs closer to comparable than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Tutoring and educational chatbots exist but show material limitations in handling nuanced student questions, maintaining engagement over time, and diagnosing conceptual misunderstandings. No deployed AI system reliably replaces instructor-led teaching at comparable learning outcomes; most are narrow assistants rather than autonomous teachers. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tutoring products exist for narrow instructional content, but no deployed product reliably performs the full advising/teaching role including personalized mentorship and judgment calls at scale. |
Assess effectiveness and efficiency of instruction according to ease of instructional technology use and student learning, knowledge transfer, and satisfaction.
28CI 25–30 · exposure 25 · augmentation 63 · click for rater detail
Assess effectiveness and efficiency of instruction according to ease of instructional technology use and student learning, knowledge transfer, and satisfaction.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Education technology adoption is slower than in finance or professional services. While learning analytics tools are increasingly deployed, most institutions still rely heavily on human coordinators for final assessment and decision-making. Pilot-stage adoption is common; production automation of the full task remains rare. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education sector is a laggard in AI adoption relative to finance or tech, with pilots for learning analytics common but full evaluative automation rare. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered dashboards and analytics tools can help coordinators by surfacing patterns in learning data, flagging problem areas, and auto-generating summary reports. However, the human coordinator remains essential for interpreting context, investigating root causes, and making pedagogical recommendations. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by aggregating student feedback, analyzing usage logs, and flagging patterns, significantly aiding the coordinator's evaluative process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help analyze quantitative learning metrics, student satisfaction surveys, and technology usage logs, the core task requires nuanced judgment about cause-and-effect relationships, contextual understanding of pedagogical issues, and synthesis across heterogeneous data sources. Current AI cannot reliably replace the holistic assessment and decision-making that drives instructional improvement. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires synthesizing qualitative judgment about pedagogy, learner experience, and organizational context that AI can support but not fully replace; only partial sub-tasks like survey analysis are automatable today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational assessment decisions often sit within institutional governance, accreditation frameworks, and faculty oversight requirements. Responsibility for instructional effectiveness typically rests with human coordinators and faculty, creating organizational and accountability barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement, but institutional trust, accreditation expectations, and need for professional judgment in evaluating educational quality create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI analytics platforms and LMS dashboards require institutional subscriptions, integration setup, and ongoing human review and interpretation. For a full assessment workflow, the total cost (software licensing, tuning, oversight) remains comparable to or exceeds the time cost of a coordinator's direct evaluation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply crunch survey/usage data, but the human synthesis, contextual judgment, and stakeholder interpretation still require costly expert time, keeping overall cost comparable to human-only work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Products exist for automated quiz grading, basic learning analytics dashboards, and survey analysis, but no mature system reliably performs the full assessment task end-to-end with the contextual judgment required. Available tools operate on narrow subcomponents and still require significant human interpretation and validation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some analytics tools can process satisfaction surveys or usage data, but no deployed product reliably assesses overall instructional effectiveness and technology usability holistically. |
Advise teaching and administrative staff in curriculum development, use of materials and equipment, and implementation of state and federal programs and procedures.
28CI 25–30 · exposure 25 · augmentation 63 · importance 4.0/5 · click for rater detail
Advise teaching and administrative staff in curriculum development, use of materials and equipment, and implementation of state and federal programs and procedures.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 and higher-ed institutions adopt technology slowly and conservatively, especially for regulatory and curriculum governance roles. Pilot use of AI drafting tools is rising, but production displacement of advisory coordinators remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education is a comparatively slow-adopting sector for AI-driven advisory replacement, with pilots for AI tutoring/content tools more common than staff-advisory automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist coordinators by summarizing regulations, generating curriculum templates, and organizing materials—useful for productivity—but human experts must validate, contextualize, and make final recommendations given the stakes. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by drafting curriculum materials, summarizing state/federal regulations, and suggesting implementation strategies, significantly speeding up the coordinator's research and drafting work. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help draft curriculum materials and summarize state/federal regulations, the advisory task requires understanding organizational context, staff expertise, and stakeholder constraints that demand human judgment. Most of the task involves dialogue and customized guidance rather than end-to-end automation. |
| Task automatability | claude-sonnet-5 | 2/5 | This task requires ongoing relational advising, contextual judgment about specific schools/staff, and interpretation of evolving regulations, which current AI cannot fully replace end-to-end despite being able to draft guidance documents. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory and accountability barriers are substantial: federal program compliance, curriculum approval by district/state authorities, and liability for educational decisions create legal gatekeeping. Educators and administrators typically expect human expertise and sign-off on compliance matters. |
| Adoption barriers | claude-sonnet-5 | 3/5 | There's no strict licensing requirement to give this advice, but organizational trust, liability for regulatory misadvice, and preference for human coaching create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI services (LLM APIs, content generation) cost less per hour than an instructional coordinator's salary, but integration, prompt engineering, and mandatory human oversight add significant labor. The cost advantage is marginal when accounting for quality control. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI could cheaply generate reference material, the human relationship-building, in-person coaching, and accountability for compliance advice keep overall costs comparable to or only modestly cheaper than a human coordinator. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No production systems reliably advise on curriculum development and regulatory compliance as a core offering. AI tools exist for drafting and information retrieval, but deployed products do not yet handle the full advisory role with the consistency and accountability institutions require. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI chatbots can answer curriculum questions or summarize compliance requirements, but no deployed product reliably serves as an institutional advisor to staff on program implementation at scale. |
Recommend, order, or authorize purchase of instructional materials, supplies, equipment, and visual aids designed to meet student educational needs and district standards.
25CI 25–25 · exposure 25 · augmentation 63 · importance 3.9/5 · click for rater detail
Recommend, order, or authorize purchase of instructional materials, supplies, equipment, and visual aids designed to meet student educational needs and district standards.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | K–12 and district administration remain relatively low-digitization sectors with organizational friction around vendor systems and procurement workflows; adoption of AI-assisted ordering is nascent and mostly in pilot phases rather than production displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | K-12 education administration is a historically slow-adopting sector for AI-driven decision tools, with pilots more common than production deployment for procurement processes. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by filtering materials against curriculum standards, flagging budget implications, and cross-referencing availability, enabling coordinators to review and decide faster—but the human must remain in the loop for final judgment and authorization. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by researching materials, comparing costs/reviews, aligning options to standards, and drafting recommendation reports, significantly speeding up the coordinator's workflow while they retain final authorization. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with material recommendations via analysis of curriculum standards and inventory systems, the task requires human judgment about district-specific needs, budget constraints, and student populations that AI cannot fully automate end-to-end. Ordering and authorization involve discretionary decisions that fall short of the ≥50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help research and compare instructional materials but the actual authorization, budget approval, and procurement decision-making requires human judgment tied to institutional standards and accountability, limiting full automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | District procurement policies typically require human authorization and sign-off for budget allocation and vendor selection; regulatory and fiduciary requirements mean a qualified administrator must legally approve purchases, and liability for unsuitable material selection rests with the organization. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Purchasing authority in public school districts typically requires designated staff with budgetary and administrative authorization, creating significant organizational and possibly legal barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI systems require significant setup (curriculum data ingestion, vendor integration, custom workflows) and ongoing human oversight for authorization decisions, making the all-in cost comparable to or potentially exceeding the time savings from partial automation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI research assistance is cheap, the human oversight, approval authority, and compliance checking required keep overall costs closer to the human baseline. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No mature production system reliably performs full procurement authorization for educational institutions today. AI can support recommendation engines and inventory flagging, but deployed products have narrow scope (e.g., simple product search) and lack integration with district approval workflows and compliance systems. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some procurement/recommendation tools and AI-assisted curriculum evaluators exist, but no deployed product reliably performs authorized purchasing decisions for districts at scale today. |
Observe work of teaching staff to evaluate performance and to recommend changes that could strengthen teaching skills.
15CI 5–25 · exposure 13 · augmentation 38 · importance 4.5/5 · click for rater detail
Observe work of teaching staff to evaluate performance and to recommend changes that could strengthen teaching skills.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Education sectors, particularly K–12 and higher education, remain low-digitization domains with strong human oversight norms. Adoption of AI for teaching evaluation is negligible; pilots are rare and organizational resistance is high. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education sector adoption of AI for teacher evaluation is slow, with pilots limited to video analytics tools and little production-scale deployment for formal performance reviews. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by organizing objective classroom data (time-on-task, student engagement metrics) or generating initial observation transcripts, but the core task—evaluating teaching quality and recommending improvements—requires human judgment, rapport, and accountability that AI augmentation cannot meaningfully enhance without the human already being the primary decision-maker. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by transcribing lessons, flagging engagement patterns, or summarizing observation notes, aiding the coordinator's analysis without replacing the judgment-based evaluation itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires nuanced behavioral observation, subjective judgment about teaching quality, and individualized recommendations—capabilities current AI systems lack. While AI could log objective metrics (attendance, test scores), meaningful evaluation of teaching performance and personalized coaching recommendations demand human expertise and contextual understanding that automated systems cannot reliably deliver. |
| Task automatability | claude-sonnet-5 | 1/5 | Direct in-person classroom observation and nuanced evaluation of teaching quality requires physical presence, contextual judgment, and interpersonal rapport that current AI cannot replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong adoption barriers exist: instructional coordinators' authority to evaluate teaching typically derives from institutional hierarchy and professional credential; recommendations carry employment consequences; and organizational culture strongly prefers human judgment on sensitive personnel matters. Legal and union agreements often specify how performance reviews must be conducted. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Teacher evaluations often carry employment, certification, and union/contractual implications requiring a qualified human evaluator to observe and sign off, creating strong organizational and legal barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI infrastructure (video capture, processing, oversight by qualified instructional coordinators to validate AI recommendations) exceeds the cost of having the coordinator directly observe and evaluate, especially given the high error-cost of misjudging teaching performance. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools for lesson analysis exist but still require significant human oversight, observation logistics, and interpretation, so cost savings versus a human coordinator are modest at best. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably observes teaching staff in classrooms and produces credible performance evaluations and improvement recommendations. Conceptual systems exist for classroom analytics, but production deployments handling the full human-judgment dimension of this task do not exist at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some products can analyze recorded classroom video or transcripts for engagement metrics, but no deployed product reliably performs full teacher performance evaluation and coaching recommendations in production. |
Address public audiences to explain program objectives and to elicit support.
15CI 5–25 · exposure 8 · augmentation 63 · importance 3.7/5 · click for rater detail
Address public audiences to explain program objectives and to elicit support.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational and instructional sectors have not adopted AI for live public address; such use would be highly novel and resistance would be high, making actual deployment rare. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education administration sectors are slow adopters of AI for public-facing persuasion and community engagement tasks, with AI used mainly for content support rather than delivery. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by drafting talking points, generating speaker notes, and analyzing audience data post-hoc, helping coordinators refine messaging and prepare more effectively, though the human must remain the primary voice. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully help coordinators draft talking points, anticipate audience questions, and refine messaging, substantially improving preparation even though delivery remains human. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate presentation scripts and speaking notes, addressing live audiences requires real-time responsiveness to crowd energy, questions, and social cues—capabilities current AI lacks at production quality. Automated delivery would fall well short of the 50% time-savings threshold for equivalent persuasive impact. |
| Task automatability | claude-sonnet-5 | 1/5 | Live public speaking to explain objectives and persuade an audience requires physical presence, real-time adaptation to audience reactions, and personal credibility that current AI cannot replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Audiences strongly prefer human contact for persuasion and trust-building; organizations face reputational risk deploying AI to solicit public support. There are also implicit organizational and social norms against algorithmic representation in civic/educational contexts. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement, but organizational and social norms strongly favor a credible human presenter to build trust and answer questions, creating moderate friction against substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems capable of public presentation would require extensive custom setup, real-time monitoring, and human oversight to avoid reputational damage, making total cost exceed that of hiring or deploying a human coordinator. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI can cheaply generate slides or scripts, the actual delivery and persuasive interaction still requires a human, so overall cost savings are limited to prep work only. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs live public address and persuasion at the quality expected of an instructional coordinator. Video synthesis and chatbots exist but cannot authentically engage audiences or build the credibility needed to elicit genuine support. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously delivers public presentations and elicits stakeholder support on behalf of an instructional coordinator; this remains a human-performed activity. |
Interpret and enforce provisions of state education codes and rules and regulations of state education boards.
13CI 0–25 · exposure 13 · augmentation 50 · importance 4.1/5 · click for rater detail
Interpret and enforce provisions of state education codes and rules and regulations of state education boards.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Education administration is a highly regulated, public-sector domain with strong human oversight norms and minimal demonstrated AI agent deployment for enforcement decisions. Adoption of AI for autonomous regulatory interpretation remains negligible. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Public education administration is a slow-adopting sector for AI in compliance-related functions, with pilots for research assistance but little production deployment for enforcement tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by summarizing regulations, flagging relevant provisions, or organizing case comparisons for a human coordinator to review and interpret. However, the core judgment remains human-centered with only limited productivity lift from current assistive tools. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly help coordinators by quickly retrieving, summarizing, and cross-referencing dense regulatory text, improving efficiency in preparing interpretations even though final enforcement remains human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Interpreting and enforcing education codes requires legal judgment, discretionary application of nuanced regulations to specific contexts, and authority that cannot be delegated to AI. Current AI cannot reliably handle the ambiguity, precedent-based reasoning, and liability exposure inherent in regulatory enforcement. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can retrieve and summarize regulatory text but authoritative interpretation and enforcement decisions require contextual judgment, discretion, and accountability that current systems cannot reliably provide end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Education codes are statutory and regulatory; enforcement typically requires human authority, professional judgment, and legal accountability. Licensure, liability exposure for incorrect interpretation, and explicit requirement for human decision-making create strong legal and organizational barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Enforcement of state codes typically requires designated authority within an institution, creating accountability and legal liability barriers that prevent full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Regulatory enforcement and code interpretation require licensed human expertise and assume legal responsibility; the human cost is substantial but the liability and oversight burden of AI-driven enforcement would be prohibitively expensive, making AI uneconomical for this task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI could cheaply assist with document search and summarization, but the enforcement and judgment components still require human coordinators, keeping overall cost savings modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No production system today performs autonomous interpretation and enforcement of education codes. While AI can retrieve and summarize regulations, the judgment and decision-making authority required for enforcement remains an expert human domain with no mature deployed alternative. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Legal/regulatory research assistants exist and are used for drafting summaries, but no deployed product reliably interprets and enforces education codes in real institutional settings without heavy human oversight. |
Conduct or participate in workshops, committees, and conferences designed to promote the intellectual, social, and physical welfare of students.
13CI 5–20 · exposure 8 · augmentation 50 · importance 4.0/5 · click for rater detail
Conduct or participate in workshops, committees, and conferences designed to promote the intellectual, social, and physical welfare of students.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational institutions have historically slow adoption of AI for core instructional and student-facing roles. Workshop facilitation and committee participation remain deeply human-centered functions, and displacement in this domain is minimal to non-existent in current practice. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Education sector adoption of AI for meeting/workshop facilitation is slow; institutions use AI for notes or logistics but not to conduct or represent in such forums. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist instructional coordinators by automating scheduling, drafting workshop agendas, generating background materials, and tracking attendance, meaningfully improving preparation efficiency. However, the live delivery and interpersonal aspects of the task remain human-driven, so augmentation is partial rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help draft agendas, summarize meeting notes, prepare materials, or synthesize discussion points, moderately aiding preparation and follow-up around these activities. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help draft workshop materials, send invitations, and schedule logistics, the core task—conducting or actively participating in workshops to promote student welfare—requires human presence, emotional intelligence, and real-time responsiveness that current AI cannot replicate end-to-end. The human facilitation component is irreducible. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires live human facilitation, in-person presence, relationship-building, and real-time interpersonal engagement that current AI cannot substitute for end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Organizational culture, institutional expectations, and the human-contact requirement to achieve meaningful student welfare outcomes create strong friction. Students and institutions expect human instructors and coordinators to lead these activities; substituting an AI would conflict with the stated educational and social mission. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Committee participation and stakeholder representation typically require an accountable human role, organizational trust, and often institutional policy mandating human involvement in student welfare decisions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools can assist with preparation and logistics (scheduling, material generation), but cannot replace the labor of actually running a workshop or chairing a committee. The cost of humans running these events remains primary; AI supplements but does not reduce overall staffing needs materially. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this role, so cost comparison favors the human entirely; AI cannot replace the output. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can autonomously conduct or meaningfully participate in live workshops, committees, or conferences designed to support student welfare. This requires human judgment, relationship-building, and adaptive facilitation that current systems cannot perform in production at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product runs or participates in workshops/committees as a human stakeholder; this remains firmly a human social activity. |
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.