Agricultural Sciences Teachers, Postsecondary
25-1041.00Median wage $98,700/yr8,920 employed (US)Rank #386 of 923 scored · top 42% by substitution
Teach courses in the agricultural sciences. Includes teachers of agronomy, dairy sciences, fisheries management, horticultural sciences, poultry sciences, range management, and agricultural soil conservation. 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
Substitution30
Exposure27
Augmentation58
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%27
panel mean rating 2.1/5 → substitution pressure 27/100
Technical feasibility todayw 20%28
panel mean rating 2.1/5 → substitution pressure 28/100
Cost vs. human wagew 15%34
panel mean rating 2.4/5 → substitution pressure 34/100
Adoption barriersw 20%inverted — strong barriers lower the score37
panel mean rating 3.5/5 (barrier strength) → substitution pressure 37/100
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.
91CI 86–95 · exposure 92 · augmentation 75 · importance 3.9/5 · click for rater detail
Maintain student attendance records, grades, and other required records.
91| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Academic institutions have adopted digital gradebooks, attendance systems, and student information systems as standard infrastructure for over a decade. Automation of these recordkeeping tasks is already deeply embedded in higher education operations. |
| Sector adoption velocity | claude-sonnet-5 | 5/5 | Higher education has near-universal adoption of digital LMS and SIS systems for attendance and grade tracking, representing mature, deep adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-driven systems actively assist faculty by automatically flagging attendance patterns, suggesting grade distributions, generating progress reports, and consolidating records, substantially raising their administrative productivity while they remain responsible for the underlying instructional judgments. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Automated systems substantially reduce faculty administrative burden, letting instructors focus on final grade decisions and record verification rather than manual tracking. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Maintaining attendance records, grades, and institutional records is highly routine and rule-based. Current systems (LMS platforms, automated gradebooks, attendance trackers) can capture and organize this data end-to-end with minimal human intervention, easily exceeding 50% time savings. |
| Task automatability | claude-sonnet-5 | 5/5 | Recording attendance and grades is a structured data-entry task fully handled by existing LMS/SIS platforms with automated gradebooks, attendance trackers, and reporting tools. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While FERPA and institutional policies govern data handling, there are no licensing or legal barriers preventing automation itself. Integration into existing institutional systems and compliance oversight create minor friction, but automation is standard practice and widely permitted. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Some institutional policy and FERPA compliance requirements exist around grade certification and data privacy, but the record-keeping mechanics themselves face few structural barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | The cost of cloud-based LMS and student information systems is negligible on a per-record basis (pennies per student per term), while a faculty member's time managing records manually carries substantial loaded wage cost, making AI/automation orders of magnitude cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Software-based record management costs a small fraction of an instructor's time compared to manual record-keeping, offering large per-record cost savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Mature, widely deployed products (Canvas, Blackboard, Banner, Workday) reliably perform these recordkeeping functions at scale in educational institutions today. Universities systematically use these systems for grades, attendance, and compliance records. |
| Technical feasibility today | claude-sonnet-5 | 5/5 | Products like Canvas, Blackboard, PowerSchool, and university SIS systems already perform automated record-keeping reliably at scale in production across higher education. |
Compile bibliographies of specialized materials for outside reading assignments.
83CI 76–90 · exposure 83 · augmentation 88 · importance 3.1/5 · click for rater detail
Compile bibliographies of specialized materials for outside reading assignments.
83| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Universities and faculty have begun adopting AI tools for administrative and research-support tasks, but adoption of AI bibliography generation remains in the pilot/early-adoption phase. Many institutions still rely on librarians or manual student/faculty effort. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Higher education is adopting AI tools for research and course prep at a moderate pace, with many faculty using AI search tools but institution-wide deployment still limited. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assistants substantially augment faculty productivity for this task by rapidly generating draft bibliographies that the instructor can review, refine, and personalize for learning objectives, transforming what would otherwise be manual curation work. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up literature discovery and citation formatting for instructors while they retain final judgment on relevance and quality of sources. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Compiling bibliographies is highly automatable today. AI systems can search academic databases, identify relevant materials by subject/keyword, format citations in standard styles (APA, MLA, Chicago), and organize sources into structured lists with minimal human correction, easily meeting the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 5/5 | Compiling bibliographies on a specialized topic is a well-defined information retrieval and formatting task that current AI systems with search/citation tools can do quickly and at equal or better quality than manual compilation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal regulatory or licensing barriers to automating bibliography compilation. Some instructors may prefer human curation for pedagogical reasons or trust, but no legal requirement mandates human involvement in this clerical task. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, regulatory, or human-contact requirement applies to compiling a bibliography; it's an administrative/preparatory task with no legal barrier to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI inference cost for bibliography generation is negligible (cents per task), while a human faculty member or librarian would spend 30–90 minutes on a thorough assignment bibliography at a loaded wage of $50–100+/hour, making AI at least 100× cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | AI-assisted literature search and citation compilation costs a fraction of a cent to a few dollars in compute versus the faculty time otherwise spent manually searching databases. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (ChatGPT, Claude, specialized tools like Zotero with AI plugins, academic search APIs with AI summarization) reliably generate bibliographies and source lists at scale. While quality varies and human review is standard practice, the core capability is production-ready. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Deployed tools like reference managers with AI search (Elicit, Semantic Scholar, ChatGPT with browsing) reliably generate topical reading lists and citations today, though occasional inaccuracies require verification. |
Prepare course materials, such as syllabi, homework assignments, and handouts.
76CI 71–81 · exposure 75 · augmentation 100 · importance 4.1/5 · click for rater detail
Prepare course materials, such as syllabi, homework assignments, and handouts.
76| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Adoption is growing in higher education but remains in pilot and early-mainstream phase rather than full substitution. Faculty awareness and experimentation with AI syllabus and assignment generation is increasing, but institutional policies and faculty preferences for direct authorship limit deep, rapid displacement. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Higher education has moderate AI adoption for administrative and content-prep tasks, with growing use of AI drafting tools among faculty, though institutional policy and caution creates uneven, mid-level adoption compared to fast-moving sectors like finance. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI excels at augmenting faculty productivity for this task: it can draft materials from course objectives, suggest assignment variations, adapt content for different student levels, and format handouts—all while the instructor retains full control, review, and customization authority. This is a prime augmentation use case in higher education. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI dramatically speeds up drafting syllabi, assignments, and handouts while instructors retain control over final content, accuracy, and pedagogical alignment, a clear case of high-value augmentation. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can generate syllabi, homework assignments, and handouts at near-production quality with minimal input (course name, level, objectives). While human review is typically needed, AI can easily achieve 50%+ time savings by automating content generation, organization, and formatting from course specifications. |
| Task automatability | claude-sonnet-5 | 4/5 | Drafting syllabi, homework assignments, and handouts is largely text generation from structured inputs, which current LLMs handle well with modest editing overhead, meeting the ≥50% time-saving bar for most of this work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Academic institutions typically retain final approval authority and require faculty sign-off on course materials, and many have policies preferring faculty authorship for IP/academic integrity reasons. These create meaningful adoption friction, though they do not absolutely prohibit AI assistance or drafting. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or legal requirement mandates a human author course materials personally; academic norms allow instructor discretion and there's no liability barrier to using AI-assisted drafts. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | A single AI API call generating a complete syllabus costs pennies to low dollars, while a faculty member's time to produce equivalent material costs $50–$200+ in fully loaded wages. The cost differential is easily an order of magnitude or more. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Generating draft course materials via AI costs a small fraction of a cent to a few dollars in compute versus hours of a postsecondary instructor's time, an order-of-magnitude cost advantage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed LLM-based tools (ChatGPT, Claude, Copilot) reliably produce course materials in real educational contexts. Many institutions already use AI to draft syllabi and assignments. Performance is consistently high for straightforward material generation, though customization and subject-matter accuracy still benefit from human oversight. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Widely deployed tools (ChatGPT, Copilot, course-design AI assistants) are routinely used by instructors to draft syllabi and assignments in production today, though customization for specialized agricultural science content still requires review. |
Compile, administer, and grade examinations, or assign this work to others.
61CI 56–66 · exposure 55 · augmentation 88 · importance 3.9/5 · click for rater detail
Compile, administer, and grade examinations, or assign this work to others.
61| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Postsecondary institutions have rapidly adopted LMS platforms, automated grading tools, and online exam administration—especially post-2020. Production deployment of these systems is common in higher education, though some instructors retain manual practices. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adoption of AI grading tools is growing but still cautious and uneven, particularly in specialized STEM/agricultural fields where domain-specific tools are less mature. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI tools substantially augment instructor productivity by generating question banks, automating objective grading, flagging outliers, and providing analytics—all while instructors retain control over assessment strategy and subjective evaluation. This is a strong augmentation case in practice. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can substantially speed up exam question generation, rubric creation, and first-pass grading, letting instructors focus on final review and edge cases. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate significant portions of exam creation (question generation), administration (online proctoring, delivery), and basic grading (objective questions, rubric-based scoring), but subjective assessment and pedagogical judgment for complex answers remain human domains. This reaches approximately 50% time savings for structured components. |
| Task automatability | claude-sonnet-5 | 3/5 | Compiling and grading standard exams (especially multiple-choice or short-answer) can be significantly assisted by AI, but grading essay/lab-based agricultural science exams requiring domain judgment still needs human oversight, capping full automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few hard barriers exist; exams are typically within instructor discretion and not licensure-dependent. However, institutional policies, accreditation standards, and preferences for human grading of subjective work create moderate friction to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human grade exams, though academic integrity policies and institutional norms create moderate friction around delegating grading fully to AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-powered exam systems (LMS platforms, automated grading software) cost significantly less per administration cycle than instructor time for compiling, administering, and grading large cohorts, especially for objective components. Cost advantage is substantial though not quite an order of magnitude across all exam types. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-assisted exam generation and automated grading tools are inexpensive per use compared to faculty or TA time, especially for large classes, though setup and calibration add cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products exist in educational technology (learning management systems, automated grading tools, exam banks) that reliably handle exam administration and objective grading at scale in postsecondary institutions. Some subjective grading requires human review, but deployment is widespread and production-ready. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Gradescope, AI-based quiz generators, and LLM-assisted grading exist and are used in some courses, but reliable handling of technical/scientific content with partial credit and nuanced rubrics remains inconsistent. |
Select and obtain materials and supplies, such as textbooks and laboratory equipment.
57CI 30–84 · exposure 58 · augmentation 75 · importance 3.6/5 · click for rater detail
Select and obtain materials and supplies, such as textbooks and laboratory equipment.
57| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Educational institutions and universities are rapidly adopting AI-driven procurement and inventory management systems as part of digital transformation, with adoption accelerating in administrative and supply-chain functions. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education administrative and procurement functions have seen slow, uneven AI adoption compared to fields like finance or tech, with most schools still using traditional processes. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI can substantially assist teachers by automating catalog search, flagging relevant new equipment, tracking inventory, and generating reorder alerts, freeing teacher time for pedagogical tasks while maintaining human oversight of purchasing decisions. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can help faculty search for textbooks, compare specifications, and generate supply lists, meaningfully speeding up part of the research and selection process. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Selecting and obtaining materials via procurement workflows is highly automatable: AI can parse textbook catalogs, cross-reference lab equipment specifications, compare supplier inventories and pricing, generate purchase orders, and track delivery—reducing hands-on time by over 50% end-to-end. Educational institutions already use procurement systems that automate large portions of this workflow. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help identify and compare textbooks or lab equipment options, but selecting appropriate curriculum-aligned materials and physically procuring supplies requires human judgment, budget authority, and institutional processes that AI cannot fully replace. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While institutions may require human sign-off on budget and purchasing authority for compliance, no legal licensing requirement or regulatory barrier prevents AI-assisted or fully automated material procurement; organizational policy friction is the main impediment. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but institutional procurement rules, budget approval chains, and departmental curriculum standards create moderate organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated procurement systems cost far less than paying a faculty member's loaded wage to manually source, compare, and order supplies; the ratio strongly favors AI at scale. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI-assisted search could cut research time, the overall task still requires human decision-making, vendor negotiation, and purchase approval, limiting cost savings relative to human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Procurement and inventory management systems with AI-assisted search, recommendation, and ordering are deployed in universities and educational institutions today, though full end-to-end automation still requires human approval of purchases and some manual supplier vetting. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously selects and procures academic/laboratory materials end-to-end; existing tools (e-commerce search, recommendation engines) only assist parts of the process. |
Write grant proposals to procure external research funding.
47CI 41–54 · exposure 42 · augmentation 75 · importance 3.5/5 · click for rater detail
Write grant proposals to procure external research funding.
47| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Universities and research institutions are experimenting with AI writing tools for grant support, with early adoption in tech-forward departments, but widespread production deployment remains limited; most adoption is still pilot or supplementary rather than transformative. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Higher education is a moderate adopter of AI writing tools; academic grant writing has seen growing but still cautious uptake due to concerns about originality, plagiarism policies, and funder rules on AI use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments grant writers by accelerating drafting, improving clarity and organization, generating budget narrative options, and catching gaps—transforming productivity while faculty and grant specialists retain full control and final authority over research framing and institutional messaging. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially speeds up drafting, editing, formatting, and idea generation for grant proposals, letting faculty focus on refining scientific content and strategy. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Grant writing requires substantial domain expertise, original research synthesis, and institutional knowledge that current AI cannot fully automate end-to-end. While AI can draft sections and improve clarity, the strategic framing, budget justification, and alignment with funder priorities demand human judgment and rarely yield 50% time savings without extensive human revision. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft substantial portions of grant proposals (background, literature review, boilerplate sections) but crafting a competitive, novel research plan tailored to a specific funder still requires significant human expertise and iteration. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No legal requirement mandates human authorship of grant proposals, but universities often have institutional review and compliance requirements, and many faculty and grant offices prefer human-led writing for quality assurance and institutional accountability, creating moderate friction to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but institutional review, PI accountability, and funder expectations of genuine intellectual contribution create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI inference and integration costs for grant-writing assistance are extremely low (< $1 per proposal draft), whereas hiring grant writers or diverting faculty time carries substantial labor cost, making AI assistance economically favorable even if human review remains necessary. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting tools are cheap per query, but the overall proposal still requires substantial faculty time for review, technical accuracy, and strategic framing, keeping total cost closer to comparable rather than order-of-magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI writing assistants (ChatGPT, Claude, specialized tools) exist and are increasingly used for grant drafting support, but deployed systems still require heavy expert oversight and produce variable quality across different funding agencies and research domains. Production adoption is growing but uneven and heavily edited. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like ChatGPT, Grantable, and specialized academic writing assistants are used to draft proposal sections today, but reliability for full proposals with accurate citations and funder-specific compliance is inconsistent. |
Evaluate and grade students' class work, laboratory work, assignments, and papers.
38CI 25–51 · exposure 38 · augmentation 63 · importance 4.0/5 · click for rater detail
Evaluate and grade students' class work, laboratory work, assignments, and papers.
38| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While some universities pilot AI grading for objective assessments (multiple choice, simple calculations), adoption in higher education—especially for complex evaluation requiring judgment—remains slow and cautious. Most postsecondary institutions have not deployed AI for the full scope of this task. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education, especially specialized agricultural science departments, has been slower and more cautious in adopting AI grading tools compared to sectors like finance or general ed-tech. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist instructors by flagging potential plagiarism, organizing submissions, and offering preliminary scoring on objective criteria, raising grading efficiency on parts of the workflow. However, augmentation is limited to routine components rather than transforming the full evaluative task. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up grading of written assignments, provide draft feedback, and flag patterns, letting instructors focus final judgment on technical/lab-specific content. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with basic grading criteria (formatting, spelling, factual accuracy), evaluating laboratory work, assignments, and papers in agricultural sciences requires contextual judgment about experimental design, methodology, and domain-specific reasoning that current systems cannot reliably perform end-to-end. The task involves subjective assessment and feedback that falls short of the 50% time-saving threshold for full automation. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft grades and feedback on written assignments and objective work reasonably well, but agricultural sciences coursework often includes lab reports, field data interpretation, and specialized technical judgment that require domain expertise and verification. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational institutions have strong norms and, in many cases, explicit policies requiring human instructors or qualified teaching assistants to evaluate student work for accountability, fairness, and pedagogical feedback purposes. Institutional friction and educational governance create meaningful barriers to substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Grading is generally an instructor responsibility with some institutional autonomy; while no formal license is required, academic integrity policies and instructor accountability create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI grading tools require integration, setup, and ongoing human review to ensure accuracy, making the all-in cost competitive with or exceeding the time a teaching assistant or instructor would spend on spot-checking and remediation rather than replacing the full task. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated grading tools cost a small fraction of instructor time per assignment for standard written work, though oversight and calibration reduce the savings somewhat for specialized content. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some AI tools (e.g., plagiarism detectors, basic rubric scorers) exist in production, but they are narrow in scope and require significant human oversight to catch errors or nuanced grading decisions. No deployed system reliably grades laboratory work or complex assignments across all dimensions without material error rates. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | AI grading assistants and LMS-integrated tools exist and are used for essays and quizzes, but reliable handling of specialized lab/field-based agricultural work is narrower and less proven in production. |
Advise students on academic and vocational curricula and on career issues.
29CI 25–34 · exposure 25 · augmentation 63 · importance 4.2/5 · click for rater detail
Advise students on academic and vocational curricula and on career issues.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Postsecondary institutions have been slow to adopt AI advising systems in production. Most remain in pilot phases or supplement human advisors; core advising relationships remain human-centered due to trust and accountability expectations. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adoption of AI advising tools is still in pilot phases; academic advising remains largely human-delivered with slow institutional change cycles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist advisors by surfacing relevant curricula options, flagging degree requirements, and summarizing career data, raising efficiency on information retrieval tasks. However, the core judgment—matching student goals to paths—still relies on the human advisor's expertise and relationship. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by providing curriculum information, career pathway data, and drafting personalized recommendations, freeing advisors to focus on nuanced conversations. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can provide generic academic and career information, personalized academic advising requires understanding individual student circumstances, constraints, and aspirations. Current systems cannot reliably match curricula to individual needs or navigate complex career pathways with human-level judgment, falling well short of 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Advising involves personalized judgment, relationship-building, and institutional knowledge that current AI cannot fully replicate end-to-end, though it can support parts of the information-gathering process.atable |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Institutional regulations often require licensed faculty or credentialed advisors to sign off on academic plans and career guidance. Students and institutions have strong preferences for human advisors, and reputational and legal risk from poor guidance creates significant organizational friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier per se, but students and institutions strongly prefer human relationship-based advising, and faculty advising is often tied to institutional roles and accreditation expectations. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration costs, oversight of AI recommendations, and the need for human review and sign-off make AI advising systems comparable to or more expensive than the marginal cost of faculty advising, especially given liability concerns. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools for basic advising queries are cheap, but the human labor cost for genuine mentorship is not fully displaced, making the effective cost comparison roughly comparable when quality-adjusted. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Chatbots and basic advising systems exist but lack the contextual depth, accreditation knowledge, and institutional familiarity needed for reliable guidance. No mainstream product reliably performs comprehensive academic and career advising at production scale in higher education. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Chatbots and advising tools exist for basic FAQ-style guidance, but no deployed product reliably handles nuanced academic/career advising for postsecondary students at scale without human oversight. |
Plan, evaluate, and revise curricula, course content, and course materials and methods of instruction.
29CI 25–34 · exposure 25 · augmentation 63 · importance 4.0/5 · click for rater detail
Plan, evaluate, and revise curricula, course content, and course materials and methods of instruction.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Higher education lags in adopting AI for curriculum design; most institutions remain in pilot or exploratory phases. Faculty governance and institutional inertia slow adoption of curriculum automation compared to information-sector adoption patterns. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adopts AI tools unevenly and cautiously, with curriculum design still largely a faculty-driven, slow-moving process despite growing use of AI for content drafting. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating content drafts, analyzing best-practices literature, and summarizing feedback, meaningfully reducing the burden of routine material preparation and research synthesis while faculty retain evaluative and design authority. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI is quite useful for generating course outlines, updating content, suggesting readings, and revising materials, substantially speeding up the drafting phase of curriculum work while faculty retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft course materials and analyze existing curricula, curriculum planning requires iterative judgment about learning objectives, pedagogical fit, and institutional context. AI cannot reliably evaluate pedagogical effectiveness or revise based on student outcome data without substantial human oversight, falling short of the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can draft syllabi and suggest materials, but genuine curriculum planning requires institutional judgment, accreditation alignment, and pedagogical expertise that current AI cannot autonomously deliver end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Academic institutions require faculty expertise, accreditation bodies mandate human review of curricula, and legal/contractual frameworks often designate curriculum development as a core faculty responsibility requiring professional judgment and accountability. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement for curriculum design itself, but institutional governance, accreditation standards, and departmental review processes create meaningful organizational friction against full AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools (LLMs, content generators) cost roughly $0.01–$0.10 per task instance, but integration, faculty review, and revision oversight are labor-intensive, making the all-in cost competitive with or exceeding a faculty member's hourly curriculum work. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting tools are cheap per query, but the human faculty time for review, approval, and contextual adaptation remains substantial, keeping overall cost comparable rather than dramatically lower. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Tools exist to assist with content generation and outline creation, but no production system reliably performs end-to-end curriculum evaluation and revision. Educational institutions still require human faculty judgment on curricular coherence and alignment with accreditation standards. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like ChatGPT and course-design assistants are used informally by faculty, but no deployed product reliably owns curriculum revision as a standalone production process in higher education. |
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 4.1/5 · 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 | Academic and agricultural research sectors show slow adoption of AI for core research tasks; AI is used for auxiliary functions (data management, writing support) but not for autonomous research execution. Cultural and institutional resistance to algorithmic research ownership slows displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Academic research in agricultural sciences is a slower-adopting sector; AI tools are used piecemeal (writing, literature search) but deep integration into the research pipeline remains limited and cautious due to publishing norms. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI substantially augments researcher productivity through literature summarization, statistical analysis, manuscript drafting, and experimental design suggestions, allowing researchers to focus on conceptualization and interpretation while AI handles time-consuming analytical and writing tasks. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly aids literature reviews, statistical analysis, manuscript drafting, and editing, meaningfully boosting researcher productivity while the scientist retains control over experimental design and interpretation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with literature review, data analysis, and manuscript drafting, original research conception, experimental design, hypothesis formation, and the judgment required to conduct novel investigations remain largely human-dependent. The core creative and investigative work cannot be fully automated to meet the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can assist with literature review, data analysis, and drafting text, but original agricultural science research requires experimental design, fieldwork/labwork, hypothesis generation, and novel contribution that current AI cannot autonomously perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: academic publishing norms require human authorship and accountability; institutional review boards mandate human researcher responsibility; funding agencies and academic institutions legally require credentialed human researchers to conduct and vouch for research. Regulatory and professional licensing requirements protect this task. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement to publish research, but academic norms, peer review, authorship ethics, and institutional expectations around researcher credibility create meaningful friction against full AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI infrastructure (including oversight, fact-checking, and correction of errors) plus integration overhead remains substantial relative to the marginal cost of a trained researcher performing research that already demands their salary. Human researchers are still more cost-effective for original discovery work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply handle literature synthesis and drafting, but the core research (experiments, data collection, domain expertise, peer-reviewed validation) still requires expensive skilled human labor, keeping overall cost savings modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably conducts end-to-end research in agricultural sciences; AI tools exist for writing support and data processing but not for independent research direction, methodological innovation, or peer-review-standard publication. Current systems lack the domain expertise and judgment needed for novel scientific contribution. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like AI writing assistants and research tools help with parts of the workflow, but no deployed system reliably conducts full agricultural research studies and produces publishable, original findings without extensive human direction. |
Participate in student recruitment, registration, and placement activities.
28CI 25–30 · exposure 25 · augmentation 50 · importance 3.8/5 · click for rater detail
Participate in student recruitment, registration, and placement activities.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Most postsecondary institutions have adopted email and CRM tools but remain hesitant to automate core recruitment and placement decisions; adoption is confined to administrative support tasks rather than end-to-end automation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education is a relatively slow-adopting sector for AI in personalized student-facing roles, with pilots for chatbots in admissions but limited faculty-level automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist faculty with initial applicant screening, scheduling, and preparing placement match recommendations, materially reducing administrative burden; however, the human advisor remains essential for interpreting student circumstances and career fit. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can help draft recruitment materials, manage communications, and organize registration logistics, freeing faculty time for personalized interactions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with email campaigns, data entry, and scheduling, but the core work—persuading prospective students, conducting interviews, and building relationships—requires human judgment and interpersonal presence that current systems cannot reliably replace at scale. |
| Task automatability | claude-sonnet-5 | 2/5 | This task involves relationship-building, personal advising, and institutional representation that current AI cannot fully replicate, though some administrative sub-tasks like scheduling could be automated. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational institutions face fiduciary duty to students, regulatory compliance in admissions (nondiscrimination laws), accreditation requirements for academic advising, and strong institutional preferences for human judgment in placement—all limiting autonomous automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No hard licensing barrier exists, but institutional norms, personal rapport with prospective students, and faculty involvement in departmental decisions create moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of AI recruitment tools has meaningful upfront costs and requires institutional setup; the per-student overhead is comparable to or slightly exceeds the marginal cost of human admissions staff for this specific task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While automated communication tools are cheap, the actual placement and personalized recruitment work still requires significant human faculty time and judgment, limiting cost savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some recruitment platforms use AI for initial screening and communications, but no mature system reliably handles the full recruitment-to-placement pipeline independently; the personal, context-dependent nature of student advising and placement remains largely human-driven in practice. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CRM and chatbot tools exist for basic inquiry handling and initial outreach, but no deployed product manages full recruitment, registration, and placement counseling for postsecondary faculty roles. |
Prepare and deliver lectures to undergraduate or graduate students on topics such as crop production, plant genetics, and soil chemistry.
25CI 25–25 · exposure 25 · augmentation 75 · importance 4.1/5 · click for rater detail
Prepare and deliver lectures to undergraduate or graduate students on topics such as crop production, plant genetics, and soil chemistry.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Despite some experimental adoption of AI-generated lecture materials in higher education, the pace remains slow and limited to content support rather than delivery replacement. Academic institutions are conservative adopters of automation in core instructional roles due to quality, liability, and credential concerns. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adoption of AI for teaching is still in early pilot stages with cautious institutional policies, especially in applied science fields like agriculture. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly assist instructors by drafting lecture outlines, generating example problems, providing background research summaries, and helping prepare visual aids. These tools demonstrably raise instructor productivity in preparation phases while the professor retains full control over content, delivery, and student interaction. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools significantly help instructors draft lecture content, create visuals, generate quizzes, and summarize research, meaningfully boosting preparation efficiency while the instructor still delivers the lecture. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate lecture content and outlines on agricultural sciences topics, delivering engaging live lectures to students requires real-time classroom management, adaptive questioning, and nuanced interaction that current AI systems cannot reliably perform end-to-end. Preparation could be partially automated, but the delivery and pedagogical core remain beyond current capability at the 50% time-saving bar. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft lecture content and slides, but live delivery, adapting to student questions, and classroom presence require human judgment and interaction that current AI cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Universities have institutional requirements for faculty credentials, tenure structures, and accreditation standards that mandate human instruction. Students expect human expertise and live interaction, and institutional policy strongly protects the role of the human instructor in formal education. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Postsecondary teaching typically requires credentialed faculty with subject expertise, institutional accreditation standards, and student expectations of human instruction, creating strong structural barriers to full replacement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI systems for content generation and slide preparation cost less per unit than a professor's time, but the human professor remains necessary for delivery, interaction, and credibility. The all-in cost of AI-assisted preparation plus human delivery does not achieve order-of-magnitude savings versus a professor working alone. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI can cheaply generate lecture outlines and materials, the human costs of actual delivery, mentoring, and institutional accreditation remain dominant, keeping overall cost comparable to a human instructor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform full lecture delivery at university scale. AI can draft lecture notes and slides, but no production system autonomously delivers lectures with the academic credibility, adaptive engagement, and field expertise that institutional contexts require. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Tools like ChatGPT and lecture-generation platforms exist for content drafting, but no deployed product reliably delivers full postsecondary lectures autonomously in production classrooms. |
Keep abreast of developments in the field by reading current literature, talking with colleagues, and participating in professional conferences.
23CI 10–35 · exposure 17 · augmentation 63 · importance 4.4/5 · click for rater detail
Keep abreast of developments in the field by reading current literature, talking with colleagues, and participating in professional conferences.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Despite high digitization in education, this particular task—maintaining personal expertise through reading and professional engagement—remains firmly rooted in individual professional responsibility and human judgment, with negligible actual displacement in practice. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adopts AI tools unevenly and cautiously for scholarly currency tasks; academic culture still emphasizes traditional methods of staying current. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by filtering and summarizing literature, alerting to relevant developments, or organizing conference schedules and abstracts. However, the core activities of critical reading, evaluation, and professional dialogue remain human-centered, limiting the transformative potential of AI augmentation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI literature search, summarization, and alert tools significantly speed up staying current with publications, meaningfully augmenting this task even though it doesn't replace networking activities. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires human judgment about relevance, critical evaluation of scientific developments, and meaningful professional dialogue. While AI can summarize literature or flag new papers, it cannot replicate the selective attention, contextual judgment, and active professional engagement (conversations, conference participation) that 'keeping abreast' demands. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help surface and summarize relevant literature, but the core task of building professional judgment, networks, and tacit knowledge through conferences and colleague discussions is not automatable end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task is protected by strong professional and institutional norms: teachers are expected to maintain their own expertise, and professional development is often contractually required or embedded in institutional culture. Academic freedom and professional credibility depend on individual judgment and engagement, creating high friction against delegation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier, but tenure and promotion systems place high organizational value on demonstrated engagement via conferences and peer interaction, creating some resistance to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The human teacher must perform this task themselves to maintain expertise and professional standing. AI tools that assist (literature alerts, summaries) are supplementary and do not replace the core activity, making the comparison of direct substitution costs moot and showing no cost advantage for automation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI literature tools are cheap for summarization, but the full task includes travel, conferences, and human networking that AI cannot replace, so overall cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Current AI can assist with literature summarization and alerting, but deployed products do not reliably capture the nuanced professional discourse, networking, and judgment required. Conference participation and colleague conversations remain inherently human activities that AI cannot perform. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Tools like literature summarization assistants and research alert services exist and are used, but no product substitutes for the networking and conference participation components of this task. |
Provide professional consulting services to government or industry.
21CI 18–25 · exposure 20 · augmentation 63 · importance 3.1/5 · click for rater detail
Provide professional consulting services to government or industry.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Consulting services remain a high-touch, relationship-driven offering with strong client preference for qualified human experts. Adoption of AI-only consulting in government and industrial advisory contexts is negligible; AI is used only as a support tool for existing human consultants. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Academic and specialized agricultural consulting sectors show slower AI adoption compared to fast-moving digital-first professional services like finance or general business consulting. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist consulting by automating literature reviews, synthesizing data, and drafting initial analysis or recommendations that the expert refines. However, augmentation is limited to backstage preparation; the client engagement and final advisory sign-off remain human-centric. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist in literature review, data analysis, drafting reports, and generating draft recommendations, significantly boosting the productivity of a professor providing consulting services. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Consulting requires deep domain expertise, stakeholder relationship management, and contextual judgment that AI cannot replicate end-to-end today. While AI can assist with research synthesis and report drafting, the core advisory function—understanding client needs, building trust, and recommending strategic direction—remains fundamentally human. |
| Task automatability | claude-sonnet-5 | 2/5 | Consulting requires synthesizing domain expertise, judgment, and context-specific recommendations that current AI can support but not independently deliver at professional standard for high-stakes government/industry decisions.rely on AI outputs directly. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Government and industry clients typically demand credentials, professional liability, and legal accountability from consultants; organizations and clients often require a named expert to sign off on recommendations. Regulatory and contractual requirements often make human consultant engagement a hard requirement rather than optional. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Government and industry consulting often requires credentialed expertise, professional reputation, and accountability for recommendations, creating strong barriers against pure AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI can reduce labor cost on research and documentation components, but the fee-based consulting model (often $150–$300+ per hour for postsecondary faculty experts) is difficult to undercut with current AI inference costs when human credibility and accountability remain essential. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI can cheaply draft supporting materials, the actual consulting value—credibility, liability-bearing judgment, stakeholder trust—still requires expensive human expert time, keeping overall cost comparable to human-only delivery. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs consulting engagement end-to-end. AI tools exist for research and writing support, but autonomous consulting—scoping problems, delivering actionable recommendations, and managing client relationships—is not a production capability in commercial systems. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools can generate research summaries or draft reports, but no deployed product independently provides trusted professional consulting advice to institutions in this specialized agricultural science domain. |
Initiate, facilitate, and moderate classroom discussions.
17CI 14–20 · exposure 16 · augmentation 50 · importance 4.0/5 · click for rater detail
Initiate, facilitate, and moderate classroom discussions.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Higher education adoption of AI for live classroom facilitation remains minimal; institutions continue to employ human instructors for this core function, with AI use limited to asynchronous supplemental roles rather than driving displacement in production. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education has been slow and cautious in adopting AI for live instructional delivery, with most current use confined to administrative or supplementary tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating discussion prompts, transcribing and organizing conversation threads, and flagging engagement patterns, which helps instructors prepare and reflect on discussions, but the human instructor remains central to real-time moderation and group dynamics. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help instructors prepare discussion questions, summarize readings, or provide real-time chat-based prompts, offering moderate productivity gains without replacing the human moderator. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can help draft discussion prompts and summarize points, but real-time facilitation and moderation require responsive judgment, conflict resolution, and dynamic engagement with student emotions and group dynamics that current systems cannot reliably replicate in live classroom settings. |
| Task automatability | claude-sonnet-5 | 2/5 | Leading and moderating live classroom discussion requires real-time reading of student engagement, adaptive questioning, and subject expertise that current AI cannot reliably replicate end-to-end in a physical classroom setting. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Institutional and pedagogical barriers are substantial: faculty autonomy and student expectations center on human instruction, accreditation standards emphasize instructor-led dialogue, and liability concerns around AI handling sensitive student interactions create friction against substitution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Accreditation and instructional norms in postsecondary education require a qualified instructor to lead discussion; institutional and pedagogical expectations create strong barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI infrastructure, moderation oversight, and error recovery (failed discussions, student disengagement) exceeds the loaded wage of a postsecondary instructor for this specific function, especially when quality parity is required. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Even where AI-assisted discussion tools exist, human faculty presence is still required, so AI adds cost as a supplement rather than substituting the wage-bearing task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably facilitates live classroom discussions autonomously; AI chatbots can answer questions in educational settings but cannot genuinely moderate peer-to-peer discussion with adaptive social awareness at the level required for classroom pedagogical value. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously runs live in-person postsecondary classroom discussions; AI is used at most for prep materials or discussion prompts, not moderation itself. |
Supervise laboratory sessions and field work and coordinate laboratory operations.
15CI 5–25 · exposure 13 · augmentation 38 · importance 4.2/5 · click for rater detail
Supervise laboratory sessions and field work and coordinate laboratory operations.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational institutions have been slow to automate supervision tasks; while colleges use learning management systems for some coordination, they remain conservative on replacing or fully automating human oversight of hands-on laboratory and field activities due to regulatory and safety constraints. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Higher education agricultural science departments show minimal adoption of AI for physical lab/field supervision, a highly analog task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could meaningfully assist by automating scheduling, generating lab reports, flagging safety concerns from sensor data, or managing equipment inventory, thereby freeing the supervisor to focus on student guidance and safety—but these are supporting functions rather than transformative to the core supervision role. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help with scheduling, equipment logs, or generating lab protocols, but offers little assistance during the actual supervisory presence needed. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with scheduling and documentation of lab sessions, the core task—supervising students in real-time, ensuring safety, providing hands-on guidance, and responding to unpredictable field conditions—requires human presence and judgment that current systems cannot deliver end-to-end with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically supervising students in labs and fields requires real-time presence, safety oversight, and hands-on demonstration that current AI cannot perform. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: institutional liability and duty of care for student safety, accreditation standards that likely mandate human supervision of lab work, and legal accountability for accidents or misconduct—all of which require a qualified human supervisor to be present and responsible. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety regulations, liability for lab/field accidents, and institutional requirements for qualified faculty oversight create strong barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Supervising lab and field work is inherently labor-intensive due to safety and liability requirements; AI tools that could aid scheduling or data collection would save only a fraction of the full task cost, while the supervisor's loaded wage remains the dominant cost. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system capable of replacing this supervisory role, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI system reliably supervises laboratory or field work sessions independently; existing tools support scheduling and record-keeping but cannot replace the live oversight, safety monitoring, and adaptive instruction that this task demands. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a human supervisor in agricultural lab or field settings; this remains entirely in-person work. |
Collaborate with colleagues to address teaching and research issues.
13CI 5–21 · exposure 5 · augmentation 38 · importance 4.1/5 · click for rater detail
Collaborate with colleagues to address teaching and research issues.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Higher education, especially research-focused agriculture programs, have shown slow adoption of AI for core academic functions. Collaboration and research governance remain heavily manual and human-centered, with minimal displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education is a moderate-to-slow adopter of AI for interpersonal governance and collegial work, with pilots mostly focused on content generation rather than collaboration itself. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can marginally assist by preparing background documents or summarizing prior discussions, but the deliberative, interpersonal core of addressing teaching and research issues remains dependent on human judgment and cannot be meaningfully augmented by current systems. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can help prepare materials, summarize research, or draft communications that support collaborative discussions, offering moderate assistance without replacing the interactive process. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Collaboration on teaching and research issues fundamentally requires human judgment, relationship negotiation, and contextual understanding of institutional politics and academic nuance. Current AI cannot participate meaningfully in the deliberative, interpersonal work that defines such collaboration. |
| Task automatability | claude-sonnet-5 | 1/5 | Collegial collaboration on teaching and research strategy requires relationship-building, institutional context, and shared judgment that current AI cannot perform end-to-end; AI cannot substitute for the human interaction itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong institutional and social barriers protect this task: academic collaboration is built on professional trust and personal relationships, faculty autonomy is legally protected in many jurisdictions, and shared governance structures mandate human participation in research and curriculum decisions. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No legal licensing barrier exists specifically for collaboration, but strong organizational and interpersonal norms make this a human-embedded academic activity resistant to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI tools for drafting or summarizing is low in isolation, but the inability to replace or materially reduce the human collaborative effort means total cost per task output remains dominated by the faculty member's time. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this collaborative task, so cost comparison favors the human by default since AI cannot deliver the output at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can help draft memos or summarize research, no deployed system reliably facilitates genuine collaborative problem-solving on complex academic issues. Current products lack the ability to understand institutional context and earn the trust required to participate authentically in colleague discussions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs collegial collaboration on academic issues; this is fundamentally a human social/professional activity, not a task product suites address. |
Maintain regularly scheduled office hours to advise and assist students.
6CI 0–11 · exposure 0 · augmentation 38 · importance 4.0/5 · click for rater detail
Maintain regularly scheduled office hours to advise and assist students.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Higher education has shown minimal adoption of AI to replace faculty office hours; colleges and universities continue to prioritize and enforce faculty advising and office hour requirements as core functions, reflecting both cultural and regulatory commitment to human-student interaction. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education is a slower-adopting sector for replacing core faculty duties, though AI tutoring supplements are increasingly piloted alongside, not instead of, office hours. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might assist with scheduling, organizing student records, or pre-screening routine questions before an office hour, but these are peripheral to the core task of interpersonal advising and mentoring that remains fundamentally human-centered. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by answering common student questions, drafting materials, or scheduling, freeing time for higher-value interactions during office hours. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires real-time human interaction, relationship-building, and personalized guidance that current AI cannot replicate. Office hours are inherently synchronous, presence-dependent advising where students seek mentorship and individualized counsel that demands human judgment and empathy. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical/synchronous presence and personalized human relationship-building with students, which AI cannot substitute for at equal quality despite chatbots handling some FAQ-style queries.jednou. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Educational institutions have strong pedagogical and duty-of-care expectations that faculty maintain direct student contact. Accreditation standards, student expectations, and institutional policies all embed requirements for human faculty availability and mentorship, creating legal and organizational barriers to substitution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Office hours are often a contractual/accreditation requirement tied to faculty employment and mentorship duties, creating strong institutional and role-based barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task is fundamentally about human presence and relationship; any attempted automation would require either a human staff member (making cost equivalent to the human doing it) or would fail to deliver the actual service, making AI solutions economically irrelevant. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI chat assistance is cheap, the actual task requires a paid faculty member's contracted time regardless of AI use, so cost savings are minimal for this specific institutional requirement. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs office hours as a substitute. While chatbots can answer routine questions, they cannot replicate the advising, mentorship, and rapport-building that constitute the core function of scheduled student office hours. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product replaces a professor's office hours advising role; AI tutoring tools exist but are not substitutes for institutional faculty availability requirements. |
Act as advisers to student organizations.
6CI 0–11 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail
Act as advisers to student organizations.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Higher education remains a slow-adopting sector for core pedagogical and advising functions. Student organization advising is particularly resistant to automation because it is embedded in faculty-student relationships and institutional governance structures that have not moved toward AI displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adopts AI tools slowly for interpersonal/advisory roles, and this specific function has seen essentially no AI displacement or agentic adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist marginally by drafting agendas, organizing meeting notes, or summarizing organizational concerns, but the advising relationship itself depends on human judgment and presence. Augmentation is limited because the task is fundamentally relational rather than information-processing. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with scheduling, communications drafting, or resource suggestions for the organization, but this is a minor slice of what being an adviser entails. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Advising student organizations requires interpersonal judgment, mentorship, and relationship-building that current AI cannot perform end-to-end. While AI can draft communications or summarize organizational issues, the core advisory function—counseling students on strategy, conflict resolution, and development—demands human contextual understanding and cannot meet the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 1/5 | Advising student organizations requires ongoing relational mentorship, judgment, and institutional presence that AI cannot perform end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task carries substantial institutional and professional barriers: student organizations often need face-to-face advising, faculty advisers hold fiduciary and pastoral responsibilities, and there are informal but real expectations that faculty (not AI) provide mentorship and institutional guidance. Regulatory and cultural norms strongly protect human advising. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Institutions typically require a designated faculty/staff adviser for liability, oversight, and mentorship purposes, creating strong organizational and sometimes policy-based barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying and monitoring AI systems to advise student organizations would exceed the cost of faculty time devoted to this task. A faculty member's marginal time cost is typically lower than the infrastructure and oversight required for AI advisory systems. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | There is no viable AI product performing this role, so the comparison is largely moot; any AI assistance would be a minor supplement, not a cost-saving substitute for the human's time commitment. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs student organization advisory at scale. While chatbots can answer routine questions, they cannot serve as genuine advisers capable of understanding organizational dynamics, member concerns, or institutional context that this role requires. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product acts as a substitute human adviser for student clubs/organizations; this remains entirely a human role in practice. |
Supervise undergraduate or graduate teaching, internship, and research work.
4CI 0–7 · exposure 0 · augmentation 38 · importance 4.2/5 · click for rater detail
Supervise undergraduate or graduate teaching, internship, and research work.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Academic institutions are slow adopters of automation in core pedagogical functions; supervision of students and research remains culturally and legally protected as a human faculty responsibility, with minimal AI-driven displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adopts AI tools slowly for high-touch relational tasks like mentorship, though AI is used more for administrative or content-generation subtasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools could assist with administrative tracking of student progress or literature review support, but offer minimal productivity boost to the core supervisory, mentoring, and evaluative judgment that defines this task. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by drafting feedback, tracking progress, summarizing research, or organizing schedules, but the core supervisory judgment remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Supervising teaching, internship, and research work requires sustained human judgment about student progress, mentorship, motivation assessment, and adaptive feedback—all deeply interpersonal and context-dependent. Current AI cannot replicate the evaluative and developmental aspects at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | Supervision requires ongoing personal mentorship, judgment calls on student performance, and relationship-based guidance that current AI cannot execute end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Universities legally require licensed faculty or designated supervisors to oversee student research, internships, and teaching; institutional governance, accreditation standards, and duty-of-care norms create hard barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Institutional accreditation, mentorship norms, and legal/administrative responsibility for student research and safety require a qualified faculty member, creating strong structural barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The full-cost AI inference and oversight for supervising student work, capturing nuance in research mentorship and academic judgment, would remain more expensive or lower-quality than a faculty member already on payroll. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this supervisory function, so no meaningful cost comparison favors AI; human faculty remain the only viable option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs the core supervisory, mentoring, and evaluative functions of this role in production academic settings. Administrative tools exist, but they do not substitute for the human supervisor's assessment and guidance. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product supervises students' teaching, internships, or research; this remains a human faculty responsibility in practice. |
Participate in campus and community events.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.2/5 · 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 human presence and relationship-building central to academic culture; sectors have shown no meaningful adoption of AI for direct event participation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Academic community engagement is a low-digitization, in-person activity with essentially no AI adoption trend. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist with logistical support (event scheduling, attendee communication) or post-event documentation, but these are peripheral to the core task of participatory engagement. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help with scheduling, event planning materials, or communications around the event, but offers little assistance for the actual participation itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Participating in campus and community events requires physical presence, real-time social interaction, relationship-building, and contextual decision-making that current AI cannot perform. No meaningful part of this task can be automated today. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical presence, social interaction, and personal representation at events cannot be performed by current AI systems end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong barriers exist: the task inherently requires a licensed educator's physical presence and direct human engagement with students and community members. Institutional and stakeholder expectations that faculty participate personally are hard to displace. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Personal presence and representation of the institution/department typically require a human faculty member, creating strong organizational and social expectations against substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI has no capability to perform this task, making cost comparison irrelevant; substitution is not feasible at any price point. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can substitute for a human's physical and social presence at events. This task is fundamentally interpersonal and location-dependent, placing it outside the scope of current AI capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product attends or participates in physical campus/community events on behalf of a person. |
Perform administrative duties, such as serving as department head.
1CI 0–3 · exposure 0 · augmentation 38 · importance 3.9/5 · click for rater detail
Perform administrative duties, such as serving as department head.
1| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This is a structural leadership role in higher education institutions; there is zero adoption of AI for autonomous department head functions, and institutional governance makes adoption infeasible. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education administration adopts AI slowly for governance and leadership roles, though it may use AI tools for scheduling or reporting support within the role. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide limited assistance with administrative tasks like document drafting, schedule management, or data analysis, but the core strategic and personnel responsibilities remain fully human; the impact on department head productivity is marginal. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with drafting reports, scheduling, budget summaries, and correspondence that support administrative work, improving efficiency on sub-tasks even though the leadership role itself remains human-held. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Administrative duties of a department head involve strategic decision-making, personnel management, budget oversight, and institutional politics that require human judgment, accountability, and discretionary authority. Current AI systems cannot autonomously assume these responsibilities. |
| Task automatability | claude-sonnet-5 | 1/5 | Department head duties involve interpersonal leadership, personnel decisions, budget authority, and institutional politics that require human judgment, authority, and accountability not replicable by current AI systems end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Department head roles carry explicit legal, fiduciary, and contractual requirements for a human to hold the position, sign off on decisions, and bear accountability. Licensing and human-contact requirements create absolute barriers to substitution. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Department head is a formal institutional appointment carrying legal signing authority, employment decisions, and accreditation responsibilities that require a designated, accountable human employee. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems cannot be deployed as department heads, making cost comparison moot—a human must occupy and be compensated for this role regardless of any AI assistance. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the role itself, so any comparison is moot; at best AI reduces some clerical sub-tasks but the core administrative/leadership function still requires a paid human occupying the position. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs department head duties end-to-end; such roles require legal authority, fiduciary responsibility, and human accountability that cannot be delegated to AI systems in any organization. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product serves as a department head or performs the full scope of academic administrative leadership; this remains firmly a human role in institutions today. |
Serve on academic or administrative committees that deal with institutional policies, departmental matters, and academic issues.
0CI 0–0 · exposure 0 · augmentation 38 · importance 3.4/5 · click for rater detail
Serve on academic or administrative committees that deal with institutional policies, departmental matters, and academic issues.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Higher education has been slow to adopt AI for any core governance functions; committee work remains exclusively human-staffed across institutions. No evidence of adoption toward automating committee participation exists. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Higher education governance and committee work show essentially no AI displacement; this remains a human-only institutional function. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by drafting meeting agendas, summarizing prior discussions, or preparing policy analyses, but these are peripheral to the core act of committee service. The incremental productivity gain is modest given that faculty already delegate much of this support work. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help draft meeting agendas, summarize documents, or prepare policy briefs for committee members, offering moderate assistance to preparation but not the deliberative task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Committee service requires contextual judgment, consensus-building, and institutional knowledge that demand human deliberation. AI cannot authentically participate in policy discussions, vote, or represent stakeholder interests in ways that satisfy institutional governance norms. |
| Task automatability | claude-sonnet-5 | 1/5 | Committee service requires human judgment, institutional politics, relationship navigation, and accountable decision-making that AI cannot perform or represent on someone's behalf. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Academic governance is legally and structurally tied to human faculty participation; committees must be staffed by credentialed humans who can be held accountable for decisions. Institutional bylaws, accreditation standards, and fiduciary obligations all require human decision-makers. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Committee membership typically requires faculty status, institutional authorization, and formal governance roles that only credentialed humans can hold. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Committee service is a governance duty typically assigned to salaried faculty as part of their role; automating it would require either hiring AI as a committee member (not feasible) or having humans perform the task anyway for legitimacy, making AI more expensive than the status quo. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI product performing this role, so cost comparison favors the human by default since AI cannot substitute at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed system can reliably serve on actual committees or make binding institutional decisions. While AI can draft position papers or summarize issues, it cannot perform the core work of committee participation—deliberating, advocating, and deciding on behalf of an institution. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a human's presence and voting/deliberative role on academic committees; this is purely a research-stage or nonexistent capability. |
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.