Career/Technical Education Teachers, Postsecondary

25-1194.00

Median wage $63,820/yr114,110 employed (US)Rank #367 of 923 scored · top 40% by substitution

Teach vocational courses intended to provide occupational training below the baccalaureate level in subjects such as construction, mechanics/repair, manufacturing, transportation, or cosmetology, primarily to students who have graduated from or left high school. Teaching takes place in public or private schools whose primary business is academic or vocational education.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution31

Exposure28

Augmentation65

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

20 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

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%28

panel mean rating 2.1/5 → substitution pressure 28/100

Technical feasibility todayw 20%27

panel mean rating 2.1/5 → substitution pressure 27/100

Cost vs. human wagew 15%33

panel mean rating 2.3/5 → substitution pressure 33/100

Adoption barriersw 20%inverted — strong barriers lower the score40

panel mean rating 3.4/5 (barrier strength) → substitution pressure 40/100

Sector adoption velocityw 10%27

panel mean rating 2.1/5 → substitution pressure 27/100

Task breakdown (20 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 reports and maintain records, such as student grades, attendance rolls, and training activity details.

CI 75–86 · exposure 80 · augmentation 88 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	5/5	Postsecondary institutions have near-universal adoption of LMS platforms and automated grading/attendance systems; automation in this domain is already standard practice across colleges and universities.
Sector adoption velocity	claude-sonnet-5	4/5	Educational institutions have broadly adopted LMS and administrative software for these functions, though postsecondary CTE programs may lag slightly behind fully digitized information-sector norms.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI tools can assist instructors by auto-flagging attendance patterns, suggesting grade adjustments based on learning analytics, and auto-populating reports, significantly reducing administrative burden while the instructor retains oversight.
Augmentation potential	claude-sonnet-5	5/5	AI-enabled administrative tools substantially reduce time spent on grade entry, attendance logging, and report generation, letting instructors focus on teaching while systems handle record maintenance.
Task automatability	claude-haiku-4-5-20251001	4/5	Most of this task—grade recording, attendance tracking, and logging training details—can be automated end-to-end with learning management systems (LMS) and integrated AI tools that parse rosters, update records, and generate reports with minimal human intervention, easily exceeding 50% time savings.
Task automatability	claude-sonnet-5	4/5	Grade recording, attendance tracking, and training-log compilation are structured, template-driven tasks well within reach of current LMS automation and AI tools, though initial setup and data entry integration still require some human involvement.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While schools must maintain accurate records for compliance, no specific licensing requirement mandates a human perform data entry or report compilation; the main friction is institutional inertia and FERPA oversight rather than legal prohibition.
Adoption barriers	claude-sonnet-5	2/5	Some institutional policy requires teacher sign-off on final grades and certification records, but the record-keeping mechanics themselves face minimal regulatory or licensing barriers.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	LMS and automated record systems cost pennies per student per term; the loaded hourly wage for a teacher or administrative staff member performing manual record-keeping is orders of magnitude higher.
Cost vs. human wage	claude-sonnet-5	4/5	Once integrated into institutional systems, automated record-keeping costs are far lower per unit than manual clerical teacher time, though some licensing and integration costs exist.
Technical feasibility today	claude-haiku-4-5-20251001	5/5	Mature, widely deployed products (Blackboard, Canvas, PowerSchool, Google Classroom) reliably perform grade tracking, attendance recording, and report generation at scale in educational institutions today.
Technical feasibility today	claude-sonnet-5	4/5	LMS platforms (Canvas, Blackboard, PowerSchool) already automate gradebook calculations, attendance rolls, and reporting in production at scale across postsecondary institutions.

Develop teaching aids, such as instructional software, multimedia visual aids, or study materials.

CI 49–67 · exposure 58 · augmentation 88 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Educational technology adoption is uneven: some institutions pilot generative AI for content, but widespread production deployment of AI-generated teaching aids remains limited due to quality concerns, institutional conservatism, and faculty resistance.
Sector adoption velocity	claude-sonnet-5	3/5	Postsecondary education is adopting AI tools for content creation at a moderate pace, with growing pilots and some institutional guidance, but broad systematic deployment is uneven across CTE programs.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI significantly augments instructors by rapidly generating draft content, visual mockups, and study material scaffolds, allowing teachers to focus on customization, pedagogical refinement, and validation rather than content creation from scratch.
Augmentation potential	claude-sonnet-5	5/5	AI is highly effective for augmenting instructors by rapidly generating drafts, slide decks, and study guides that the teacher then customizes and refines for their specific technical curriculum.
Task automatability	claude-haiku-4-5-20251001	3/5	AI can generate substantial portions of teaching aids—drafting instructional content, creating study materials, and producing basic multimedia assets—but the task requires curriculum alignment, pedagogical judgment, and quality control that still benefit from human oversight. Current tools achieve meaningful time savings on content generation but not the full end-to-end workflow.
Task automatability	claude-sonnet-5	4/5	Generative AI can draft slides, worksheets, quizzes, and even outline instructional software or interactive materials quickly, covering a large share of this task's drafting work, though customization to specific technical/vocational content and hands-on skills still needs instructor input.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Educational institutions typically retain human oversight for curricular content and intellectual property concerns; instructor sign-off on teaching materials is standard practice, and institutional policies often require human accountability for educational quality and accessibility compliance.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement forces a human to create teaching aids, though institutional quality standards, accreditation content requirements, and instructor preference create some friction.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI-assisted generation of teaching aids is substantially cheaper than hiring instructional designers or multimedia specialists to build the same materials from scratch, reducing per-unit creation costs significantly.
Cost vs. human wage	claude-sonnet-5	4/5	AI subscription and generation costs are minor compared to the instructor time saved drafting materials, making AI substantially cheaper for the bulk of content creation.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Products exist (generative AI for content, design tools, multimedia platforms) that demonstrably assist in creating teaching aids, but error rates and inconsistent pedagogical fit limit production-scale reliability. Material human review is still required to ensure instructional validity.
Technical feasibility today	claude-sonnet-5	3/5	Products like ChatGPT, Canva, and AI-driven course-authoring tools (e.g., Articulate AI features) are used by educators today to generate materials, but reliability for specialized technical/vocational content and quality control still requires human review.

Review enrollment applications and correspond with applicants to obtain additional information.

CI 34–67 · exposure 45 · augmentation 88 · importance 3.6/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Postsecondary education, especially career/technical programs, remains relatively slow in adopting AI-driven admissions workflows; most institutions still rely on manual review with limited AI integration beyond basic document management.
Sector adoption velocity	claude-sonnet-5	3/5	Higher education administration has moderate AI adoption in admissions/enrollment tech, with growing but not yet pervasive use of automated correspondence tools.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can substantially assist by automatically flagging incomplete applications, suggesting follow-up questions based on program requirements, and drafting personalized emails that staff then refine, significantly raising productivity of admissions staff while keeping human judgment central.
Augmentation potential	claude-sonnet-5	5/5	AI tools strongly assist staff in flagging missing information, drafting follow-up emails, and summarizing applications, significantly speeding the review process while humans retain decision authority.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can extract structured data from applications and generate templated correspondence, it struggles with the nuanced judgment required to assess applicant fit and craft personalized, contextually appropriate follow-up questions that vary significantly by program and applicant background.
Task automatability	claude-sonnet-5	4/5	Reviewing applications and drafting correspondence to request missing information is a structured, text-based workflow well-suited to current AI systems, especially with document parsing and templated communication.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Educational institutions typically require human review of admissions decisions for accreditation and fairness reasons, and some institutions have policies or values preferring direct human contact with prospective students, creating organizational friction against full automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement for this administrative task, though institutional policy and FERPA-related data handling create some compliance friction.
Cost vs. human wage	claude-haiku-4-5-20251001	3/5	Basic application screening and template-based email generation are cheap at scale via AI, but the need for human review of judgments and personalization keeps total integrated cost comparable to lower-wage administrative staff handling this work.
Cost vs. human wage	claude-sonnet-5	4/5	Automated document review and email generation cost a fraction of staff time per application, though some integration and oversight costs remain.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Chatbots can generate standard response emails and extract basic application data, but deployed systems lack reliable judgment for determining when and what additional information is truly needed, making full end-to-end automation prone to errors that require human oversight.
Technical feasibility today	claude-sonnet-5	3/5	Admissions software and AI-assisted CRM tools exist and are used in higher-ed enrollment offices, but full autonomous review and correspondence with edge-case judgment still typically involves human oversight.

Prepare outlines of instructional programs and training schedules and establish course goals.

CI 29–56 · exposure 38 · augmentation 88 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Educational institutions adopt digital tools slowly; curriculum planning remains largely a human-led, committee-based process with limited AI integration in production. Most adoption is at pilot or planning stage, not scaled displacement.
Sector adoption velocity	claude-sonnet-5	2/5	Postsecondary vocational/technical education is a moderate-to-slow adopter of AI tools compared to fast-moving sectors like finance or tech, with usage still emergent and inconsistent across institutions.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can substantially assist educators by generating draft outlines, suggesting scheduling alternatives, mapping learning objectives, and surface-level alignment with standards, allowing teachers to focus on contextual refinement, career-pathway fit, and institutional integration.
Augmentation potential	claude-sonnet-5	5/5	AI is highly useful for brainstorming, drafting, and restructuring course outlines and schedules, significantly speeding up an instructor's planning process while they retain final control.
Task automatability	claude-haiku-4-5-20251001	2/5	AI can draft outlines and course schedules quickly, but instructional design requires integrating institutional constraints, accreditation standards, student competency levels, and career pathways that demand human judgment and contextual knowledge. Current AI cannot reliably handle the full scope of establishing meaningful course goals aligned with evolving industry needs.
Task automatability	claude-sonnet-5	3/5	AI can draft syllabi, course outlines, and schedules quickly given inputs, but aligning them to institutional accreditation, student population needs, and specific technical/vocational competencies still requires human judgment and customization.:
Adoption barriers	claude-haiku-4-5-20251001	4/5	Instructional design for accredited postsecondary programs faces regulatory requirements (regional accreditation bodies, industry certifications), faculty governance in curriculum decisions, and institutional accountability for learning outcomes that create friction against full automation of this task.
Adoption barriers	claude-sonnet-5	2/5	No strict licensing barrier prevents using AI to draft outlines, though institutional approval processes and accreditation standards create some review friction.
Cost vs. human wage	claude-haiku-4-5-20251001	3/5	AI-assisted outline generation is inexpensive, but the output typically requires 30–50% educator effort to customize, validate, and integrate with institutional requirements, making the all-in cost competitive with but not cheaper than a teacher doing it directly.
Cost vs. human wage	claude-sonnet-5	4/5	Generating a first-draft outline or schedule via an LLM costs a fraction of a cent compared to the hours an instructor would spend, though review time still adds human cost.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While AI tools can generate template outlines and scheduling suggestions, no deployed product reliably produces complete, institution-ready instructional programs that meet accreditation and career-alignment requirements without substantial educator revision and validation.
Technical feasibility today	claude-sonnet-5	3/5	Products like ChatGPT and specialized ed-tech tools are used by instructors to draft course outlines and schedules today, but they are not deployed as reliable end-to-end systems without instructor review.

Administer oral, written, or performance tests to measure progress and to evaluate training effectiveness.

CI 34–50 · exposure 38 · augmentation 75 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Community colleges and technical institutes are piloting AI-assisted assessment platforms, particularly for written exams and online courses, but widespread adoption for performance testing remains limited. Early movers in online technical education adopt written-test automation; in-person, hands-on evaluation lags.
Sector adoption velocity	claude-sonnet-5	2/5	Postsecondary CTE programs are physical/hands-on and adopt AI more slowly than office-based education sectors, though some digital assessment tools are gaining traction.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can substantially assist instructors by auto-generating contextually appropriate test items, pre-scoring written responses, providing analytics on student progress, and flagging outliers—freeing instructors to focus human judgment on performance observation and remediation. This substantially raises instructor productivity in assessment workflows.
Augmentation potential	claude-sonnet-5	4/5	AI tools substantially help teachers draft test items, auto-grade written/objective components, and analyze performance trends, freeing time for hands-on skill evaluation.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can generate and score written/multiple-choice tests efficiently, administering performance tests and evaluating subjective skills (trades, technical craft) requires observing physical execution, safety compliance, and nuanced judgment that current AI cannot reliably do end-to-end. Oral testing via conversational AI is emerging but lacks consistent reliability for measuring competency in technical domains.
Task automatability	claude-sonnet-5	3/5	AI can generate and grade written tests and quizzes effectively, but performance-based assessment (hands-on skills in technical/vocational fields) and oral exams still require human judgment and physical observation.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Institutional accreditation standards and licensing bodies (e.g., trade certifications) typically require documented human evaluation of hands-on competency, creating friction. However, written and oral components are increasingly amenable to AI support without legal barriers, reducing but not eliminating friction.
Adoption barriers	claude-sonnet-5	3/5	Postsecondary accreditation and certification programs often require instructor-administered and verified assessments, especially for licensure-track technical skills, creating moderate institutional and regulatory friction.
Cost vs. human wage	claude-haiku-4-5-20251001	3/5	AI test generation and automated grading of written/quiz components can reduce costs, but the need for human oversight of performance assessment and occasional re-testing keeps the total cost per student comparable to a human instructor administering tests directly.
Cost vs. human wage	claude-sonnet-5	3/5	For written test creation/grading, AI is much cheaper, but performance test administration still needs an instructor present, keeping blended costs moderate rather than dramatically lower.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	AI-powered assessment platforms exist for written tests and some standardized metrics, but production systems for evaluating hands-on performance in vocational/technical education remain immature. Deployed solutions handle objective scoring only; subjective technical assessment (tool proficiency, safety awareness, workmanship) is not yet reliably automated.
Technical feasibility today	claude-sonnet-5	3/5	AI grading and quiz-generation tools are deployed in many LMS platforms, but performance testing of technical/trade skills and nuanced oral exam evaluation are not reliably automated in production.

Observe and evaluate students' work to determine progress, provide feedback, and make suggestions for improvement.

CI 30–35 · exposure 30 · augmentation 63 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Postsecondary education, particularly technical and career programs, has been slower to adopt AI grading compared to higher-tech sectors. Pilots exist but production-scale displacement of evaluation work remains limited, reflecting institutional conservatism and the importance of human judgment in credentialing.
Sector adoption velocity	claude-sonnet-5	2/5	Postsecondary CTE programs are slower adopters of AI evaluation tools compared to purely digital education sectors, given the hands-on nature of many trades.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can meaningfully assist instructors by auto-flagging common errors, generating preliminary feedback on technical work, and organizing performance data, allowing educators to focus on high-value individualized coaching and nuanced progress assessment. This augmentative use is already feasible with existing tools.
Augmentation potential	claude-sonnet-5	3/5	AI can assist by scoring quizzes, analyzing written reports, tracking progress data, and drafting feedback templates that instructors then customize and deliver.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can analyze student work artifacts (code, essays, designs) and flag obvious errors or provide generic feedback, it cannot reliably evaluate the full context of student progress, learning trajectory, or provide nuanced, individualized suggestions that meet the ≥50% time-saving bar. Human judgment on holistic progress remains central.
Task automatability	claude-sonnet-5	2/5	AI can grade written/coded assignments and give feedback but observing hands-on technical/vocational work (welding, culinary, automotive skills) requires physical presence and nuanced judgment AI cannot replicate end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Educational institutions have accreditation and accountability requirements that favor human instructor sign-off on grades and progress assessments. Organizational norms and student expectations also favor human evaluation, creating friction, though not absolute legal barriers to AI assistance.
Adoption barriers	claude-sonnet-5	3/5	Accreditation and certification standards in CTE programs often require instructor sign-off on competency assessments, creating moderate institutional and credentialing barriers to full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI grading tools have low per-instance costs but require setup, oversight, and human review of edge cases. The loaded cost of a postsecondary instructor is high, but integration and quality assurance overhead means AI does not yet achieve an order-of-magnitude cost advantage for the full evaluation task.
Cost vs. human wage	claude-sonnet-5	2/5	For digital coursework AI grading is cheap, but for practical skills evaluation it requires sensors/video analysis plus human oversight, making costs comparable to or exceeding instructor time for that portion.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Products exist (e.g., essay grading systems, code analysis tools) that can score or comment on discrete work products, but they operate with material limitations in interpreting intent, grading consistency, and handling diverse technical domains. These are deployed in some educational settings but do not yet replace human evaluation reliably across the range of postsecondary technical work.
Technical feasibility today	claude-sonnet-5	2/5	Products exist for grading essays, quizzes, and code, but no deployed system reliably evaluates hands-on technical/career skill demonstrations at scale in real classrooms.

Select and assemble books, materials, supplies, and equipment for training, courses, or projects.

CI 25–35 · exposure 25 · augmentation 50 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Postsecondary career/technical education remains relatively fragmented and conservative in digitization compared to corporate or financial sectors. Adoption of AI for logistics and procurement in this space is nascent; most institutions still rely on manual processes and human coordinators rather than integrated automation systems.
Sector adoption velocity	claude-sonnet-5	2/5	Postsecondary CTE programs are relatively slow to adopt AI tools for logistical/curricular tasks compared to fully digital, white-collar sectors.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can augment this task through intelligent inventory recommendation, cost comparison, supplier databases, and curriculum-linked material suggestions, meaningfully speeding up the selection phase. However, the human educator typically remains the decision-maker and must verify suitability for their specific courses and students, limiting augmentation to the planning side rather than end-to-end execution.
Augmentation potential	claude-sonnet-5	3/5	AI can meaningfully assist by suggesting textbooks, materials lists, and lesson-aligned resources, saving instructor time on the research portion of this task.
Task automatability	claude-haiku-4-5-20251001	2/5	Current AI can assist with selecting and organizing materials based on course syllabi and learning objectives, but the physical assembly and verification of equipment condition requires human judgment and hands-on work. The task involves domain expertise in what's pedagogically appropriate and logistically functional, which AI can partially automate through recommendations but not fully execute end-to-end.
Task automatability	claude-sonnet-5	2/5	AI can help identify and recommend materials/resources, but physically assembling equipment, supplies, and course kits for hands-on technical training requires human judgment and physical action that current AI cannot perform end-to-end.'
Adoption barriers	claude-haiku-4-5-20251001	4/5	Educational institutions have institutional norms favoring human curation of learning materials for quality assurance and pedagogical fit. Liability and accreditation concerns also create friction: an educator is typically accountable for material appropriateness, making pure automation difficult without formal sign-off and oversight remaining with humans.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement blocks AI assistance in resource selection, though institutional purchasing procedures and hands-on equipment needs create moderate friction.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI-assisted selection tools exist but remain supplementary; the core assembly and physical logistics work still require paid labor. The cost of integrating AI recommendation systems, oversight, and error-correction in selection errors would likely not be substantially cheaper than having an experienced educator or technician perform direct selection and assembly.
Cost vs. human wage	claude-sonnet-5	2/5	AI could cheaply assist with content recommendations, but the physical assembly and procurement aspects still require human labor, keeping overall cost savings modest.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed product reliably performs physical material assembly and equipment preparation at scale. AI can generate selection lists or suggest suppliers via inventory systems, but actually gathering, checking, and assembling materials for classroom readiness requires human presence and tactile verification that current systems cannot do.
Technical feasibility today	claude-sonnet-5	2/5	Recommendation and curation tools exist (e.g., for reading lists or curriculum suggestions) but no deployed product handles the full logistics of selecting and assembling physical materials and equipment for technical courses.

Participate in conferences, seminars, and training sessions to keep abreast of developments in the field, and integrate relevant information into training programs.

CI 25–35 · exposure 25 · augmentation 75 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Educational institutions, particularly in career/technical education, lag in AI adoption compared to information-intensive sectors. Conference and seminar participation remains largely manual, and automated curriculum integration is not yet standard practice in these settings.
Sector adoption velocity	claude-sonnet-5	2/5	Postsecondary CTE instruction is a moderate-adoption sector; while AI research tools are spreading in academia, the specific behavior of conference attendance and integration is not yet meaningfully automated or fast-adopting.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI tools can significantly assist educators by filtering conference proceedings, summarizing session notes, flagging emerging trends in their field, and identifying potential curriculum additions—enabling them to extract value from more content faster while maintaining human judgment over actual program changes.
Augmentation potential	claude-sonnet-5	4/5	AI tools can help teachers efficiently digest conference materials, summarize trends, generate curriculum update suggestions, and search literature, meaningfully boosting productivity even though the human still performs the core task.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can help summarize conference materials and identify relevant developments, the task fundamentally requires human judgment to decide what information is genuinely valuable for a specific program's curriculum and student population. Integration into training programs also requires pedagogical expertise and program-specific knowledge that current AI cannot reliably handle end-to-end.
Task automatability	claude-sonnet-5	2/5	AI can help summarize field developments and surface relevant literature, but attending conferences, networking, and judging what to integrate into curricula requires human presence and professional judgment that current systems cannot replace end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Professional educators are expected to maintain their own currency in their field as part of accreditation, institutional policy, and professional standards. Many institutions require documented professional development participation, creating a barrier to full automation of this function.
Adoption barriers	claude-sonnet-5	2/5	No licensing barrier prevents AI assistance, but institutional norms, professional development requirements, and accreditation standards often expect faculty personal engagement in these activities.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	The cost of AI tools for literature review and summarization, plus human oversight to validate and implement findings, likely approaches or exceeds what an educator currently spends on conference attendance and self-directed learning, especially when quality assurance is factored in.
Cost vs. human wage	claude-sonnet-5	2/5	AI can cheaply summarize content but cannot substitute for the human activity of attending and networking at conferences, so cost comparison mostly applies only to a partial support function, not the full task.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed product reliably performs the full task of staying current with a field and intelligently integrating new developments into a specific training program. AI can assist with research and summarization, but products lack the domain expertise and contextual understanding needed for reliable, independent execution.
Technical feasibility today	claude-sonnet-5	2/5	Some AI tools (research summarizers, conference transcript analyzers) exist and are used informally, but no deployed product actually 'attends' conferences or reliably curates and integrates field developments into training programs at scale.

Present lectures and conduct discussions to increase students' knowledge and competence using visual aids, such as graphs, charts, videotapes, and slides.

CI 25–34 · exposure 25 · augmentation 75 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Postsecondary education remains labor-intensive with slow digital transformation outside online-first institutions. Most career/technical programs prioritize hands-on instruction and direct student-instructor interaction, limiting AI adoption to content-support roles rather than full lecture automation.
Sector adoption velocity	claude-sonnet-5	2/5	Postsecondary CTE teaching is a relatively slow-adopting sector for full AI-driven instructional delivery, though supplementary AI tools (slide generators, video tools) see some pilot use.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can substantially assist instructors by auto-generating presentation materials, designing visual aids tailored to learning objectives, and drafting discussion prompts—freeing instructor time for deeper interaction and feedback while the human remains central to pedagogy.
Augmentation potential	claude-sonnet-5	4/5	AI substantially helps teachers prepare visual aids, generate discussion prompts, and create supplementary materials, meaningfully boosting productivity while the teacher still delivers and facilitates.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can generate visual aids and draft lecture content, the live presentation, discussion facilitation, and real-time responsiveness to student questions require human presence. No current system can replace the full interactive teaching loop end-to-end with 50% time savings at equal learning outcomes.
Task automatability	claude-sonnet-5	2/5	AI can generate lecture content and visual aids, but live presentation and interactive discussion facilitation with students requires human presence, adaptability, and real-time responsiveness that current AI cannot fully replicate end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Accreditation bodies, institutional policies, and regulatory frameworks (especially for career/technical programs with licensing pathways) typically require human instruction, assessment, and sign-off. Legal liability for educational quality and student outcomes also favors human accountability.
Adoption barriers	claude-sonnet-5	3/5	No formal licensing barrier prevents AI-assisted teaching, but institutional norms, accreditation expectations, and student preference for live instructors create moderate friction.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI tools for content generation and visual aid creation are cheap, but integration, customization for specific curricula, and the human instructor overhead remain substantial. The all-in cost per student outcome does not yet undercut an instructor's loaded wage.
Cost vs. human wage	claude-sonnet-5	3/5	AI tools for content/slide generation are cheap, but replicating an entire live lecture-and-discussion session with instructor-level engagement still requires human involvement, making the full task cost comparable rather than dramatically cheaper.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	AI systems can create slides and graphics, but no deployed product reliably conducts live lectures or genuine classroom discussions at scale. Chatbot tutors exist in narrow domains; they do not match the breadth and depth required for postsecondary career/technical education.
Technical feasibility today	claude-sonnet-5	2/5	AI-generated slides, video content, and even AI avatars for lectures exist, but no deployed product reliably conducts full classroom discussions with postsecondary students in production at scale.

Provide individualized instruction and tutorial or remedial instruction.

CI 25–34 · exposure 25 · augmentation 75 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Postsecondary institutions have adopted AI-assisted tutoring tools (supplementary learning platforms), but true replacement of individualized remedial instruction by human teachers remains rare; most pilots remain add-ons rather than substitutes.
Sector adoption velocity	claude-sonnet-5	2/5	Postsecondary CTE programs are physical/vocational in nature and have historically been slower to adopt AI tools compared to fully digital instructional sectors, with pilots more common than production-scale deployment.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can meaningfully assist instructors by generating personalized practice problems, identifying common misconceptions at scale, providing rapid feedback on student work, and flagging at-risk learners—substantially raising instructor productivity while the instructor retains pedagogical judgment and rapport-building.
Augmentation potential	claude-sonnet-5	4/5	AI tutoring systems and generative AI can meaningfully help identify student gaps, generate practice materials, and provide supplementary remedial content, boosting instructor productivity even though the instructor remains central for hands-on skills.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI tutoring systems can deliver content and basic explanations, truly individualized instruction requires assessing student misconceptions, emotional state, motivation, and adapting pedagogy in real time—tasks where AI falls short of 50% time savings at equal quality. Current AI tutors lack the dynamic responsiveness and human judgment needed for remedial work with struggling learners.
Task automatability	claude-sonnet-5	2/5	AI tutoring tools can deliver adaptive remedial content and practice problems, but hands-on technical/vocational instruction and personalized mentoring in postsecondary CTE contexts require physical demonstration, equipment access, and human judgment that current AI cannot fully replace.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Accreditation standards, institutional policy, and student protections often require human instructors to certify learning outcomes and provide direct feedback in postsecondary settings. Liability and accountability for remedial instruction also create strong pressure for human sign-off, limiting pure automation.
Adoption barriers	claude-sonnet-5	3/5	No licensing law mandates a human tutor for remedial instruction, but institutional expectations, accreditation standards, and the need for hands-on supervision in technical fields create moderate friction against pure AI substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI-based tutoring infrastructure has ongoing costs (content curation, model fine-tuning, human oversight for quality assurance) that, when divided across students, may rival or exceed the loaded cost of a postsecondary instructor providing small-group or individual remedial instruction, especially in lower-enrollment settings.
Cost vs. human wage	claude-sonnet-5	3/5	AI tutoring software can be cheap per interaction for conceptual content, but when factoring in the need for human oversight of hands-on skill remediation and integration into vocational programs, the overall cost advantage narrows to roughly comparable.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Tutoring products exist (e.g., Chegg, Khan Academy-style systems), but they operate at scale with broad curricula and cannot reliably replicate the depth of one-on-one remediation with tailored feedback that this task demands. Deployed systems show material limitations in handling individual learning blockers and gaps.
Technical feasibility today	claude-sonnet-5	2/5	AI tutoring products (e.g., adaptive learning platforms) are deployed for academic subjects like math or language, but reliable deployed systems for individualized technical/vocational skill remediation are narrow and not widespread in CTE settings.

Develop curricula and plan course content and methods of instruction.

CI 25–34 · exposure 25 · augmentation 75 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Educational institutions are conservative on curriculum automation; most adoption is pilot-stage (faculty experimenting with ChatGPT for drafts) rather than production-level displacement of curriculum development roles. Organizational inertia, union protections, and accreditation gatekeeping slow deep adoption in schools and community colleges.
Sector adoption velocity	claude-sonnet-5	2/5	Postsecondary education, especially vocational/technical sectors, has been slower than white-collar professional services to adopt AI tools into core curriculum design workflows.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI already meaningfully assists educators in curriculum planning: generating content ideas, drafting learning objectives, scaffolding lesson sequences, and providing feedback on course design. Many instructors use AI to accelerate content creation and explore pedagogical alternatives while retaining human judgment and customization.
Augmentation potential	claude-sonnet-5	4/5	AI tools are already useful for brainstorming course structures, generating draft materials, and suggesting instructional methods, meaningfully speeding up an instructor's planning process while they retain final control.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can generate draft outlines, learning objectives, and instructional content, developing effective curricula requires deep pedagogical judgment, understanding of student populations, compliance with accreditation standards, and iterative refinement that current AI cannot fully automate end-to-end. AI might handle 20–30% of the work (content synthesis, outline generation), but the core curriculum development remains heavily human-dependent.
Task automatability	claude-sonnet-5	2/5	AI can draft syllabi and lesson outlines but aligning curriculum to program accreditation standards, industry certification requirements, and specific student cohorts requires human judgment that current tools cannot fully replace end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Educational institutions face accreditation requirements (SACSCOC, program-specific boards), faculty governance standards, and legal/contractual obligations to employ qualified educators for curriculum design. Many jurisdictions and institutions explicitly require human credentials (degree, teaching experience) to sign off on curricula, creating hard barriers to full automation.
Adoption barriers	claude-sonnet-5	3/5	Postsecondary CTE programs often require instructor credentials and accreditation body approval of curriculum content, creating moderate institutional and regulatory friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI tools (ChatGPT, Claude, specialized education platforms) cost far less per unit output than hiring curriculum specialists, but the overhead of human review, revision, and validation keeps total cost-per-outcome comparable to or only slightly below a human educator's loaded wage.
Cost vs. human wage	claude-sonnet-5	3/5	AI drafting tools are cheap per use, but the human oversight, subject-matter validation, and iterative refinement needed keep overall cost roughly comparable to instructor-led curriculum design.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed product reliably develops complete, compliant curricula independently. AI writing tools exist and can assist with content drafting, but institutions still require human curriculum specialists to ensure coherence, alignment with standards, and pedagogical soundness. Production use is limited to narrow sub-tasks, not full curriculum development.
Technical feasibility today	claude-sonnet-5	2/5	Products like ChatGPT and specialized ed-tech tools can generate course outlines and materials, but no deployed product reliably produces complete, accreditation-ready CTE curricula without substantial instructor revision.

Advise students on course selection, career decisions, and other academic and vocational concerns.

CI 25–34 · exposure 25 · augmentation 75 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Postsecondary institutions have been slow to adopt AI for advising despite pilot projects; human advisors remain the default delivery model. While some campuses use AI for information provisioning, institutional inertia and faculty governance structures limit rapid deployment in the core advising function.
Sector adoption velocity	claude-sonnet-5	2/5	Postsecondary CTE institutions are generally slower adopters of AI advising tools compared to fast-moving sectors like finance or tech; pilots exist but deep production use is uncommon.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can substantially augment human advisors by surfacing relevant course prerequisites, labor market data, and graduation requirements in real time, reducing manual lookup and allowing advisors to focus on relationship-building and personalized guidance. This assistant role has demonstrated value in pilot environments.
Augmentation potential	claude-sonnet-5	4/5	AI can meaningfully assist advisors by surfacing course options, career pathway data, and drafting communications, significantly speeding up parts of the advising workflow while the human retains final judgment.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can provide generic course recommendations and career information retrieval, advising requires understanding individual student context, goals, constraints, and nuanced career-outcome tradeoffs. Meaningful advice demands human judgment about student capabilities and aspirations that current AI systems cannot reliably replicate, and oversight costs would be substantial.
Task automatability	claude-sonnet-5	2/5	Advising involves nuanced, personalized judgment about a student's specific circumstances, institutional requirements, and career trajectory that current AI cannot fully replicate end-to-end without significant human oversight.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Institutional policies typically require certified educators to provide official academic and vocational advising; many institutions have accreditation standards mandating human advisor sign-off on academic plans. Liability concerns around career guidance and institutional accountability create substantial friction against full automation.
Adoption barriers	claude-sonnet-5	3/5	No formal licensure requires a human to give this advice, but institutional policies, liability concerns for poor guidance, and student preference for human mentorship create moderate friction.
Cost vs. human wage	claude-haiku-4-5-20251001	3/5	AI chatbot deployment is inexpensive, but integration with institutional systems, monitoring for errors, and human review of outputs partially offsets savings. The cost comparison is roughly at parity when all integration and oversight overhead is included.
Cost vs. human wage	claude-sonnet-5	2/5	While AI chat tools are cheap per interaction, the need for human validation, relationship-building, and institutional knowledge means integration and oversight costs keep total cost closer to human-comparable for genuine advising quality.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Chatbots and career-matching tools exist but operate at surface level (matching keywords to databases). Production systems cannot reliably handle the ambiguity, conflicting priorities, and personalized guidance that effective academic advising requires; they function as supplementary information sources, not advisors.
Technical feasibility today	claude-sonnet-5	2/5	Chatbots and advising tools exist for basic course scheduling info, but no deployed product reliably handles the full scope of academic/career/vocational advising for real students at scale.

Determine training needs of students or workers.

CI 25–30 · exposure 25 · augmentation 63 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Career/technical education operates in primarily public institutional and union-protected settings with slow technology adoption; while some pilot uses of assessment tools exist, production deployment of AI-driven needs determination remains limited and cautious.
Sector adoption velocity	claude-sonnet-5	2/5	Postsecondary vocational education is a moderately slow-adopting sector, with AI tools used mainly in pilot programs rather than deep production integration.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can meaningfully assist by analyzing test results, suggesting common skill gaps, and generating baseline training options that instructors then refine and personalize; this augmentation is useful but not transformative given that human judgment remains essential.
Augmentation potential	claude-sonnet-5	4/5	AI can meaningfully assist by analyzing skills assessments, test results, or job market data to help teachers identify training gaps, though the teacher still makes final determinations.
Task automatability	claude-haiku-4-5-20251001	2/5	AI can assist with initial assessment of educational gaps and skill inventories through testing or skill surveys, but determining training needs requires nuanced judgment about individual learning contexts, career goals, and organizational constraints that demand human expertise and one-on-one interaction.
Task automatability	claude-sonnet-5	2/5	Assessing individual training needs requires contextual judgment about a student's background, goals, and workplace requirements that current AI cannot reliably synthesize end-to-end without heavy human input.'
Adoption barriers	claude-haiku-4-5-20251001	4/5	Strong barriers exist: educators are expected to hold credentials and licensure, institution accreditation often mandates qualified staff oversight of student assessment and advising, and there is legal/reputational liability if inappropriate training recommendations harm student outcomes or career trajectories.
Adoption barriers	claude-sonnet-5	3/5	No strict licensing requirement for needs assessment itself, but institutional accreditation, curriculum requirements, and reliance on instructor expertise create moderate organizational friction.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Comparable to human cost when accounting for setup, integration, and mandatory oversight by qualified instructors who must verify and contextualize recommendations before using them in actual student guidance.
Cost vs. human wage	claude-sonnet-5	2/5	AI diagnostic/assessment tools can be cheap to run but still require significant human oversight and interpretation, keeping all-in costs closer to comparable rather than dramatically cheaper.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While AI tools can run skill assessments and flag knowledge gaps, no mature deployed product reliably performs the full end-to-end task of needs determination in postsecondary career/technical settings; existing tools are narrow in scope and require significant human validation.
Technical feasibility today	claude-sonnet-5	2/5	Some adaptive learning and skills-gap-analysis tools exist, but they are narrow and not widely deployed as full replacements for instructor judgment in postsecondary CTE settings.

Integrate academic and vocational curricula so that students can obtain a variety of skills.

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

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Postsecondary education remains a laggard sector for production AI adoption. While some institutions pilot learning analytics and content platforms, curriculum redesign itself is still predominantly human-driven with minimal documented AI displacement.
Sector adoption velocity	claude-sonnet-5	2/5	Postsecondary vocational education is a slower-adopting sector with limited digitization of curriculum development processes compared to fields like finance or software.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can usefully assist educators by suggesting skill-alignment mappings, identifying content gaps, and organizing material cross-references, raising their efficiency in the design process while the educator retains decision-making authority over learning outcomes and integration strategy.
Augmentation potential	claude-sonnet-5	4/5	AI can meaningfully help teachers brainstorm curriculum connections, draft materials, and identify skill alignments, significantly speeding up parts of the integration process while the teacher retains final judgment.
Task automatability	claude-haiku-4-5-20251001	2/5	Curriculum integration requires understanding pedagogical goals, diverse student needs, and institutional constraints—tasks demanding human judgment and creativity. AI can assist with content organization and cross-referencing materials, but cannot independently redesign curricula to coherently blend academic and vocational elements at equal or superior quality.
Task automatability	claude-sonnet-5	2/5	Curriculum integration requires pedagogical judgment, alignment with institutional standards, and knowledge of student needs that AI can assist with but not fully execute end-to-end at equal quality.'
Adoption barriers	claude-haiku-4-5-20251001	4/5	Educational institutions have institutional inertia, accreditation requirements, and faculty governance that slow automation. Curriculum decisions involve stakeholder buy-in, regulatory compliance, and pedagogical accountability that legally and practically require human educator sign-off.
Adoption barriers	claude-sonnet-5	3/5	Accreditation standards, institutional approval processes, and instructor expertise requirements create moderate friction against full automation of curriculum design.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI tools for curriculum design are relatively new and often require significant customization and expert oversight, making their effective all-in cost comparable to or higher than a curriculum specialist's time investment.
Cost vs. human wage	claude-sonnet-5	2/5	Because human curriculum designers and instructors must still validate and adapt materials, AI assistance saves some cost but doesn't eliminate the substantial human labor and oversight needed.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No mature product reliably performs full curriculum integration across institutions. Tools exist for content management and learning analytics, but integrating two distinct educational frameworks requires contextual institutional knowledge and human oversight that deployed systems do not yet handle at scale.
Technical feasibility today	claude-sonnet-5	2/5	AI tools can suggest curriculum mappings or generate lesson content, but no deployed product reliably integrates academic and vocational curricula in production at postsecondary institutions today.

Arrange for lectures by experts in designated fields.

CI 11–35 · exposure 13 · augmentation 50 · importance 3.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Educational institutions adopt technology slowly, and speaker arrangement remains embedded in faculty autonomy and institutional culture; adoption of AI in this domain is in early pilot phases rather than production deployment.
Sector adoption velocity	claude-sonnet-5	2/5	Postsecondary CTE instruction is a moderately digitized sector with slow adoption of AI for administrative/relationship tasks like guest speaker arrangement.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can meaningfully assist by generating speaker lists, drafting contact templates, and tracking logistics, improving the teacher's efficiency without removing human judgment over speaker selection and institutional fit.
Augmentation potential	claude-sonnet-5	3/5	AI tools can help draft outreach emails, find potential speakers, and manage scheduling logistics, meaningfully assisting the instructor while they retain the relationship-building role.
Task automatability	claude-haiku-4-5-20251001	2/5	AI can assist with identifying potential expert speakers and drafting outreach emails, but the core task—negotiating availability, confirming logistics, and relationship management—requires human judgment and real-time communication that AI cannot reliably automate end-to-end.
Task automatability	claude-sonnet-5	1/5	Arranging guest lectures requires interpersonal outreach, negotiation, scheduling, and relationship management with external professionals, which AI cannot substantively perform end-to-end today.deps.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Educational institutions typically require human faculty to own speaker selection and institutional relationships for quality assurance, liability, and accreditation purposes; automation would face organizational resistance and potential policy constraints.
Adoption barriers	claude-sonnet-5	2/5	No licensing barrier, but institutional norms and the need for personal rapport with outside experts create moderate friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Current AI tools offer modest savings on research and initial outreach, but human oversight of speaker vetting, scheduling, and relationship maintenance remains necessary, limiting the cost advantage to a fraction of the total task.
Cost vs. human wage	claude-sonnet-5	2/5	AI could assist with drafting invitations or scheduling logistics cheaply, but the core relationship-building and coordination still requires human time, limiting cost savings.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While AI systems can generate speaker recommendations and draft communications, no deployed product reliably handles the full coordination workflow including scheduling conflicts, contract negotiation, and last-minute logistics that this task entails.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously identifies, contacts, negotiates with, and schedules external expert speakers on behalf of an instructor.

Conduct on-the-job training classes or training sessions to teach and demonstrate principles, techniques, procedures, or methods of designated subjects.

CI 16–25 · exposure 20 · augmentation 50 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Educational institutions, particularly in technical/career programs, have historically slow adoption of emerging automation due to funding constraints, accreditation rigidity, union considerations, and cultural attachment to human instruction. While online and hybrid training tools are spreading, autonomous on-the-job training displacement remains minimal.
Sector adoption velocity	claude-sonnet-5	2/5	Vocational and technical education sectors adopt AI more slowly than white-collar/information sectors, with hands-on training remaining largely analog and instructor-led.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can meaningfully assist instructors by generating course materials, creating simulations or practice modules, providing automated feedback on written assignments, and reducing administrative load. However, the core live teaching and demonstration remain human-centered, with AI playing a supporting role.
Augmentation potential	claude-sonnet-5	3/5	AI can help design curricula, create instructional materials, simulate scenarios, or provide supplementary digital demonstrations, aiding instructors without replacing the hands-on teaching itself.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can generate training materials and deliver some content, conducting live on-the-job training requires real-time demonstration, adaptation to student questions, hands-on correction of techniques, and classroom management—tasks that demand human presence and responsiveness. Current AI systems cannot meaningfully replace the interactive, embodied teaching required for technical skill acquisition.
Task automatability	claude-sonnet-5	2/5	This task involves in-person, hands-on demonstration of technical/vocational skills and physical supervision of trainees, which current AI cannot perform end-to-end.dummy Physical demonstration and real-time hands-on correction remain human-dependent.time.dummy
Adoption barriers	claude-haiku-4-5-20251001	4/5	Educational licensing, accreditation standards, institutional liability for student outcomes, and labor agreements in many jurisdictions establish strong protections. Postsecondary institutions have regulatory and accreditation pressure to employ credentialed instructors who sign off on student competency.
Adoption barriers	claude-sonnet-5	4/5	Postsecondary CTE teaching often requires credentialing, accreditation standards, and hands-on supervision for safety and liability reasons (e.g., trade certifications), creating strong barriers to full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI training platforms and content creation tools incur significant setup, customization, and ongoing maintenance costs relative to their current limited scope. The cost per delivered training session, including human oversight, remains comparable to or higher than direct instructor wages, especially when quality and safety standards are factored in.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute delivering this task, so cost comparison favors the human instructor by default since AI cannot perform the physical/on-site component.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Some AI tools can support training content creation and delivery (e.g., video tutorials, simulations), but no deployed product reliably conducts end-to-end on-the-job training sessions with the adaptability, safety oversight, and hands-on demonstration expected in postsecondary technical education. Production systems remain primarily assistive rather than autonomous.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product conducts physical on-the-job training sessions autonomously; AI tools exist only as supplementary content generators, not as replacements for live instruction.

Acquire, maintain, and repair laboratory equipment and tools.

CI 5–19 · exposure 8 · augmentation 25 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Adoption of automation for this task remains extremely limited across postsecondary institutions. Educational organizations have not deployed AI or robotics solutions for equipment maintenance at scale, and human technicians remain the standard in virtually all laboratory settings.
Sector adoption velocity	claude-sonnet-5	1/5	Postsecondary CTE instructional and facilities-maintenance work is a low-digitization, physical-labor-heavy domain with minimal AI/robotics adoption in production settings.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could modestly assist by predicting equipment failure via sensor data, maintaining digital maintenance logs, or recommending repair procedures, but the core physical and diagnostic work remains human-dependent. The augmentation value is limited compared to tasks where AI can generate or process large information volumes.
Augmentation potential	claude-sonnet-5	2/5	AI can assist with procurement research, inventory tracking, or troubleshooting guides/documentation, but offers little help with the physical acquisition and repair itself.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI could assist with maintenance scheduling and identifying equipment failures through diagnostic data, the hands-on repair and physical maintenance of laboratory equipment requires manual dexterity, spatial reasoning, and direct interaction with hardware that current AI systems cannot perform end-to-end. Acquisition and some record-keeping could be partially automated, but core repair work remains non-automatable.
Task automatability	claude-sonnet-5	1/5	This is a hands-on physical task involving sourcing, purchasing, and physically repairing equipment; current AI cannot manipulate tools or perform physical maintenance.6/lack of embodiment prevents automation.6
Adoption barriers	claude-haiku-4-5-20251001	4/5	Strong barriers exist due to safety and liability concerns: using non-certified systems to maintain lab equipment could damage expensive instruments, contaminate samples, or create safety hazards. Educational institutions typically require qualified personnel for equipment stewardship, and there are organizational and regulatory expectations around proper maintenance records and trained oversight.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement per se, but physical safety, liability for faulty equipment, and specialized technical knowledge create moderate organizational friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Current AI tools (software-only) cannot perform the physical labor of acquiring, maintaining, or repairing equipment, making direct cost comparison misleading. Any robotic solution capable of such work would be prohibitively expensive compared to paying a technician.
Cost vs. human wage	claude-sonnet-5	1/5	AI cannot perform the physical repair/maintenance work at all, so there is no viable cost comparison—human labor remains necessary and thus cheaper by default.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed products can reliably perform physical equipment maintenance, repair, or acquisition independently. This task fundamentally requires robotics with fine motor control and domain expertise that do not exist in production systems for general laboratory equipment servicing.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product performs physical acquisition or repair of lab equipment; at best software can help track inventory or generate purchase orders, not repair.

Supervise and monitor students' use of tools and equipment.

CI 0–23 · exposure 13 · augmentation 38 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Postsecondary technical education remains highly traditional and instructor-centered, with slow digital adoption; safety and accreditation concerns make institutions reluctant to experiment with autonomous monitoring, and no evidence suggests rapid uptake of AI supervision in this sector.
Sector adoption velocity	claude-sonnet-5	1/5	Postsecondary CTE instruction involves hands-on, physical environments with low AI adoption for direct safety supervision tasks.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI-powered camera systems or sensor alerts could augment an instructor by flagging potential safety issues or unusual equipment handling patterns, allowing them to focus attention more efficiently, though the human instructor remains essential for judgment and intervention.
Augmentation potential	claude-sonnet-5	2/5	AI could support scheduling, checklists, or sensor-based alerts for equipment status, but it offers minimal direct assistance to the core supervisory act itself.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI could monitor for basic safety violations through computer vision of tools and equipment, the task requires judgment about proper technique, individual student readiness, and intervention timing that depends heavily on contextual, embodied understanding of what safe and correct use looks like in real time.
Task automatability	claude-sonnet-5	1/5	This requires real-time physical presence to observe students handling tools/equipment for safety and correct technique, which current AI cannot perform end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Strong legal and liability barriers exist: instructors have a duty of care and institutional responsibility for student safety that cannot be fully delegated to AI, and many jurisdictions may require certified personnel to physically supervise hands-on technical training to mitigate injury and equipment-damage liability.
Adoption barriers	claude-sonnet-5	5/5	Safety liability, insurance requirements, and often legal mandates require a qualified human instructor to directly supervise equipment use, especially with potentially dangerous tools.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	The cost of deploying robust vision systems, infrastructure integration, and required human oversight to ensure safety would be comparable to or exceed the cost of a teaching assistant or instructor providing live supervision in a classroom or shop setting.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute for in-person supervision, so any comparison favors the human instructor by default.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Computer vision systems can detect some unsafe postures or unauthorized equipment use, but deployed solutions lack the real-time responsiveness, contextual judgment, and liability coverage needed to replace human supervision in hands-on technical education environments where injury risk is significant.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product physically supervises students using shop or lab equipment in real time; this remains a research/robotics-stage capability at best.

Supervise independent or group projects, field placements, laboratory work, or other training.

CI 0–13 · exposure 5 · augmentation 50 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Educational institutions are slow to adopt AI for safety-critical supervision roles due to liability concerns and accreditation requirements. Adoption remains largely limited to optional attendance tracking and assignment grading, not active supervision of training activities.
Sector adoption velocity	claude-sonnet-5	1/5	Postsecondary CTE instruction involving hands-on supervision is a low-digitization, physical-presence-dependent context with minimal AI adoption for this specific function.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can assist instructors by flagging incomplete submissions, tracking group dynamics via logged interactions, or providing automated grading rubrics, allowing instructors to focus attention on real-time mentoring and safety. However, the augmentation is partial and mostly back-office rather than transformative for the supervision task itself.
Augmentation potential	claude-sonnet-5	3/5	AI can help with scheduling, tracking progress, generating rubrics, or flagging safety issues, but doesn't materially transform the core supervisory task itself.
Task automatability	claude-haiku-4-5-20251001	1/5	Supervising projects and training requires real-time observation of student behavior, safety assessment, individualized feedback, and adaptive intervention—activities that demand human judgment in dynamic environments. Current AI cannot reliably monitor multiple students simultaneously or ensure safety compliance in physical labs or field settings.
Task automatability	claude-sonnet-5	1/5	Direct supervision of hands-on labs, field placements, and group projects requires physical presence, safety oversight, and real-time judgment that current AI cannot perform end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Educational institutions face legal and safety liability for student welfare during lab work and field placements; many jurisdictions mandate that a qualified instructor supervise hands-on training. Accreditation bodies and regulatory frameworks (OSHA, etc.) often require credentialed human presence, creating hard barriers to full automation.
Adoption barriers	claude-sonnet-5	4/5	Safety liability, accreditation requirements, and the need for a qualified instructor physically present for labs/field placements create strong structural barriers to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	AI surveillance or monitoring tools carry significant infrastructure, integration, and liability costs, and they still require human oversight to act on alerts. The total cost easily exceeds the wage of an instructor present in the room.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute performing this supervisory function, so cost comparison favors the human by default.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While AI can log attendance, grade submissions, or flag unusual patterns in project outputs, no deployed product reliably supervises laboratory safety, adjusts instruction mid-activity, or provides the real-time behavioral feedback that this task inherently requires. Some learning management systems offer limited monitoring, but genuine supervision requires human presence.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product supervises physical training, labs, or field placements autonomously; this remains firmly in the human domain.

Serve on faculty and school committees concerned with budgeting, curriculum revision, and course and diploma requirements.

CI 0–7 · exposure 0 · augmentation 38 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Educational institutions have not adopted AI for faculty committee membership or governance roles, nor is this plausible given legal and accreditation structures. Adoption remains zero across the sector.
Sector adoption velocity	claude-sonnet-5	2/5	Higher education governance and administrative committee work is slow to adopt AI substitution, though AI note-taking/summarization tools are creeping in.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist by analyzing budget data, summarizing curriculum trends, or drafting policy language, but the core deliberative and decision-making work must remain with human faculty who bear institutional responsibility and possess democratic legitimacy.
Augmentation potential	claude-sonnet-5	3/5	AI can help draft budget analyses, summarize curriculum documents, or prepare meeting materials, meaningfully aiding preparation even though the deliberative task itself remains human-led.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires judgment about institutional policy, stakeholder consensus-building, and decision-making authority that cannot be delegated to AI. Current systems lack the contextual understanding of organizational politics, budget constraints, and accreditation requirements needed to serve meaningfully on such committees.
Task automatability	claude-sonnet-5	1/5	This requires interpersonal deliberation, institutional judgment, negotiation, and representing stakeholder interests in live committee settings—AI cannot perform this end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Strong regulatory and governance barriers exist: faculty committee membership typically requires employment status, tenure considerations, and legal responsibility for institutional decisions. Educational accreditation bodies require human sign-off on curriculum and budgeting decisions.
Adoption barriers	claude-sonnet-5	4/5	Committee membership typically requires institutional standing, faculty status, and governance rules that inherently require a human role-holder, though not a formal license.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	AI cannot perform this task at all, so cost comparison is moot. The task inherently requires a human with institutional standing and fiduciary responsibility.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI substitute performing this role, so cost comparison favors the human by default.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI product can independently participate in faculty committees, make budgeting decisions, or set curriculum requirements. These tasks require institutional authority, legal responsibility, and human accountability that AI systems cannot currently assume.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product substitutes for a human committee member; AI at best supports document drafting or scheduling around such meetings.

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.