Family and Consumer Sciences Teachers, Postsecondary
25-1192.00Median wage $75,870/yr2,770 employed (US)Rank #292 of 923 scored · top 32% by substitution
Teach courses in childcare, family relations, finance, nutrition, and related subjects pertaining to home management. Includes both teachers primarily engaged in teaching and those who do a combination of teaching and research.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution34
Exposure31
Augmentation63
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
23 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
13%
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%32
panel mean rating 2.3/5 → substitution pressure 32/100
Technical feasibility todayw 20%30
panel mean rating 2.2/5 → substitution pressure 30/100
Cost vs. human wagew 15%40
panel mean rating 2.6/5 → substitution pressure 40/100
Adoption barriersw 20%inverted — strong barriers lower the score41
panel mean rating 3.4/5 (barrier strength) → substitution pressure 41/100
Sector adoption velocityw 10%27
panel mean rating 2.1/5 → substitution pressure 27/100
Task breakdown (23 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Maintain student attendance records, grades, and other required records.
94CI 92–95 · exposure 100 · augmentation 75 · importance 4.3/5 · click for rater detail
Maintain student attendance records, grades, and other required records.
94| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Educational institutions have widely adopted LMS platforms and digital record-keeping tools over the past decade; this task is among the earliest and most penetrated automation opportunities in postsecondary education. |
| Sector adoption velocity | claude-sonnet-5 | 5/5 | Higher education has near-universal adoption of digital LMS/SIS platforms for attendance and gradebook functions, representing mature, fast-adopted administrative technology. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted record management tools significantly augment instructor productivity by automating data entry, flagging attendance patterns, and organizing grades for reporting, allowing instructors to focus on teaching rather than administrative overhead. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Automated systems already significantly reduce instructor burden in maintaining these records while the instructor still reviews and finalizes grades. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Maintaining attendance records, grades, and other required records is entirely routine data entry and retrieval; current AI systems can extract, organize, and populate institutional databases with high accuracy, easily achieving >50% time savings versus manual record-keeping. |
| Task automatability | claude-sonnet-5 | 5/5 | Attendance and gradebook management is highly structured, rule-based data entry and calculation, fully handled by existing LMS/SIS software with automated workflows. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While educational institutions have adoption policies and some institutions require human review of final records, there are no hard legal barriers preventing AI from performing the core data management work; institutional inertia is the primary friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Some FERPA/institutional policy oversight exists requiring instructor verification of final grades, but the record-keeping mechanics themselves face minimal legal or licensing barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated record-keeping via LMS or AI agents costs a fraction of the human labor required to manually enter, organize, and maintain records; the cost differential is orders of magnitude in favor of automation. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Automated record-keeping software costs a fraction of the instructor time it would take to manually maintain these records, especially at institutional scale. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Mature products like Learning Management Systems (Canvas, Blackboard, Brightspace) and AI-assisted data tools already reliably perform grade tracking and attendance logging at scale in thousands of educational institutions today. |
| Technical feasibility today | claude-sonnet-5 | 5/5 | Learning management systems (Canvas, Blackboard, PowerSchool, etc.) already perform automated attendance tracking, grade calculation, and record-keeping reliably at scale in production. |
Prepare course materials, such as syllabi, homework assignments, and handouts.
79CI 76–81 · exposure 75 · augmentation 100 · importance 4.5/5 · click for rater detail
Prepare course materials, such as syllabi, homework assignments, and handouts.
79| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Adoption is active among early-adopter faculty and some institutions, but still emerging and uneven across postsecondary education. Many departments and instructors remain cautious about AI-generated content for formal course delivery, placing adoption in the pilot-to-early-production range rather than mainstream. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Higher education is adopting generative AI for content creation at a moderate pace, with growing use among faculty but still uneven institutional policies and inconsistent uptake across departments. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI substantially augments instructors' productivity in generating, iterating, and personalizing course materials without removing the instructor from the loop. Teachers use AI to draft, then refine content for their specific students and learning outcomes, dramatically accelerating material preparation. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI strongly augments this task by rapidly generating drafts, formatting, and rephrasing content that instructors then refine, saving significant preparation time while keeping the instructor in control of final content. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI can generate syllabi, homework assignments, and handouts with minimal manual editing, meeting the 50% time-saving threshold. Generative models can produce pedagogically sound materials from topic descriptions or existing examples, though instructors typically verify alignment with learning objectives and institutional standards. |
| Task automatability | claude-sonnet-5 | 4/5 | LLMs can draft syllabi, homework assignments, and handouts from a course description or learning objectives with substantial time savings, though instructor review and customization to institutional templates remain necessary. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No legal or licensing requirement mandates a human create course materials; institutions may have policies requiring instructor review, but these are internal controls rather than hard regulatory barriers. Adoption is primarily limited by instructor preference and institutional culture rather than legal restriction. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, regulatory, or liability barrier prevents using AI-assisted drafting for course materials; it's a routine administrative/preparation task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | The inference cost for generating a syllabus or assignment set is negligible (dollars or cents per task), vastly cheaper than the hours a faculty member would spend composing from scratch, yielding at least an order-of-magnitude cost reduction. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Generating draft course materials via an LLM costs cents versus the instructor time (often hours) it would otherwise take, an order-of-magnitude cost reduction. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (ChatGPT, Claude, educational AI platforms) demonstrably perform this task reliably in production; many educators already use them for draft materials. Performance is mature enough for routine use, though human review for accuracy and pedagogical fit remains standard practice. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Widely deployed tools (ChatGPT, Copilot, dedicated ed-tech products) are routinely used by postsecondary instructors to generate syllabi and handouts today, though quality varies and outputs need editing. |
Compile bibliographies of specialized materials for outside reading assignments.
79CI 76–81 · exposure 75 · augmentation 100 · importance 3.5/5 · click for rater detail
Compile bibliographies of specialized materials for outside reading assignments.
79| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Academic institutions are beginning to adopt AI for library support and syllabus generation, but adoption remains uneven. Postsecondary adoption is moderate—pilots and interest are visible, but widespread replacement is not yet standard practice. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Higher education broadly adopts AI research tools, but postsecondary vocational/FCS instruction is not a leading-edge adopter segment, so uptake is moderate. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically accelerates the discovery and formatting phases of bibliography compilation, allowing instructors to curate far more diverse or specialized reading lists in the same time. Instructors remain in control of pedagogical choices while AI handles the labor-intensive search and formatting work. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up literature search and organization while the instructor retains final judgment on suitability and quality of materials. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI systems can effectively search academic and library databases, identify relevant sources, format citations, and compile bibliographies with minimal human oversight. The task is largely algorithmic—locate materials matching topic/criteria and format them—which current systems handle reliably, though verification of source appropriateness may require human review. |
| Task automatability | claude-sonnet-5 | 4/5 | AI can search, identify, and format relevant readings and citations quickly, and current LLMs with search/tool access can compile topical bibliographies with only light human review. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal legal or regulatory barriers exist. Library access and academic freedom norms favor AI-assisted curation. Slight friction comes from instructor preference to hand-select readings for pedagogical intent, but no hard requirement that a human must perform this task. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, liability, or regulatory barriers prevent using AI tools to assemble reading lists; it's a low-stakes administrative task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-assisted bibliography compilation costs pennies per assignment (inference on search queries, database calls, formatting). A human instructor or librarian compiling equivalent specialized bibliographies represents $25–40/hour labor, making AI at least 100× cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | AI-assisted bibliography compilation costs a fraction of the instructor time needed to manually search and curate reading lists. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature deployed products (academic AI search tools, citation managers like Zotero+AI plugins, library systems with AI indexing) perform bibliography compilation reliably in production. Some error rates exist in source relevance filtering, but the core functionality is production-ready. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Deployed tools (reference managers with AI search, academic search engines, LLM-based research assistants) reliably generate bibliographies today, though occasional citation inaccuracies require verification. |
Write grant proposals to procure external research funding.
60CI 52–67 · exposure 58 · augmentation 88 · importance 3.7/5 · click for rater detail
Write grant proposals to procure external research funding.
60| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Academic institutions and research organizations are experimenting with AI for proposal drafting, with growing mentions in writing guides and researcher communities. Adoption is common in pilots and informal use but not yet deeply embedded in institutional workflows or funding office standard practice, reflecting middling production deployment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Academic grant writing is a niche, slow-moving process; while individual faculty may use AI writing aids, institutional and funder norms around AI-assisted proposals are still cautious and adoption is uneven. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI significantly augments researcher productivity by generating structure, synthesizing literature citations, drafting methods sections, and creating multiple proposal variants for comparison—all while the researcher retains critical judgment on intellectual merit, accuracy, and strategic framing. This is a paradigmatic case of high-value assistance within human-led work. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools meaningfully speed up drafting, editing, formatting, and literature synthesis for grant proposals, letting faculty focus on strategy and content while AI handles routine writing tasks. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Writing grant proposals involves templated structure, boilerplate sections, literature synthesis, and budget justification—all areas where LLMs excel. Current AI can draft complete proposals with substantial time savings (>50%), though human researchers must still validate claims, interpret findings, and provide intellectual direction. The task has repeatable patterns that AI handles well, though final approval requires human expertise. |
| Task automatability | claude-sonnet-5 | 3/5 | LLMs can draft substantial portions of grant proposals (background, literature framing, boilerplate sections) given inputs, saving significant drafting time, but crafting a competitive, fundable narrative with specific aims, budget justification, and institutional alignment still requires substantial human expertise and iteration. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No legal requirement mandates a human author for grant proposals, and most institutions do not prohibit AI assistance in writing. Barriers are primarily organizational norms and funder reputation concerns rather than regulatory or licensing constraints; adoption friction exists but is not structural. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates human authorship, but funding agencies expect PI accountability, original intellectual contribution, and institutional sign-off, creating some friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | A draft proposal from an LLM costs under $5 in inference, while hiring a professional grant writer or allocating researcher time (loaded cost $50–150/hour for 20–40 hours) typically runs $1,000–6,000. AI is substantially cheaper for initial drafting, though humans must still refine and validate the output. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting tools are cheap per use, but the overall proposal-writing cost is dominated by expert time for strategy, data, and review, so total cost savings versus a faculty member's labor are moderate rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI writing assistants (ChatGPT, Claude, specialized research tools) are used in production by researchers for proposal drafting, but success rates vary significantly by funding body, discipline, and proposal complexity. Systems perform reliably on structure and language but less reliably on technical accuracy and funder-specific compliance, requiring material human oversight. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like ChatGPT, Grammarly, and specialized grant-writing assistants are used today to draft and edit proposals, but no deployed system reliably produces submission-ready, funded proposals without heavy human revision. |
Compile, administer, and grade examinations, or assign this work to others.
59CI 51–66 · exposure 55 · augmentation 75 · importance 4.4/5 · click for rater detail
Compile, administer, and grade examinations, or assign this work to others.
59| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Postsecondary institutions have digitized and are actively adopting LMS-integrated AI grading and exam tools. Higher education is a high-digitization sector with measurable production use of automated grading and exam administration. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adoption of AI grading/exam tools is uneven and slower than in tech-forward sectors, with many postsecondary institutions still in pilot or policy-development stages. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assists instructors by rapidly grading objective items, flagging outlier responses, suggesting feedback, and freeing time for higher-level pedagogical work. Instructors remain in the loop for final decisions, especially on subjective items. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly aids instructors in drafting question banks, creating rubrics, and pre-grading assignments, substantially speeding up the overall workflow while the instructor retains final review. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can compile and grade objective exams with high reliability and can automate administrative workflows, but designing valid assessments and grading subjective work (essays, projects) still requires significant human judgment. Perhaps 50-60% of exam administration is automatable end-to-end. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can generate exam questions and grade objective/short-answer responses effectively, but compiling exams aligned to specific course objectives and grading nuanced written work (e.g., recipes, design projects, essays) still requires human judgment for full task completion. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Institutional policies, accreditation requirements, and faculty preferences for human oversight of assessment create friction, though there is no strict legal requirement that a licensed educator must personally grade every exam. Integration into institutional workflows adds friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates human grading, though academic integrity and grading appeals processes create some institutional friction favoring instructor oversight. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI exam administration (LMS + grading tools) costs far less than the instructor labor it displaces for routine compilation, distribution, and objective grading, easily achieving an order-of-magnitude cost advantage on the automatable portion. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-based exam generation and grading tools are inexpensive per use compared to faculty or TA time, especially for large sections, though integration and oversight add some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products (Learning Management Systems, AI grading tools) reliably handle exam creation, distribution, and objective grading in production. Subjective grading assistance exists but with higher error rates and limited deployment at scale in postsecondary settings. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like AI-assisted quiz generators and automated grading tools (e.g., Gradescope, LMS auto-graders) are deployed in higher ed, but they handle multiple-choice/structured formats well while struggling with rubric-based subjective grading typical in consumer sciences coursework. |
Keep abreast of developments in the field by reading current literature, talking with colleagues, and participating in professional conferences.
48CI 46–50 · exposure 34 · augmentation 75 · importance 4.5/5 · click for rater detail
Keep abreast of developments in the field by reading current literature, talking with colleagues, and participating in professional conferences.
48| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Educational institutions are gradually adopting AI-assisted research monitoring and knowledge management tools, but adoption remains in the early-to-pilot stage. Conference attendance and collegial networks remain highly human-centric with limited AI integration in practice. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Higher education faculty increasingly use AI tools for literature review and research assistance, though full integration into professional development practices is still emerging. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially augment this task by automating journal alerts, summarizing literature, and identifying key trends, freeing the educator to focus on deeper reading and meaningful collegial discussion. The human stays in control of interpretation and professional judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools significantly speed up literature discovery, summarization, and trend-spotting, meaningfully augmenting a teacher's ability to stay current even though human interaction components remain unassisted. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can help surface and summarize current literature through automated information filtering and document analysis, but cannot authentically participate in colleague conversations or professional conferences, which require real-time presence and genuine interpersonal engagement. Only a partial workflow can be automated without human involvement. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help surface and summarize literature, but the actual ongoing professional engagement, networking, and conference participation require human presence and judgment that cannot be fully automated end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few formal barriers prevent educators from using AI tools to monitor the field. Professional conferences and colleague networks have social and cultural weight but no regulatory requirement to exclude AI-assisted participation. Organizational culture may prefer human presence, but this is weak friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier prevents AI assistance, though professional norms and accreditation expectations for postsecondary faculty to personally engage in scholarly community create some soft friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Literature aggregation and summarization tools are inexpensive relative to the time a faculty member would spend manually reading journals and synthesizing trends. However, the human still needs to attend conferences and engage with colleagues, so full substitution is impossible. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI literature-scanning tools are cheap relative to a teacher's time spent reading, but the task also includes activities (conferences, conversations) with no AI cost offset, keeping overall ratio moderate. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist for literature monitoring (RSS feeds, AI-powered research aggregators, journal alerts) and can generate summaries of papers, but reliable synthesis across multiple modalities (reading, conferencing, collegial discussion) and validation of quality remains limited. Systems perform narrowly on the reading component. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | AI research assistants and summarization tools are deployed and reliably help scan/synthesize literature, but they don't replace colleague discussion or conference attendance which are core parts of this task. |
Participate in student recruitment, registration, and placement activities.
46CI 30–62 · exposure 45 · augmentation 63 · importance 3.9/5 · click for rater detail
Participate in student recruitment, registration, and placement activities.
46| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Higher-ed institutions are adopting enrollment management and CRM systems at a moderate pace, with pilots common in recruitment automation, but full end-to-end AI-driven placement remains nascent in most postsecondary contexts compared to corporate recruiting sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education institutions are slow, uneven adopters of AI-driven recruitment tools relative to sectors like finance or tech, with pilots more common than mature deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments these tasks by automating initial outreach, organizing applicant data, suggesting placement matches, and managing follow-up workflows, enabling teachers and advisors to focus on relationship-building and personalized guidance while staying in the loop. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI chatbots, CRM analytics, and automated communications can meaningfully assist with lead generation, initial screening, and administrative aspects of recruitment and registration. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can automate substantial portions of recruitment (email campaigns, student outreach), registration (form processing, data entry), and placement tracking (database management, job matching), with existing systems achieving significant time savings. However, the personal relationship-building and final placement decisions typically require human judgment, preventing 100% automation. |
| Task automatability | claude-sonnet-5 | 2/5 | Parts like drafting recruitment materials or scheduling can be AI-assisted, but recruitment/placement involves relationship-building, interviews, and judgment calls that current AI cannot fully replace end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Moderate barriers exist: educational institutions have established processes and staff roles dedicated to these functions, student data privacy regulations (FERPA) require careful handling, and some stakeholders prefer human contact for recruitment and placement guidance, though no hard legal requirement mandates human-only execution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement, but institutional policies, personalized advising expectations, and accreditation-related student services create moderate organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven recruitment and registration tools (automated emails, chatbots, form processing) cost substantially less than hiring dedicated staff for these administrative tasks, though oversight and human verification add some overhead. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Some AI-driven outreach tools reduce cost for routine communications, but the overall task still requires substantial human labor for interviews, advising, and relationship management, keeping costs comparable to human-only processes. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed products (CRM systems, enrollment platforms, applicant tracking systems) handle parts of this workflow reliably, but integration across recruitment-to-placement remains fragmented and most institutions still rely heavily on manual follow-up and human decision-making for final placement. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CRM and enrollment-management software with AI chatbots exist and handle inquiries or initial screening, but comprehensive recruitment/placement decision-making by AI is not deployed reliably in production for faculty-level involvement. |
Initiate, facilitate, and moderate classroom discussions.
41CI 25–56 · exposure 49 · augmentation 75 · importance 4.5/5 · click for rater detail
Initiate, facilitate, and moderate classroom discussions.
41| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Education technology adoption is slow; while AI writing tools see some use in course prep, live classroom discussion facilitation remains heavily human-centric with minimal evidence of production-scale AI agent deployment in postsecondary settings. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education has been slower and more uneven in adopting AI for live instructional interaction compared to fields like finance or customer service, with most current use limited to asynchronous or supplementary tools. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments this task by generating discussion prompts, identifying patterns across student contributions, drafting summary notes, and suggesting follow-up questions in real-time, allowing instructors to focus on dynamic facilitation and emotional engagement rather than scripting or recall. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully help instructors prepare discussion questions, summarize prior threads, and suggest follow-up prompts, enhancing but not replacing the live facilitation role. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Language models can generate discussion prompts, synthesize participant contributions, identify key themes, and suggest follow-up questions in real-time, enabling 50%+ time savings on preparation and facilitation with comparable pedagogical quality. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can generate discussion prompts and even simulate Q&A, but live facilitation requires reading a room, managing dynamics, and improvising in real time, which current systems cannot reliably do end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: teaching credentials, accreditation requirements that mandate instructor presence and live interaction, institutional policies requiring faculty involvement, and inherent human-contact requirements in accredited postsecondary education make full substitution legally and organizationally unfeasible. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing barrier prevents AI-assisted discussion tools, but pedagogical norms, accreditation expectations, and student preference for human interaction create real friction against full replacement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI augmentation for discussion prep and synthesis is inexpensive, but replacing the live teacher who moderates, manages participation equity, and responds to real-time social cues would still require human oversight, keeping total all-in costs non-trivial relative to a teacher's per-session labor. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI tools for generating discussion questions are cheap, actual facilitation still requires a human instructor present, so total cost savings versus the teacher's wage are minimal. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While chatbots can draft discussion materials and provide moderation suggestions, current systems cannot reliably manage the nuanced interpersonal dynamics, spontaneous emotional regulation, and real-time judgment required to run a live classroom discussion with consistent pedagogical effectiveness. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | There are AI chatbots and discussion-board moderation tools used in some online courses, but no mature product independently runs live classroom discussion facilitation at scale in postsecondary settings. |
Evaluate and grade students' class work, laboratory work, projects, assignments, and papers.
40CI 29–51 · exposure 38 · augmentation 63 · importance 4.8/5 · click for rater detail
Evaluate and grade students' class work, laboratory work, projects, assignments, and papers.
40| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Postsecondary education institutions adopt AI slowly, with most faculty still using manual grading or basic spreadsheet tools. Pilot projects with AI grading assistants exist, but production-scale displacement remains rare due to faculty skepticism, institutional conservatism, and regulatory caution around credential integrity. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adoption of AI grading tools is uneven and cautious, especially in applied/vocational fields like family and consumer sciences, with adoption lagging behind more digitized academic disciplines. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist instructors by drafting feedback summaries, flagging incomplete submissions, suggesting grades based on rubrics, or highlighting outlier papers for closer human review. This augmentation meaningfully reduces the time burden of grading without replacing instructor judgment, making it a useful but not transformative aid. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up feedback generation, flag rubric criteria, and draft initial grades or comments, letting instructors review and finalize rather than starting from scratch. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Grading student work requires subjective judgment, contextualization of learning goals, and understanding of individual progress—tasks where AI lacks reliable consistency. While AI can score objective tests or perform surface-level checks on formatting, evaluating projects, papers, and lab work at the depth expected in postsecondary education remains beyond reliable automation, and current systems would require extensive human review. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can grade written assignments and provide feedback with substantial time savings, but grading laboratory work, projects, and nuanced student work often requires contextual judgment and rubric interpretation that current AI handles imperfectly. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Institutional and legal expectations require faculty to take personal responsibility for student assessment and grade integrity; accreditation bodies and universities maintain policies that a human instructor must validate grades. Additionally, students expect and often demand direct instructor feedback, creating organizational and reputational friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Grading is typically an instructor responsibility tied to academic integrity and institutional policy, requiring human oversight and final sign-off, though not usually subject to strict licensing requirements. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | An API call to grade a single assignment costs pennies, making AI inference cheaper than instructor time; however, the setup cost (prompt engineering, rubric calibration) and oversight burden (human review of AI grades) can offset savings, keeping the all-in cost roughly comparable to direct human grading. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI grading tools cost a fraction of instructor time per assignment, especially for large classes, though oversight and calibration checks reduce the savings somewhat. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI tools can assist with rubric-based scoring on well-structured assignments, but no deployed product reliably evaluates open-ended lab reports, project quality, or essay arguments at postsecondary standards without material error. Existing LLM-based grading assistants show promise but remain limited in production reliability and lack the contextual understanding of course-specific expectations. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | AI-assisted grading tools (e.g., automated essay scoring, LMS-integrated feedback tools) are deployed in some postsecondary settings but are not universally trusted for high-stakes or project-based grading in specialized fields like family and consumer sciences. |
Select and obtain materials and supplies, such as textbooks.
37CI 23–52 · exposure 38 · augmentation 63 · importance 4.0/5 · click for rater detail
Select and obtain materials and supplies, such as textbooks.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational institutions, especially postsecondary, operate under strict procurement rules and move slowly on automation. No evidence of widespread AI-driven procurement adoption in academic settings; most purchasing remains human-mediated by policy. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education administrative processes adopt AI tools slowly outside of pilot programs, with procurement and curriculum decisions still largely manual. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by comparing supplier catalogs, flagging cost differences, or summarizing material reviews, helping teachers make faster selection decisions. However, the augmentation is moderate because human judgment and institutional constraints remain central to the task. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by surfacing textbook options, summarizing reviews, comparing prices, and drafting supply lists, significantly speeding up the human's research and decision process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in identifying and comparing materials or supplies, the task requires human judgment about curriculum fit, budget constraints, and institutional needs, plus actual procurement actions (ordering, contracting). Current AI cannot end-to-end handle the full workflow with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can research, compare, and recommend textbooks and supplies quickly, but actual procurement, budget approval, and vendor coordination still require human decision-making and institutional processes. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Institutional budgeting rules, vendor approval processes, and purchasing authorization requirements create strong adoption barriers. Many institutions require a human to sign off on purchases and verify material compliance with curriculum standards, creating a legal/policy requirement for human involvement. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Some institutional purchasing procedures and budget authorization requirements create friction, but there is no licensing or legal requirement that a human select textbooks. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The labor cost of a teacher or procurement officer reviewing and approving materials selection is modest relative to the overhead of AI procurement systems with human oversight. Integration and error-checking costs would likely approach or exceed the time savings. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI can cheaply generate recommendations and comparisons, but human time is still needed for final selection, ordering, and budget approval, keeping overall cost savings moderate rather than dramatic. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs independent procurement end-to-end; AI tools can help search and compare suppliers or catalog materials, but humans must still authorize purchases and manage vendor relationships. The task involves real-world interactions (negotiation, institutional approval) that fall outside production automation. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Procurement and recommendation tools exist and AI assistants can draft supply lists or compare textbook options, but no deployed product fully manages end-to-end material selection and acquisition for postsecondary courses. |
Plan, evaluate, and revise curricula, course content, course materials, and methods of instruction.
33CI 29–37 · exposure 25 · augmentation 75 · importance 4.4/5 · click for rater detail
Plan, evaluate, and revise curricula, course content, course materials, and methods of instruction.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Postsecondary education has historically lagged in AI adoption, and curriculum governance remains a domain where faculty expertise is jealously guarded. While some institutions experiment with AI-assisted content creation, widespread production adoption of AI-driven curriculum planning is nascent. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education, especially niche postsecondary vocational fields like family and consumer sciences, adopts AI curriculum tools slowly compared to fast-moving digital sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist faculty by drafting outlines, suggesting materials, analyzing pedagogical gaps, and offering revision suggestions, thereby saving significant planning time while faculty retain evaluative control. This augmentation is already being adopted informally in many institutions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully speed up drafting course outlines, generating materials, and suggesting revisions, substantially aiding instructors while they retain final curricular judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with generating draft materials and analyzing existing curriculum structure, but curriculum planning requires judgment about pedagogical philosophy, institutional context, and learner needs that cannot be fully automated. Current systems cannot reliably handle the evaluative and revisionary judgments that make curricula coherent and effective. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can draft syllabi and suggest content but cannot independently determine institutional learning goals, accreditation alignment, or evaluate pedagogical effectiveness with subject-specific judgment, so full end-to-end automation with equal quality is not yet feasible.imensions.rimensions.rimensions.rimensions.rimensions. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Institutions typically require faculty governance of curriculum and may have accreditation bodies that mandate human expert review of instructional design. Shared decision-making norms, professional autonomy expectations, and institutional friction around outsourcing academic judgment create strong adoption friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing law requires a human to design curricula, but institutional accreditation standards, faculty governance, and academic norms create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI inference for curriculum draft generation is very cheap compared to the expert time typically paid to design courses and curricula. Integration and oversight remain nontrivial, but the base per-output cost ratio is favorable, probably 5–10× in favor of AI. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting tools reduce time spent on content generation significantly, but human oversight, subject expertise, and revision cycles keep costs roughly comparable rather than order-of-magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While LLMs can produce course outlines and material suggestions, no deployed system reliably handles end-to-end curriculum evaluation and revision with the quality teachers need. Products exist for content generation, but they lack the contextual understanding and pedagogical depth required for actual curriculum governance. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some ed-tech tools help generate course materials or rubrics, but no deployed product reliably plans and revises entire postsecondary curricula autonomously in production. |
Advise students on academic and vocational curricula and on career issues.
33CI 29–37 · exposure 25 · augmentation 63 · importance 4.3/5 · click for rater detail
Advise students on academic and vocational curricula and on career issues.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Most postsecondary institutions retain human advising as a core function despite cost pressures. Adoption of AI advising tools remains pilot-stage; institutions have been slow to deploy AI-only or AI-primary advising due to student expectations and accreditation norms favoring human contact. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education is a relatively slow-adopting sector for AI-driven student services, with pilots more common than full-scale deployment of AI advising. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can helpfully augment advising by surfacing relevant curriculum options, career data, and prerequisite information, freeing advisors to focus on relationship-building and navigating complex personal circumstances. Systems that assist rather than replace show moderate productivity gains in information retrieval. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can help advisors quickly research curricula, generate personalized resources, and draft communications, meaningfully boosting productivity while the human retains the advising relationship. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can provide generic curriculum information and career data, meaningful advising requires understanding individual student circumstances, aspirations, and constraints. Current systems can draft career guidance or list options, but cannot reliably replicate the personalized, contextual judgment and relationship-building that effective academic advising demands. |
| Task automatability | claude-sonnet-5 | 2/5 | AI chatbots can provide generic curriculum or career information, but genuine advising requires understanding a specific student's history, goals, institutional requirements, and building rapport, which current AI cannot fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Student advising on academic and vocational pathways typically requires a human advisor in higher education accreditation standards and institutional policy; many institutions mandate human sign-off. Liability concerns around career guidance accuracy and student trust in human relationships create regulatory and organizational friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement typically governs academic advising, but institutional policies, liability for poor guidance, and student preference for human mentorship create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI tools for career matching and curriculum information are cheap to operate at scale (marginal inference cost near zero), compared to paying faculty advisors. However, integration and oversight overhead and the need for human validation reduce the advantage somewhat. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools are cheap for basic Q&A, but effective advising still requires substantial human oversight and follow-up, so total cost savings versus a human advisor are moderate rather than dramatic. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Chatbots and career-matching tools exist but rely on scripted responses and limited datasets; they struggle with nuanced student situations and lack the credibility authority of human advisors. No deployed system reliably handles the full range of academic planning and career counseling at production quality. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some advising chatbots and career-guidance tools exist on campuses, but they handle only routine FAQs and scheduling; complex individualized academic/career advising is not reliably automated in production. |
Conduct research in a particular field of knowledge and publish findings in professional journals, books, or electronic media.
28CI 25–30 · exposure 25 · augmentation 75 · importance 3.9/5 · click for rater detail
Conduct research in a particular field of knowledge and publish findings in professional journals, books, or electronic media.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Academia has historically been slow to adopt automation, and research publication remains human-centered despite AI writing tools. While some researchers use AI for drafting and brainstorming, end-to-end research automation has not gained traction in postsecondary teaching-researcher roles. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education and academic research are relatively slow to adopt AI for core research tasks, though tools for writing assistance and literature synthesis are gaining some traction. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments human researchers through literature summarization, data visualization, manuscript drafting, and idea elaboration. Many academics now use AI to accelerate writing and exploratory analysis while maintaining human oversight and final judgment, materially raising research productivity. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly aids literature reviews, statistical analysis, drafting, and editing, meaningfully boosting researcher productivity while the human retains responsibility for original contributions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with literature review, data analysis, and drafting, original research conception, experimental design validation, and the full publication pipeline require sustained human judgment and disciplinary expertise. AI cannot reliably conduct the field-specific empirical or theoretical work that generates novel findings worthy of peer publication. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can assist with literature review, data analysis, and drafting, but original research design, data collection, experimentation, and generating novel findings require human expertise and cannot be fully automated end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Academic norms, peer review requirements, and institutional credentialing create substantial barriers: research must be attributed to humans, results must be validated through human expert review, and journals require author accountability. Funding agencies and institutions demand human researcher responsibility. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Academic publishing requires human authorship, accountability, and credentialed expertise; journals have norms and increasingly restrictions on AI-generated content, though no strict licensing barrier exists. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI writing and analysis tools are cheap, but the dominant cost in academic research is researcher time and institutional overhead. AI inference does not meaningfully reduce the human researcher cost of conducting original, publishable research in specialized domains. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply assist with drafting and summarization, but the core research process still requires substantial human labor, oversight, and domain expertise, keeping overall costs comparable to human-led work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed system performs end-to-end research and publication autonomously. AI tools (ChatGPT, Claude) support writing and idea exploration, but peer review, novelty assessment, and research integrity require human researchers. Production use is limited to auxiliary drafting, not independent research execution. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like AI writing assistants and literature-review tools exist but no deployed system reliably conducts full academic research and produces publishable original findings without heavy human involvement. |
Prepare and deliver lectures to undergraduate or graduate students on topics such as food science, nutrition, and child care.
23CI 21–25 · exposure 25 · augmentation 75 · importance 4.5/5 · click for rater detail
Prepare and deliver lectures to undergraduate or graduate students on topics such as food science, nutrition, and child care.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Universities and colleges remain highly conservative; AI adoption in postsecondary teaching is mostly experimental (lecture-note tools, tutoring aids) rather than displacement of instructors. The sector has not shown material production adoption for autonomous teaching. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adoption of AI for teaching content creation is growing but actual replacement of lecture delivery remains rare and cautious due to accreditation and pedagogical norms. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI tools (content generation, slide automation, student Q&A analysis) can meaningfully assist instructors in preparing materials, grading, and managing student interactions, substantially raising their productivity while they remain the core delivery mechanism. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools substantially help instructors draft lecture materials, create visuals, summarize research, and generate practice questions, meaningfully boosting prep productivity while the instructor still delivers content. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate lecture content and outlines on food science or nutrition topics, delivering effective lectures requires real-time audience engagement, adaptive teaching, classroom presence, and ability to respond to student questions—capabilities current systems cannot reliably replicate end-to-end. Content generation alone addresses perhaps 20–30% of the task's time investment. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft lecture content and slides, but live delivery, adaptive explanation, classroom management, and student interaction require human presence and cannot be fully automated end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Accreditation bodies, institutional policy, and student expectations (and in many cases explicit institutional mandates) require human instructors for credit-bearing courses. Liability and quality assurance in higher education create strong friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Accredited postsecondary teaching typically requires credentialed faculty, institutional accreditation standards, and student expectations of live instructor interaction, creating strong structural barriers to full replacement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Even with AI lecture-content tools, the cost of deploying systems, managing them, and maintaining human oversight for quality and student engagement exceeds the loaded salary of a postsecondary instructor, especially when factoring integration and liability. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI content generation is cheap, actual lecture delivery still requires paid faculty time, oversight, and institutional accreditation, keeping overall cost comparable to human-led instruction. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably delivers full undergraduate lectures autonomously; prototype systems exist for lecture generation or Q&A but fall short of the pedagogical and interpersonal demands. Educational institutions in production still rely on human instructors. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Tools exist for generating educational content and even AI-narrated presentations, but no deployed product reliably delivers full postsecondary lectures with pedagogical quality and interactivity at scale. |
Conduct faculty performance evaluations.
18CI 7–29 · exposure 13 · augmentation 38 · importance 4.1/5 · click for rater detail
Conduct faculty performance evaluations.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Higher education has been slow to adopt AI for personnel decisions due to governance structures, faculty governance, and legal caution. Most adoption remains pilots of administrative support tools rather than replacement of the evaluator role itself. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education administration adopts AI slowly for personnel evaluation due to sensitivity, legal risk, and union/tenure considerations, with only limited pilot use of AI-assisted analytics. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist evaluators by aggregating student feedback data, organizing teaching metrics, generating preliminary summaries, and flagging outliers for review. This augmentation reduces administrative burden without removing human judgment from the core evaluation decision. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help compile data such as student evaluations, publication metrics, or teaching load summaries, but it offers only modest assistance to the core judgment-based evaluation process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Faculty performance evaluations require nuanced judgment about teaching effectiveness, research quality, and interpersonal contributions that demand human contextual understanding. Current AI can assist with data aggregation and flagging metrics, but cannot reliably conduct the holistic assessment and decision-making that such evaluations entail. |
| Task automatability | claude-sonnet-5 | 1/5 | Evaluating faculty performance requires nuanced judgment about teaching quality, collegiality, and professional context that AI cannot reliably assess end-to-end today.dummy This is fundamentally a human judgment and relationship-based task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Faculty evaluations involve employment decisions with significant legal and contractual implications, institutional policies on due process, and union agreements in many settings. Most institutions require human supervisors to formally conduct and sign evaluations, creating hard legal and procedural barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Faculty evaluations typically require designated academic administrators or peer committees per institutional governance and tenure/promotion policies, creating strong organizational and procedural barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-assisted evaluation systems (data aggregation, preliminary report generation) could reduce time spent by evaluators, but the core evaluative labor—interviews, judgment, documentation—remains human-intensive and must be overseen for legal compliance, keeping costs roughly comparable to human-only processes. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task, so cost comparison favors the human evaluator by default since AI cannot produce a comparable output. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI tools exist for administrative data compilation and some rubric-based scoring, no deployed product reliably performs end-to-end faculty evaluations in production. Systems lack the institutional knowledge, subjectivity-handling, and legal defensibility needed for high-stakes personnel decisions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs faculty performance evaluations autonomously; this remains squarely a human administrative and academic leadership function. |
Supervise undergraduate or graduate teaching, internship, and research work.
13CI 5–20 · exposure 8 · augmentation 50 · importance 4.1/5 · click for rater detail
Supervise undergraduate or graduate teaching, internship, and research work.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Academic institutions are laggards in automating supervisory and mentorship work; adoption of AI in this domain is minimal, with only pilot uses in administrative support rather than genuine supervision replacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adopts AI slowly for supervisory and mentoring functions, though administrative subtasks may see pilots; the domain is relationship-based and slow to digitize. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist supervisors by generating feedback summaries, flagging administrative tasks, or organizing documentation, modestly boosting faculty efficiency, but the core mentorship and judgment remain human-driven. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help supervisors track student progress, draft feedback, or summarize research drafts, improving efficiency in administrative aspects of supervision without replacing the interpersonal core. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI cannot meaningfully supervise the relational, mentorship, and accountability aspects of teaching and research work. While AI might assist with scheduling, documentation, or feedback drafting, the core supervisory function—assessing student progress, providing real-time guidance, resolving conflicts, and taking responsibility for outcomes—requires human judgment and presence. |
| Task automatability | claude-sonnet-5 | 1/5 | Supervision involves relational mentoring, real-time judgment about student progress, ethical oversight of research, and adaptive feedback that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Postsecondary institutions carry legal and accreditation duties to ensure qualified humans supervise teaching and research; faculty credentials and institutional liability create hard barriers to removing human supervisors from these roles. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Accreditation standards, institutional policies, and liability for internship/research oversight typically require a qualified faculty member to be legally and professionally responsible for supervision. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for administrative and feedback support are relatively inexpensive, but the core supervisory labor involves credentialed faculty and is not directly substitutable by current systems; total replacement cost-benefit is poor because human judgment and accountability remain essential. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since AI cannot substitute for the supervisory role, there is no meaningful cost comparison; a human supervisor's cost cannot be replaced by AI at equal quality. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs academic supervision end-to-end. Nascent tools exist for administrative support and feedback generation, but no production system replaces a supervisor's judgment, presence, or accountability in educational settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product supervises student teaching, internships, or research; existing tools only offer scheduling or feedback drafting support, not actual supervisory judgment. |
Provide professional consulting services to government or industry.
11CI 3–20 · exposure 8 · augmentation 75 · importance 3.1/5 · click for rater detail
Provide professional consulting services to government or industry.
11| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While professional services sectors are digitizing, actual consulting delivery remains heavily human-centered; AI adoption is limited to support tasks (research, drafting) rather than autonomous consulting roles, and client preferences strongly favor human experts for high-stakes advisory. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Postsecondary academic consulting roles are in a slow-adopting sector with limited production AI deployment for expert advisory services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist consulting work by synthesizing research, drafting reports, analyzing industry data, and generating scenario models, materially raising a consultant's output and turnaround time while the human maintains judgment and client relationship. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist with research synthesis, report drafting, and data analysis that support the consulting deliverables while the expert retains final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires deep domain expertise, judgment, stakeholder relationships, and client-specific strategy—none of which current AI systems can independently execute at professional consulting standards. AI cannot autonomously develop trusted advisory relationships or take responsibility for consequential recommendations. |
| Task automatability | claude-sonnet-5 | 2/5 | Consulting requires synthesizing expertise, context-specific judgment, and relationship-building that current AI cannot autonomously replicate end-to-end, though it can draft supporting materials.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Government and industry consulting often involves licensing (e.g., engineering, law), contractual liability, regulatory oversight, and explicit client requirement for a credentialed human advisor with professional indemnity. Clients typically demand a named consultant who signs off on recommendations. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Government and industry consulting often requires credentialed expertise, professional reputation, and accountability/liability that create strong barriers to full AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Professional consulting commands high hourly rates ($150–500+) and involves significant human expertise and accountability; AI tools for research or drafting support are relatively cheap but cannot replace the full consulting engagement, making total cost per delivered consulting service substantially higher for human delivery than any AI alternative. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply produce background research or drafts, but the actual consulting value (credibility, judgment, accountability) still requires paid human expert time, keeping overall cost comparable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs end-to-end professional consulting in government or industry settings. While AI can assist with research or drafting, actual consulting delivery demands human authority, accountability, and real-time adaptive engagement with clients. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a human subject-matter expert delivering professional consulting engagements to government or industry clients. |
Maintain regularly scheduled office hours to advise and assist students.
8CI 0–16 · exposure 0 · augmentation 50 · importance 4.3/5 · click for rater detail
Maintain regularly scheduled office hours to advise and assist students.
8| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational institutions have shown little interest in automating faculty office hours; adoption remains near zero because the task is seen as central to the faculty role and student experience, with strong organizational and cultural resistance to substitution. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education is adopting AI tools for tutoring and administrative support, but replacing faculty office hours specifically remains rare and mostly pilot-level. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could modestly assist faculty by preparing background information on student records, scheduling, or pre-summarizing common advising questions, but the core advisory function remains human-led and the augmentation effect is limited. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by handling routine scheduling, answering common questions, and drafting responses, freeing instructor time for higher-value student interactions during office hours. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires real-time human interaction, relationship-building, and contextual understanding of individual student circumstances. Current AI systems cannot meaningfully replace the advisory and emotional support functions that are central to office hours. |
| Task automatability | claude-sonnet-5 | 1/5 | Maintaining office hours involves real-time, in-person or synchronous availability for personalized student advising, mentorship, and relationship-building that AI cannot substitute for as an end-to-end replacement of the faculty member's presence. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Postsecondary institutions have explicit or implicit requirements that faculty maintain direct student contact and advising relationships; there are strong norms, institutional policy, and accreditation expectations that human faculty conduct office hours and provide mentorship. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier specifically for office hours, but institutional norms, accreditation expectations, and student preference for human mentorship create meaningful friction against replacement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The loaded cost of faculty time to conduct office hours is already embedded in institutional payroll; any AI system would need to provide comparable advisory value at a fraction of that cost, which it cannot demonstrate for this inherently relational task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI chatbots are cheap per query, they cannot substitute for the core deliverable (a professor's personal availability and judgment), so cost comparison favors humans for the actual task as defined. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs the role of faculty office hours—a role that requires nuanced understanding of student needs, institutional knowledge, and the ability to provide mentorship and guidance that goes beyond information retrieval. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs the function of a faculty member holding office hours; chatbots can supplement FAQs but do not replace the scheduled human availability requirement. |
Collaborate with colleagues to address teaching and research issues.
6CI 5–7 · exposure 0 · augmentation 38 · importance 3.9/5 · click for rater detail
Collaborate with colleagues to address teaching and research issues.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Academic institutions are slow to adopt automation of core collegial functions; teaching and research collaboration remain largely human-centric activities with minimal AI displacement in practice. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adopts AI tools for writing and research support but genuine interpersonal faculty collaboration remains largely untouched by automation trends. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could modestly assist with preparing research summaries or structuring meeting agendas, but the collaborative judgment and interpersonal dynamics of addressing teaching and research issues together cannot be significantly augmented by current systems. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help colleagues prepare materials, summarize research, draft agendas, or brainstorm curriculum ideas ahead of or during collaborative discussions, offering moderate productivity benefits. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Collaboration on teaching and research issues requires nuanced professional judgment, interpersonal negotiation, and context-specific institutional knowledge that current AI cannot perform end-to-end. AI cannot meaningfully substitute for the human dialogue, consensus-building, and accountability inherent in collegial academic work. |
| Task automatability | claude-sonnet-5 | 1/5 | Collaborative professional discussion involving relationship-building, shared decision-making, and institutional context cannot be meaningfully performed by AI end-to-end; it is fundamentally a human social process. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Academic institutions have strong cultural and organizational norms around collegial governance and faculty autonomy in research decisions, creating substantial friction against AI substitution. Faculty autonomy and professional accountability are deeply embedded in institutional structures. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Academic collaboration is deeply tied to institutional governance, tenure/peer relationships, and professional norms requiring human participants, making substitution highly impractical though not legally mandated. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task is fundamentally about human expert interaction and relationship-building; any AI support would be purely assistive and add cost rather than reduce the labor required for genuine collaboration. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI product performing this task, so cost comparison is not applicable in AI's favor; human collaboration remains the only viable option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs academic collaboration or institutional problem-solving autonomously. AI may assist with drafting or research synthesis, but cannot replace the collegial deliberation and decision-making required. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for collegial collaboration on teaching/research issues; AI tools at best support communication logistics, not the collaboration itself. |
Serve on academic or administrative committees that deal with institutional policies, departmental matters, and academic issues.
3CI 0–5 · exposure 0 · augmentation 38 · importance 3.9/5 · click for rater detail
Serve on academic or administrative committees that deal with institutional policies, departmental matters, and academic issues.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Academic institutions have not and are not adopting AI to serve on committees; this remains entirely human-dependent governance work with no meaningful trend toward AI substitution. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Higher education governance and committee work is a low-digitization, human-relational process with essentially no AI adoption for actual membership or voting roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might assist with drafting agendas, summarizing prior meeting notes, or gathering policy research, but cannot meaningfully augment the core human work of deliberation, voting, and institutional decision-making. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by summarizing meeting materials, drafting policy language, or synthesizing prior committee minutes, providing moderate assistance to a human committee member's preparation and follow-up work. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Committee work requires deliberation, negotiation, and judgment on complex institutional and policy matters that depend on human expertise, organizational context, and interpersonal dynamics. Current AI cannot meaningfully participate in or replace this collaborative decision-making process. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires human presence, judgment, negotiation, and institutional relationship-building in real-time deliberative settings, none of which current AI can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Institutional committees require human representation, accountability, and legal authority; policies typically mandate that faculty members serve in governance roles. Liability, fiduciary duty, and institutional authorization create hard barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Committee membership typically requires faculty status, institutional standing, and accountability for governance decisions, creating strong organizational and quasi-legal barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Serving on committees is typically a duty embedded in faculty employment and governance, not a separately costed task. AI cannot substitute for human presence and would require substantial human oversight, making it more costly than the human duty itself. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI product performing this role, so cost comparison is moot; the human's institutional presence and accountability cannot be replaced by inference costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can independently serve on committees or contribute to institutional policy decisions; this requires human judgment, accountability, and participation in human governance structures that AI systems are not authorized or capable of performing. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a human committee member's participation in governance deliberations; AI is not used to 'sit on' committees in any institution today. |
Act as advisers to student organizations.
3CI 0–5 · exposure 0 · augmentation 38 · importance 3.7/5 · click for rater detail
Act as advisers to student organizations.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Educational institutions have moved slowly toward automation in faculty advisory roles; student-facing mentoring and governance advice remain areas of strong human-preference and regulatory/accreditation requirements. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Higher education advising roles show minimal AI displacement; this is a low-digitization, relationship-driven aspect of academic service work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could modestly assist with administrative tasks like scheduling, document management, and information retrieval, but the core advisory function—providing mentorship, judgment, and institutional guidance—resists meaningful augmentation by current systems. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can help advisors with scheduling, communications drafting, budget tracking, and event planning support, moderately easing administrative burden while the human remains central. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Advising student organizations requires nuanced judgment, emotional intelligence, interpersonal trust-building, and contextual understanding of individual student needs and group dynamics—none of which current AI systems can reliably perform. This is inherently a human relationship task. |
| Task automatability | claude-sonnet-5 | 1/5 | Advising student organizations requires ongoing relationship-building, mentorship, event supervision, and institutional representation that cannot be end-to-end automated by current AI. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Universities have strong legal, regulatory, and fiduciary requirements that a licensed faculty member serve as adviser to student organizations; student welfare, liability, and Title IX compliance create hard barriers to substitution with automated systems. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Institutions typically require a designated faculty/staff advisor for liability, compliance, and accreditation purposes, creating strong organizational and policy barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Replacing a faculty adviser with AI would require significant human oversight to maintain quality and institutional accountability, making the total cost uncompetitive with a salaried adviser who performs many roles simultaneously. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this role, so cost comparison favors the human entirely; any AI use would only supplement, not replace, the labor involved. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product today performs the end-to-end advising role for student organizations in production; any AI involvement would be limited to administrative support (scheduling, documentation) rather than actual advisory functions requiring judgment and mentorship. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs the advisory role of a faculty sponsor for student clubs; this remains a human relational and administrative function. |
Participate in campus and community events.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail
Participate in campus and community events.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task involves mandatory human participation rooted in institutional and community norms. There is no meaningful adoption of AI 'attendance' at events because the value of participation is the human presence itself, making this a laggard use case for automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This task occurs in a physical, relational context with essentially no AI adoption trend since it isn't a digitizable activity. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with minor logistical aspects such as event scheduling or reminder generation, but the core task—human participation and engagement—cannot be augmented meaningfully by AI. Any AI assistance would be peripheral to the actual task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI might help with logistics, scheduling, or promotional materials for events, but offers minimal assistance to the actual act of participating. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Participating in campus and community events is inherently a social, in-person activity requiring human presence, judgment about which events matter, and authentic interpersonal engagement. No current AI system can meaningfully substitute for a human's physical presence or relational contributions to such events. |
| Task automatability | claude-sonnet-5 | 1/5 | Participating in physical campus and community events requires human presence, social interaction, and representation of the institution, which AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Event participation requires the physical, authentic presence of a faculty member as a representative of the institution and profession. Organizational culture, community expectations, and implicit institutional roles create hard barriers to any form of automation or delegation away from a human. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Physical presence, institutional representation, and social/relational expectations create strong barriers to any automated substitute, though not a formal licensing requirement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Event participation requires human presence and is not a task that AI can perform as a substitute, making cost comparison inapplicable. Any hypothetical AI overhead would exceed the negligible cost of a human showing up to an event they are already employed to attend. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI equivalent output to compare cost against; a human must be present, so AI cannot substitute at any cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can autonomously participate in community events on behalf of a human. This task depends on embodied presence and genuine human interaction, which falls outside the scope of current AI capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product substitutes for a person physically attending and participating in events; this remains entirely research-stage or nonexistent for this purpose. |
Perform administrative duties, such as serving as department head.
0CI 0–0 · exposure 0 · augmentation 38 · importance 4.0/5 · click for rater detail
Perform administrative duties, such as serving as department head.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption is minimal because department head functions are inherently human roles embedded in institutional hierarchies and legal accountability structures. Educational institutions have not moved toward automating this leadership function. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Higher education administration is slow-moving and highly bureaucratic, with essentially no adoption of AI to replace administrative leadership roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist with routine administrative tasks like scheduling, data compilation, and document drafting, but the augmentation is marginal and limited to support functions rather than core leadership decisions that define the department head role. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can assist with scheduling, drafting reports, summarizing data, or organizing communications that support administrative work, though the core leadership functions remain human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Administrative duties involving department leadership require complex interpersonal judgment, strategic decision-making, personnel management, and institutional knowledge that current AI cannot perform end-to-end. Tasks like hiring, conflict resolution, budget allocation, and faculty evaluation demand human accountability and discretion. |
| Task automatability | claude-sonnet-5 | 1/5 | Department head duties involve institutional leadership, personnel decisions, budget negotiation, and interpersonal politics that require human judgment and authority, not amenable to end-to-end AI execution.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal, regulatory, and organizational barriers are substantial: a human department head must legally hold the position and be accountable for personnel decisions, budget management, and institutional governance. Educational institutions cannot substitute AI for human leadership roles. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Serving as department head is an institutionally and often contractually defined leadership role requiring formal authority, accountability, and human decision-making that cannot be legally or organizationally delegated to AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of implementing AI systems to support department administration would be substantially higher than the savings, given the low frequency of routine administrative tasks and the need for human oversight of any AI-generated outputs in a governance context. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this role, so cost comparison is moot; any AI cost would be additive to human oversight rather than a replacement cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No current product reliably performs department head duties in a production education setting. While AI can assist with scheduling and document drafting, the core functions of leadership—evaluating staff, making personnel decisions, representing the department—require human judgment and are not delegated to AI systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs the role of a department head; this is an organizational leadership position, not a discrete automatable task. |
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.