Health Specialties Teachers, Postsecondary

25-1071.00
Median wage $107,310/yr221,270 employed (US)Rank #377 of 923 scored · top 41% by substitution

Teach courses in health specialties, in fields such as dentistry, laboratory technology, medicine, pharmacy, public health, therapy, and veterinary medicine.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution30
Exposure28
Augmentation62

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

22 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

14%

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%29

panel mean rating 2.1/5 → substitution pressure 29/100

Cost vs. human wagew 15%33

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

Adoption barriersw 20%inverted — strong barriers lower the score36

panel mean rating 3.6/5 (barrier strength) → substitution pressure 36/100

Sector adoption velocityw 10%30

panel mean rating 2.2/5 → substitution pressure 30/100

Task breakdown (22 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.

86

CI 76–95 · exposure 92 · augmentation 75 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510015/5Educational institutions have already adopted LMS and SIS platforms universally; record automation is a core, mature function with broad institutional deployment.
Sector adoption velocityclaude-sonnet-55/5Higher education has near-universal adoption of digital LMS and grade management systems already embedded in daily academic operations.
Augmentation potentialclaude-haiku-4-5-202510014/5AI assists by auto-populating grades from assessments, flagging attendance patterns, generating summary reports, and reducing manual data entry burden, substantially raising instructor productivity while they retain verification and decision authority.
Augmentation potentialclaude-sonnet-54/5AI-enhanced LMS tools help instructors track, flag, and analyze attendance/grade trends, improving efficiency while instructors retain oversight and final authority.
Task automatabilityclaude-haiku-4-5-202510014/5Attendance and grade record maintenance is highly structured data entry and retrieval that can be nearly fully automated through learning management systems, student information systems, and AI-assisted data processing, easily achieving >50% time savings with equal accuracy.
Task automatabilityclaude-sonnet-55/5Recording attendance and grades is a structured data-entry/computation task fully handled by existing LMS and gradebook software with automated calculation and reporting.
Adoption barriersclaude-haiku-4-5-202510014/5Institutional record-keeping requirements and FERPA compliance impose regulatory and legal oversight, and educational institutions typically require human sign-off or institutional validation of records, creating meaningful friction against full automation.
Adoption barriersclaude-sonnet-52/5Some institutional policy requires instructor sign-off on final grades, but routine record maintenance itself faces minimal regulatory or licensing barriers.
Cost vs. human wageclaude-haiku-4-5-202510015/5Per-task inference cost for database queries and automated record logging is negligible relative to even part-time instructor wages, representing orders of magnitude savings once systems are deployed.
Cost vs. human wageclaude-sonnet-55/5Automated record-keeping software costs a small fraction of the faculty/administrative time it replaces, especially at scale across many students and courses.
Technical feasibility todayclaude-haiku-4-5-202510015/5Mature, production-ready systems (Canvas, Blackboard, Banner, Workday) perform this reliably at scale in thousands of institutions; these are standard institutional infrastructure.
Technical feasibility todayclaude-sonnet-55/5Mature, widely deployed LMS platforms (Canvas, Blackboard, PowerSchool, etc.) already perform gradebook management and attendance tracking reliably at scale in universities.

Prepare course materials, such as syllabi, homework assignments, and handouts.

80

CI 76–84 · exposure 75 · augmentation 100 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Higher education and EdTech sectors show rapid adoption of AI for material preparation; faculty surveys and institutional pilots indicate widespread use of generative AI for syllabus and assignment drafting, consistent with high-digitization professional services adoption patterns.
Sector adoption velocityclaude-sonnet-53/5Higher education is adopting generative AI for content creation at a moderate pace, with many pilots and increasing informal faculty use but no systematic institutional deployment yet.
Augmentation potentialclaude-haiku-4-5-202510015/5AI dramatically amplifies instructor productivity by generating drafts, variations, and formats that instructors refine and customize. The human remains in control while AI handles time-intensive document generation and iteration, exemplifying high-value augmentation.
Augmentation potentialclaude-sonnet-55/5AI tools substantially speed up drafting of syllabi, assignments, and handouts while faculty retain control over pedagogical accuracy, learning objectives, and final content.
Task automatabilityclaude-haiku-4-5-202510014/5Current AI can generate syllabi, homework assignments, and handouts at high quality with minimal human input, easily meeting the 50% time-saving bar. LLMs excel at structured document creation, content organization, and formatting, requiring only subject matter specification and light review.
Task automatabilityclaude-sonnet-54/5AI can draft syllabi, homework problems, and handouts from a course outline or textbook, requiring mainly human review and customization, meeting the time-saving threshold for most of this drafting work.9
Adoption barriersclaude-haiku-4-5-202510011/5No licensing requirement, legal mandate, or regulatory barrier prevents AI use for preparing instructional materials. Institutions adopt freely; only organizational friction and faculty preference for manual control create soft friction, not hard barriers.
Adoption barriersclaude-sonnet-52/5No licensing requirement governs authorship of course materials, though institutional policies, accreditation standards, and instructor accountability for content accuracy create mild friction.
Cost vs. human wageclaude-haiku-4-5-202510015/5AI inference cost for generating comprehensive course materials is negligible (pennies per document), while instructor time to create equivalent materials costs substantially more (hours at professional wages). The cost differential is well over an order of magnitude.
Cost vs. human wageclaude-sonnet-55/5Generating draft course materials via an LLM costs a few cents to dollars in compute versus hours of faculty or TA time at academic wages, an order-of-magnitude cost advantage.
Technical feasibility todayclaude-haiku-4-5-202510014/5Mature products (Claude, ChatGPT, specialized educational tools) reliably generate course materials in production use. Educational institutions and individual instructors deploy these systems at scale for material preparation, though human review remains standard practice.
Technical feasibility todayclaude-sonnet-54/5Deployed LLM tools (ChatGPT, Copilot, dedicated ed-tech platforms) are widely used by instructors today to generate syllabi templates, quizzes, and handouts, though instructors still edit for accuracy and alignment with course goals.

Compile bibliographies of specialized materials for outside reading assignments.

73

CI 65–81 · exposure 70 · augmentation 88 · importance 3.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510013/5Academic institutions are adopting AI tools for course support, but adoption remains patchy and often experimental rather than systematic replacement. Higher education lags some sectors in automation deployment, though awareness is rising rapidly.
Sector adoption velocityclaude-sonnet-52/5Higher education and academic teaching, especially in specialized health fields, tends to adopt AI tools slowly and unevenly for course prep tasks despite general availability of AI research tools.
Augmentation potentialclaude-haiku-4-5-202510014/5AI is highly effective at augmenting faculty work by rapidly generating initial reading lists, filtering sources by relevance, and handling tedious formatting, freeing the professor to focus on pedagogical curation and thematic coherence of assignments.
Augmentation potentialclaude-sonnet-55/5AI substantially speeds up literature search, summarization, and citation formatting, letting instructors quickly build and refine reading lists while retaining final judgment on relevance and rigor.
Task automatabilityclaude-haiku-4-5-202510014/5AI systems can reliably search academic databases, extract citations, format references, and compile reading lists with minimal errors using current tools. The task is largely mechanical—identifying relevant sources, organizing them, and standardizing formats—where AI can achieve >50% time savings at equal quality without significant human intervention.
Task automatabilityclaude-sonnet-54/5AI systems can search literature databases, identify relevant specialized materials, and generate formatted bibliographies with high time savings, especially for standard citation compilation.4 rather than 5 because domain-specific curation for advanced health specialties may still need expert vetting for accuracy and relevance.
Adoption barriersclaude-haiku-4-5-202510012/5Few legal or regulatory barriers exist; bibliographies can be generated and reviewed by the teacher before assignment. The main friction is organizational inertia and faculty preference to curate materials themselves, but nothing prevents substitution.
Adoption barriersclaude-sonnet-51/5There is no licensing, liability, or regulatory requirement tying bibliography compilation to a credentialed human; it's a low-stakes administrative/academic task.
Cost vs. human wageclaude-haiku-4-5-202510015/5The marginal cost of AI-generated bibliography compilation (inference + API calls) is far less than the hourly wage of a health specialties professor or librarian assembling the same list manually, likely a 10–100× cost advantage.
Cost vs. human wageclaude-sonnet-54/5AI-assisted literature search and bibliography generation is dramatically cheaper than a faculty member manually compiling reading lists, though some oversight time is still needed to verify accuracy.
Technical feasibility todayclaude-haiku-4-5-202510014/5Deployed AI tools (ChatGPT, academic search APIs, reference management software with AI features) can compile bibliographies reliably today. Products like Semantic Scholar, Elicit, and LLM-powered citation tools are used in production by researchers and educators with demonstrated reliability for this narrow, well-defined task.
Technical feasibility todayclaude-sonnet-53/5Tools like reference managers with AI features, literature search assistants, and citation generators exist and are used, but hallucinated or incorrect citations remain a known reliability issue requiring human verification.

Compile, administer, and grade examinations, or assign this work to others.

52

CI 50–54 · exposure 50 · augmentation 75 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510013/5Higher education is in the pilot-to-early-production phase with LMS automation and some AI grading tools, but adoption remains uneven across institutions and disciplines, with slower uptake in professional health programs that prioritize human judgment.
Sector adoption velocityclaude-sonnet-53/5Higher education is adopting AI grading and content tools at a moderate pace, with pilots common but full-scale institutional deployment still limited, especially in specialized health fields.
Augmentation potentialclaude-haiku-4-5-202510014/5AI substantially assists instructors by rapidly generating exam questions, flagging inconsistent grading patterns, providing item analysis, and handling bulk objective grading, freeing faculty to focus on complex feedback and assessment design.
Augmentation potentialclaude-sonnet-54/5AI substantially assists in drafting exam questions, generating rubrics, and providing first-pass grading feedback, meaningfully speeding up instructor workflow while they retain final control.
Task automatabilityclaude-haiku-4-5-202510013/5AI can automate significant parts of exam creation, administration of objective tests, and grading of multiple-choice/short-answer formats, but human oversight is typically needed for complex subjective assessments and for ensuring academic integrity compliance—achieving roughly 50% time savings in a typical workflow.
Task automatabilityclaude-sonnet-53/5AI can generate question banks, grade objective and even some short-answer exams with rubrics, but compiling exams aligned to specific course learning objectives and grading nuanced clinical reasoning still needs faculty oversight.
Adoption barriersclaude-haiku-4-5-202510013/5Faculty autonomy preferences, institutional quality-assurance policies, and accreditation requirements around assessment rigor create meaningful friction, though no hard legal barrier prevents delegating exam administration and grading to AI-assisted systems.
Adoption barriersclaude-sonnet-53/5Academic policies often require instructor accountability for grades and exam content, especially in accredited health programs, creating moderate institutional and accreditation-related friction.
Cost vs. human wageclaude-haiku-4-5-202510014/5AI-based grading and exam administration tools cost substantially less than paying faculty or teaching assistants for full exam cycles, though oversight costs and integration overhead prevent reaching a full order-of-magnitude advantage.
Cost vs. human wageclaude-sonnet-53/5AI-assisted grading and question generation can cut time substantially, but human review for accuracy and academic integrity keeps costs only moderately below fully manual grading.
Technical feasibility todayclaude-haiku-4-5-202510013/5Mature products exist (learning management systems with automated grading, AI-powered assessment platforms) and are deployed in educational institutions, but error rates on nuanced grading and limitations in handling diverse question types prevent full reliability across all exam contexts.
Technical feasibility todayclaude-sonnet-53/5Products like exam-generation tools and AI graders (e.g., Gradescope AI assistance, LLM-based grading) are deployed in some institutions but are not universally reliable for high-stakes health science assessments.

Evaluate and grade students' class work, assignments, and papers.

51

CI 48–54 · exposure 50 · augmentation 75 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510013/5Higher education has adopted plagiarism detection and automated quiz grading widely, but clinical specialties move more cautiously on high-stakes assessment; pilots of AI-assisted grading are growing but production-level adoption for complex clinical work remains limited.
Sector adoption velocityclaude-sonnet-52/5Higher education, especially specialized health science programs, has been slower and more cautious in adopting AI grading tools compared to fast-moving sectors like tech or finance.
Augmentation potentialclaude-haiku-4-5-202510014/5AI can significantly assist faculty by automating initial feedback on structure, accuracy, and common errors, generating rubric-aligned comments, and surfacing outliers for deeper review; this allows instructors to focus on high-level clinical reasoning assessment and mentoring rather than low-level feedback generation.
Augmentation potentialclaude-sonnet-54/5AI tools can meaningfully speed up initial review, flag issues, generate draft feedback, and check for plagiarism, significantly aiding instructors while they remain the final evaluators.
Task automatabilityclaude-haiku-4-5-202510013/5AI can automate significant portions of grading for objective or formulaic work (e.g., multiple-choice, basic technical assignments, plagiarism detection, grammar/style feedback), but subjective evaluation of deeper clinical reasoning, clinical judgment, and complex writing quality typically requires human expertise, falling short of the 50% time-saving-at-equal-quality bar for the full task.
Task automatabilityclaude-sonnet-53/5AI can draft grades and feedback for objective or structured assignments (quizzes, short answers) but grading complex clinical case papers or nuanced written work still requires domain expertise and judgment to reach equal quality reliably.
Adoption barriersclaude-haiku-4-5-202510013/5Institutional policies, accreditation standards (e.g., ACPE, CCNE), and faculty responsibility for grading may create moderate friction; additionally, clinical educators often have contractual and professional expectations to perform assessment themselves, though no single hard legal bar exists for routine assignment grading.
Adoption barriersclaude-sonnet-53/5No strict licensing requirement for grading itself, but institutional academic integrity policies, accreditation standards, and instructor accountability create moderate friction against full automation.
Cost vs. human wageclaude-haiku-4-5-202510014/5AI inference and integration costs are low compared to faculty labor, especially if applied to routine assignments and initial feedback; however, the need for human review of high-stakes clinical assessments limits the cost advantage to perhaps 3–5× rather than 10×.
Cost vs. human wageclaude-sonnet-53/5AI can cut grading time substantially for standardized items, but oversight and verification for clinical accuracy narrows the cost advantage, making it roughly comparable rather than order-of-magnitude cheaper.
Technical feasibility todayclaude-haiku-4-5-202510013/5Mature LLM-based grading tools and plagiarism detection systems exist and are used in production, but they show material error rates on nuanced work, struggle with domain-specific clinical reasoning, and require significant human oversight; they work reliably only for narrow, well-structured assignments.
Technical feasibility todayclaude-sonnet-53/5AI grading assistants and plagiarism/rubric-based tools are deployed in some universities, but for health specialties with technical/clinical accuracy requirements, error rates remain material and human review is standard.

Keep abreast of developments in the field by reading current literature, talking with colleagues, and participating in professional conferences.

47

CI 41–52 · exposure 30 · augmentation 88 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Universities and medical education programs have already begun deploying AI literature alerts, personalized recommendation systems, and conference scheduling tools. Adoption is accelerating in higher-education institutions, which tend to be digitally advanced and willing to experiment with productivity tools.
Sector adoption velocityclaude-sonnet-53/5Academic and research settings show moderate uptake of AI tools like summarization and alert services, though full replacement of professional engagement remains rare.
Augmentation potentialclaude-haiku-4-5-202510015/5AI significantly enhances this task by automatically curating relevant literature, summarizing papers, identifying key trends, and organizing conference materials—allowing educators to stay current far more efficiently while human judgment remains central to evaluating significance and applying findings to teaching.
Augmentation potentialclaude-sonnet-54/5AI tools can significantly speed up literature discovery, summarization, and trend-spotting, meaningfully augmenting a professional's ability to stay current.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can summarize recent literature and flag relevant papers, the task fundamentally requires human judgment about what developments matter, synthesis across disparate sources, and meaningful collegial dialogue—none of which AI can fully replace autonomously. The networking and professional judgment components are difficult to automate at scale.
Task automatabilityclaude-sonnet-52/5AI can help surface and summarize literature, but the core task of ongoing professional engagement, judgment about relevance, and networking with colleagues requires sustained human involvement rather than end-to-end automation.'
Adoption barriersclaude-haiku-4-5-202510012/5There are no legal or regulatory barriers preventing automated literature monitoring, and no requirement that a licensed educator personally perform every aspect of staying current. However, institutional norms and the value placed on human professional networks create some organizational friction.
Adoption barriersclaude-sonnet-52/5No licensing barrier prevents AI-assisted literature review, but professional norms and accreditation expectations still favor active human engagement in the field.
Cost vs. human wageclaude-haiku-4-5-202510014/5AI-assisted literature monitoring is relatively inexpensive (subscription services, API costs), whereas a human dedicating hours to this task carries high loaded labor costs. The cost of AI tools is orders of magnitude lower for the reading/filtering component, though human time for synthesis and networking remains necessary.
Cost vs. human wageclaude-sonnet-53/5AI literature aggregation tools are cheap relative to a professor's time spent reading, but the task also includes non-automatable components like attending conferences and networking, keeping overall cost comparable.
Technical feasibility todayclaude-haiku-4-5-202510013/5AI systems like literature summarization tools, paper recommendation engines, and conference content aggregators exist and are used in production, but they typically require significant human curation to identify truly relevant developments and cannot replicate the value of in-person professional conversations.
Technical feasibility todayclaude-sonnet-52/5Tools like literature summarizers and alerting services exist and are used, but no product autonomously 'keeps abreast' in the full sense including conference participation and colleague discussion.'

Write grant proposals to procure external research funding.

40

CI 25–55 · exposure 38 · augmentation 88 · importance 3.8/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Higher education and research institutions remain conservative on delegating grant writing to AI, with limited production adoption. Most use remains experimental (pilots, drafting aids) rather than routine delegation of the full task.
Sector adoption velocityclaude-sonnet-53/5Academic and research settings show growing but uneven adoption of AI writing tools for grants, with many institutions still cautious due to originality and funder policy concerns.
Augmentation potentialclaude-haiku-4-5-202510014/5AI excels at augmenting grant writers—drafting literature reviews, refining language, identifying gaps in preliminary data descriptions, and reformatting for compliance. These assistive functions measurably speed production while the human PI retains full strategic and credibility control.
Augmentation potentialclaude-sonnet-55/5AI substantially speeds up drafting literature reviews, structuring proposals, editing prose, and generating budget justifications, giving researchers a strong productivity boost while they retain control over scientific content and strategy.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can draft sections of grant proposals (e.g., literature reviews, methodology descriptions), the task requires deep domain knowledge, strategic alignment with funder priorities, novel research positioning, and credibility establishment that current systems struggle to provide reliably. End-to-end automation meeting the 50% time-saving threshold is not yet demonstrated in production.
Task automatabilityclaude-sonnet-53/5AI can draft substantial portions of grant text (background, methods framing, budget justification language) given inputs, but synthesizing novel research ideas, aligning with funder priorities, and ensuring scientific rigor still requires significant human expertise and revision.
Adoption barriersclaude-haiku-4-5-202510014/5Institutional prestige, PI reputation, and research originality are tied to the grant narrative; many funders and institutions expect human accountability and authorship. Legal liability for misrepresented credentials or methods, plus the high stakes of funding decisions, create organizational friction against full automation.
Adoption barriersclaude-sonnet-52/5No licensing requirement bars AI use, but institutional research offices, PI accountability for scientific claims, and funder expectations of PI authorship create moderate friction against full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5The cost of AI systems plus oversight, revision, and domain-expert review approaches or exceeds the cost of a research administrator or grant writer spending several days on a proposal, especially considering reputational risk if AI-generated errors undermine credibility.
Cost vs. human wageclaude-sonnet-53/5AI drafting tools are cheap relative to faculty time, but the human oversight, fact-checking, and strategic input needed for competitive proposals keeps the effective cost comparable rather than order-of-magnitude cheaper.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed product reliably writes complete, competitive grant proposals. AI tools exist for drafting components and editing, but production grant-writing remains human-driven; success rates and funder acceptance are not demonstrated at scale with full automation.
Technical feasibility todayclaude-sonnet-53/5Products like ChatGPT, Claude, and specialized grant-writing assistants are used in production by researchers today, but outputs require heavy editing for accuracy, specificity, and compliance with funder formatting/requirements.

Participate in student recruitment, registration, and placement activities.

30

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

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5While higher education uses administrative software for registration, actual recruitment and placement activities remain heavily human-driven. Adoption of AI agents for these core functions is still in pilot phases across most institutions, not yet at production scale.
Sector adoption velocityclaude-sonnet-52/5Higher education administrative processes are adopting AI tools slowly, with pilots in admissions chatbots and CRM systems but limited deep integration into faculty-driven recruitment and placement workflows.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can meaningfully assist faculty and staff by automating administrative scheduling, preliminary candidate screening, and data management, thereby freeing human effort for the high-value relationship and judgment portions of recruitment and placement.
Augmentation potentialclaude-sonnet-53/5AI can meaningfully assist with drafting recruitment materials, managing registration data, and matching students to placements, improving efficiency while faculty retain relationship and decision-making roles.
Task automatabilityclaude-haiku-4-5-202510012/5Student recruitment and placement involve relationship-building, contextual judgment about fit, and interpersonal persuasion—tasks poorly suited to full automation today. AI can assist with administrative components (data entry, email scheduling, initial screening), but the core recruitment and placement conversations require human judgment and trust.
Task automatabilityclaude-sonnet-52/5This task combines interpersonal outreach, evaluative judgment on candidate fit, and administrative coordination that current AI cannot fully replace end-to-end, though parts like scheduling or initial screening could be assisted.
Adoption barriersclaude-haiku-4-5-202510013/5Educational institutions have significant organizational and regulatory friction around student recruitment and placement—accreditation requirements, privacy laws, and institutional preference for human advisors managing these relationships create meaningful (though not insurmountable) adoption barriers.
Adoption barriersclaude-sonnet-53/5No strict licensing requirement mandates faculty involvement, but institutional norms, accreditation expectations, and the personal nature of academic placement and mentorship create moderate friction against full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5Current AI tools for recruitment and registration carry integration and oversight costs that approach or exceed the cost of human coordinators performing these tasks, particularly when relationship quality and liability are factored in.
Cost vs. human wageclaude-sonnet-52/5While software can handle routine registration logistics cheaply, the human judgment and relationship-building components of recruitment and placement still require costly faculty/staff time, keeping overall cost savings modest.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed systems reliably perform end-to-end recruitment, registration, and placement at the quality needed for educational contexts. While CRM and registration software exists, the relationship and judgment-intensive aspects of recruitment and placement placement remain primarily human-driven in practice.
Technical feasibility todayclaude-sonnet-52/5Some university systems use chatbots for recruitment inquiries and CRM tools for registration tracking, but these are narrow point solutions, not comprehensive replacements for faculty involvement in these activities.

Select and obtain materials and supplies, such as textbooks and laboratory equipment.

30

CI 25–35 · exposure 25 · augmentation 50 · importance 3.8/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Higher education procurement remains fragmented and heavily manual with low tech adoption; most institutions rely on legacy systems and human specialists rather than AI-driven procurement agents.
Sector adoption velocityclaude-sonnet-52/5Higher education administrative and curricular functions have seen slow AI adoption compared to fields like finance or software, with pilots for course planning tools still uncommon in mainstream use.
Augmentation potentialclaude-haiku-4-5-202510013/5AI could usefully assist by searching supplier catalogs, comparing product specifications, and flagging cost-effective alternatives, allowing faculty to focus on evaluating pedagogical fit rather than manual sourcing work.
Augmentation potentialclaude-sonnet-53/5AI can meaningfully assist with recommending textbooks, comparing lab equipment specs, and summarizing options, improving efficiency of the selection process even though procurement remains human-driven.
Task automatabilityclaude-haiku-4-5-202510012/5While AI could assist with inventory searches and supplier identification, the task involves human judgment about educational fit, budget constraints, and physical procurement decisions that require domain expertise and organizational context that current systems cannot fully automate end-to-end.
Task automatabilityclaude-sonnet-52/5AI can help identify and compare textbooks or equipment options but cannot independently select institution-specific materials, negotiate procurement, or physically obtain lab supplies.pipeline requires human judgment and physical action, limiting time savings below the 50% threshold for full automation.
Adoption barriersclaude-haiku-4-5-202510014/5Institutional procurement policies, budget approval chains, vendor relationships, and curriculum alignment requirements create significant friction; moreover, educators typically have contractual or policy obligations to participate in material selection for their programs.
Adoption barriersclaude-sonnet-52/5No licensing requirement blocks AI assistance in selecting materials, but institutional procurement policies, budget approval processes, and physical handling of equipment create organizational friction.
Cost vs. human wageclaude-haiku-4-5-202510012/5The cost of AI systems that integrate supplier databases, specifications, and ordering would likely approach or exceed the labor cost of a specialist doing manual sourcing, given the relatively low volume and high customization typical of academic material procurement.
Cost vs. human wageclaude-sonnet-52/5While AI search/recommendation tools are cheap, the overall task still requires human decision-making, vendor coordination, and physical procurement, so total cost savings versus a human doing this are modest.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed educational procurement system fully automates material selection and ordering for postsecondary health specialties programs; existing tools are fragmented (e-procurement, inventory management) and require human oversight of curriculum alignment and supplier vetting.
Technical feasibility todayclaude-sonnet-52/5There are no deployed products that autonomously handle end-to-end selection and procurement of course materials and lab equipment for postsecondary health instructors; existing tools only assist with research or catalog browsing.

Advise students on academic and vocational curricula and on career issues.

29

CI 25–34 · exposure 25 · augmentation 75 · importance 3.7/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Higher education lags in AI adoption; advising remains a human-centric function with limited pilot deployment of autonomous systems, and institutions prioritize human contact and institutional knowledge.
Sector adoption velocityclaude-sonnet-52/5Higher education has been slow and cautious in adopting AI for personalized advising, with pilots more common than deep production deployment, especially in specialized health fields.
Augmentation potentialclaude-haiku-4-5-202510014/5AI tools (chatbots, curriculum databases, career outcome dashboards) effectively augment advisors by handling routine queries, surfacing options, and freeing time for deeper mentorship, while the advisor retains judgment and relationship responsibility.
Augmentation potentialclaude-sonnet-54/5AI can meaningfully assist faculty by drafting curriculum information, summarizing career pathways, and answering common questions, letting the human focus on personalized judgment and mentorship.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can generate generic academic guidance and career information, advising students on personalized curricula and career paths requires understanding individual aptitudes, constraints, and goals—tasks demanding contextual judgment and relationship-building that current AI performs poorly without substantial human oversight.
Task automatabilityclaude-sonnet-52/5Advising involves interpreting a student's individual goals, transcript, personal circumstances, and institutional nuance, which requires contextual judgment beyond current AI's reliable end-to-end capability, though AI can support parts of the research and drafting.rate
Adoption barriersclaude-haiku-4-5-202510014/5Academic advising is often a credentialed or contractual duty embedded in faculty roles; institutional requirements, accreditation standards, and student-staff relationships create strong organizational and regulatory friction against full substitution.
Adoption barriersclaude-sonnet-53/5No strict licensing requirement for informal advising, but institutional norms, accreditation expectations, and student preference for a human mentor create moderate friction against full substitution.
Cost vs. human wageclaude-haiku-4-5-202510012/5An AI-only advising solution requires significant integration, data infrastructure, and content curation costs; deployed systems still demand human oversight, making the all-in cost comparable to or higher than employing advisors, especially when accounting for liability and trust.
Cost vs. human wageclaude-sonnet-53/5AI tools are cheap per interaction, but human oversight, correction, and liability management for career guidance narrows the cost advantage to roughly comparable when factoring in necessary faculty involvement.
Technical feasibility todayclaude-haiku-4-5-202510012/5Chatbots and career-matching tools exist but lack the reliability, personalization, and trust required for genuine academic advising; no mature deployed product reliably replaces a faculty advisor across the full scope of curriculum planning and career counseling.
Technical feasibility todayclaude-sonnet-52/5Chatbot advising tools exist in some universities for basic FAQs and scheduling, but no deployed product reliably handles nuanced academic/career advising for health specialties students at scale.

Prepare and deliver lectures to undergraduate or graduate students on topics such as public health, stress management, and work site health promotion.

26

CI 23–30 · exposure 25 · augmentation 75 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Higher education remains a laggard sector for AI displacement of core teaching roles; faculty unions, accreditation bodies, and institutional resistance slow adoption. Current uptake is limited to AI-assisted preparation tools, not replacement of instructors.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools slowly for core teaching duties, with pilots in content generation but little production-level replacement of live lecturing.
Augmentation potentialclaude-haiku-4-5-202510014/5AI powerfully augments lecture preparation through content generation, slide design, and real-time translation of complex concepts into simplified explanations. AI can also assist with student Q&A, office-hour supplementation, and personalized learning materials—transforming instructor productivity while keeping the human educator in the central role.
Augmentation potentialclaude-sonnet-54/5AI substantially helps faculty draft lecture materials, generate examples, create quizzes, and summarize research, meaningfully boosting prep productivity while the instructor still delivers the lecture.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can generate lecture outlines, slides, and explanatory content, delivering live lectures to students requires real-time interaction, adaptive explanation based on student feedback, and dynamic engagement—capabilities current AI systems cannot replicate end-to-end with 50% time savings at equal pedagogical quality. AI can assist preparation but not replace the full task.
Task automatabilityclaude-sonnet-52/5AI can help draft lecture content and slides, but live delivery, adapting to student questions, and classroom presence still require a human instructor, so end-to-end time savings fall well short of 50%.
Adoption barriersclaude-haiku-4-5-202510014/5Institutional accreditation, faculty employment contracts, and educational policy require human instructors to teach and assess students. Regulatory frameworks and university governance structures mandate human faculty for course delivery and credential authorization, creating substantial legal and organizational barriers to full automation.
Adoption barriersclaude-sonnet-53/5No licensing requirement mandates a human lecturer per se, but accreditation standards, student expectations of live instruction, and institutional norms create moderate friction against full replacement.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI can generate lecture materials at low cost, but the human instructor's loaded wage includes salary, benefits, and institutional overhead that far exceeds the cost of AI content generation. The task also includes grading, office hours, and curriculum design, which AI cannot fully substitute.
Cost vs. human wageclaude-sonnet-52/5AI can cheaply assist with prep work, but replacing the actual lecture delivery would require video/avatar systems plus human oversight, keeping total cost comparable to or only modestly below instructor wages for the full task.
Technical feasibility todayclaude-haiku-4-5-202510012/5Deployed products (generative AI, lecture-generation tools) can draft lecture materials reliably, but no mature system can independently deliver a full academic lecture with the responsiveness, credibility, and pedagogical effectiveness required in a classroom. Lecture delivery requires human presence and real-time adjustment.
Technical feasibility todayclaude-sonnet-52/5Tools like ChatGPT or Khanmigo can generate lecture outlines and even narrated content, but no deployed product reliably delivers full postsecondary lectures in real classrooms at scale.

Plan, evaluate, and revise curricula, course content, course materials, and methods of instruction.

25

CI 25–25 · exposure 25 · augmentation 63 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Higher education, particularly in health specialties with stringent accreditation (ACCJC, specialized health boards), adopts AI cautiously. While some institutions pilot AI-assisted content tools, systematic replacement of curriculum planning decisions remains minimal because of governance structures and professional liability concerns.
Sector adoption velocityclaude-sonnet-52/5Higher education, especially health specialties, is a slower-adopting sector with cautious integration of AI into curriculum design due to accreditation and academic governance processes.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can usefully assist faculty by drafting syllabus sections, suggesting learning objectives aligned with competency frameworks, or generating example course materials, raising the speed of curriculum work. However, the human faculty judgment remains central to ensuring educational integrity and alignment with institutional and accreditation standards.
Augmentation potentialclaude-sonnet-54/5AI is highly useful for drafting course outlines, suggesting readings, generating assessment items, and summarizing new research, meaningfully boosting instructor productivity in curriculum development while humans retain final judgment.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can assist in generating or revising course materials and suggesting instructional methods, curriculum planning and evaluation require significant judgment about learning outcomes, pedagogical coherence, and institutional context that AI cannot fully replace. End-to-end automation would need to meaningfully reduce time by 50% while maintaining educational quality, which current systems cannot consistently achieve.
Task automatabilityclaude-sonnet-52/5AI can draft syllabi and suggest materials, but planning and revising a curriculum requires judgment about accreditation standards, program goals, and pedagogical fit that current systems cannot autonomously handle end-to-end.6:1 time savings are unlikely to reach 50% given need for expert review and institutional context.
Adoption barriersclaude-haiku-4-5-202510014/5Curriculum decisions in higher education are protected by faculty governance, accreditation requirements, and institutional autonomy. Most institutions have explicit processes requiring faculty committees, program reviews, and alignment with professional standards; substituting AI would face strong organizational and governance friction.
Adoption barriersclaude-sonnet-54/5Curriculum for licensed health professions is subject to accreditation bodies (e.g., LCME, CCNE) requiring qualified faculty oversight and sign-off, creating strong regulatory and credentialing barriers to full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI tools for curriculum support (LLMs, content platforms) have deployment costs, but faculty labor for curriculum work is episodic and relatively low-cost per institution compared to scaling AI oversight and integration. The cost advantage is minimal since curriculum revision is not high-volume transactional work.
Cost vs. human wageclaude-sonnet-52/5While AI drafting is cheap, the human faculty time for evaluation, accreditation alignment, and clinical-relevance review still dominates costs, so overall cost savings versus a faculty member's loaded wage are modest.
Technical feasibility todayclaude-haiku-4-5-202510012/5No production systems reliably perform full curriculum planning and revision at the quality standards required by educational institutions. Some tools assist with content generation or material drafting, but comprehensive curriculum evaluation—balancing accreditation standards, student outcomes, and institutional mission—remains primarily human-driven.
Technical feasibility todayclaude-sonnet-52/5AI tools (e.g., ChatGPT, course-design assistants) are used ad hoc by instructors to brainstorm content, but no deployed product reliably manages full curriculum planning and revision in production at health-sciences postsecondary institutions.

Initiate, facilitate, and moderate classroom discussions.

23

CI 20–25 · exposure 20 · augmentation 75 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Postsecondary education remains a relatively low-adoption sector for AI automation of teaching tasks; most institutions use AI for administrative support (grading assistance, content recommendations) rather than replacing instructor-led classroom facilitation, which is core to institutional identity.
Sector adoption velocityclaude-sonnet-52/5Higher education, especially health sciences, has been slower to adopt AI for live teaching interactions compared to administrative or content-generation tasks.
Augmentation potentialclaude-haiku-4-5-202510014/5AI can meaningfully assist instructors by generating discussion questions, flagging key student contributions, tracking engagement metrics, and providing real-time transcripts—allowing the instructor to focus on group dynamics and deeper mentoring rather than logistics.
Augmentation potentialclaude-sonnet-54/5AI can help instructors prepare discussion questions, summarize readings, and generate case studies to enrich in-class discussion, meaningfully aiding preparation even though it doesn't replace live facilitation.
Task automatabilityclaude-haiku-4-5-202510012/5AI can generate discussion prompts and transcribe/summarize discussions, but leading dynamic classroom interactions requires real-time responsiveness to student cues, managing group dynamics, and adapting to emotional/pedagogical context—tasks that fall short of 50% time savings at equal quality today.
Task automatabilityclaude-sonnet-52/5Facilitating live classroom discourse requires real-time social judgment, reading student cues, and adaptive pedagogy that current AI cannot reliably replicate end-to-end, though AI can generate discussion prompts.stions or summarize.
Adoption barriersclaude-haiku-4-5-202510014/5Higher education teaching carries strong institutional and accreditation expectations that instructors directly engage students; there is likely regulatory/contractual language protecting human instruction, and organizations face reputational and pedagogical barriers to automating the core intellectual exchange between teacher and learner.
Adoption barriersclaude-sonnet-54/5Postsecondary health instruction often requires credentialed faculty for accreditation and quality assurance, and live classroom facilitation strongly favors human presence and authority.
Cost vs. human wageclaude-haiku-4-5-202510012/5Implementing AI-assisted classroom discussion (software, integration, human oversight) carries non-trivial infrastructure and personnel costs; savings are marginal compared to the loaded salary of an experienced postsecondary instructor who brings irreplaceable subject expertise and interpersonal judgment.
Cost vs. human wageclaude-sonnet-52/5Even where AI could assist with prep, replacing the live facilitation role would still require human presence and oversight, so cost savings are minimal for the core task.
Technical feasibility todayclaude-haiku-4-5-202510012/5AI tools can draft discussion questions and assist with moderation (e.g., filtering comments), but no deployed product reliably facilitates and moderates live classroom discussions at the quality expected in higher education without substantial human oversight and intervention.
Technical feasibility todayclaude-sonnet-51/5No deployed product autonomously moderates live health-specialties graduate/professional classroom discussions; existing tools are limited to chatbots or discussion-board assistants, not live in-person facilitation.

Conduct research in a particular field of knowledge and publish findings in professional journals, books, or electronic media.

14

CI 0–28 · exposure 13 · augmentation 63 · importance 3.6/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Academic research institutions remain conservative and slow to adopt automation of core research functions; research is a high-trust, credentialed activity where human authorship and accountability are non-negotiable, limiting any meaningful adoption of end-to-end research automation.
Sector adoption velocityclaude-sonnet-53/5Academic and health research settings are adopting AI tools for literature review, writing assistance, and data analysis at a moderate pace, but full automation of research and publication remains rare in practice.
Augmentation potentialclaude-haiku-4-5-202510013/5AI assists meaningfully with parts of research workflow—literature synthesis, statistical analysis drafting, manuscript formatting, and editing—but the human researcher remains the central actor in hypothesis formation, methodology, interpretation, and publication decision-making.
Augmentation potentialclaude-sonnet-54/5AI significantly assists researchers with literature reviews, drafting manuscripts, statistical analysis, and identifying research gaps, meaningfully increasing productivity while humans retain control of the scientific process.
Task automatabilityclaude-haiku-4-5-202510011/5Conducting original research and publishing findings requires domain expertise, novel hypothesis generation, experimental design decisions, and creative scientific judgment that current AI systems cannot perform end-to-end. While AI can assist with literature review or manuscript editing, the core research conception and execution remain fundamentally human-driven.
Task automatabilityclaude-sonnet-52/5AI can assist with literature review, data analysis, and drafting, but original research design, experimentation, data collection, and generating novel scientific insight in health specialties require human expertise and cannot be fully automated end-to-end today.
Adoption barriersclaude-haiku-4-5-202510015/5Research publication and authorship carry strict professional, ethical, and institutional requirements: only qualified humans can be listed as authors, IRB/ethics approval requires human accountability, and institutional affiliation and credibility are legally and professionally tied to human researchers. Academic publishing has hard gatekeeping on who may claim research contribution.
Adoption barriersclaude-sonnet-54/5Academic publishing requires named authorship, accountability, ethical review, and often IRB/institutional oversight; journals and universities have strong norms and requirements around human authorship and research integrity.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of training a health specialties researcher and conducting original research far exceeds current AI inference and assistance costs, but AI cannot substitute for the human researcher here—the comparison is moot since automation is not feasible.
Cost vs. human wageclaude-sonnet-52/5AI reduces costs for literature review and drafting portions, but the core research (experiments, clinical data, peer-reviewed validation) still requires expensive expert labor, keeping overall costs comparable to human-led research.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product performs original research generation and peer-ready publication in specialized health fields today. AI cannot independently design studies, collect novel empirical data, or make the contextual scientific judgments required for publishable research contributions.
Technical feasibility todayclaude-sonnet-52/5Tools like AI writing assistants, literature summarizers, and statistical analysis software are used by researchers, but no deployed product independently conducts full research studies and publishes findings reliably.

Supervise undergraduate or graduate teaching, internship, and research work.

13

CI 0–25 · exposure 13 · augmentation 50 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Higher education adoption of AI for core teaching supervision remains slow; while some institutions pilot AI-assisted grading and scheduling, actual replacement of faculty supervision is rare and resisted due to concerns about educational quality and institutional accountability.
Sector adoption velocityclaude-sonnet-51/5Higher education, especially in health specialties with clinical/research supervision requirements, is slow to adopt AI for core mentorship and oversight functions.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can usefully assist faculty by automating scheduling, generating initial feedback drafts, flagging at-risk students via analytics, and organizing research documentation, which can help supervisors allocate time to higher-value mentoring and decision-making.
Augmentation potentialclaude-sonnet-53/5AI tools can help with scheduling, drafting feedback, tracking research progress, or summarizing student work, providing moderate assistance to a supervising faculty member.
Task automatabilityclaude-haiku-4-5-202510012/5AI can assist with logistical tracking (attendance, deadlines, document organization) and generate feedback templates, but cannot replace the core supervision function that requires evaluating student competence, providing nuanced mentoring, resolving interpersonal conflicts, and making judgment calls on student progress—all requiring human expertise and accountability.
Task automatabilityclaude-sonnet-51/5Supervising students' teaching, internships, and research requires ongoing relationship-based mentorship, judgment calls on individual progress, and institutional accountability that current AI cannot perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5Academic institutions have strong governance structures, accreditation requirements, and liability concerns around student evaluation and progression that legally and practically require a credentialed faculty member to supervise teaching and research; institutional culture and professional norms also strongly favor human mentorship.
Adoption barriersclaude-sonnet-55/5Supervision of interns and graduate research typically requires credentialed faculty with institutional and sometimes accreditation/licensure authority, making this a hard barrier to substitution.
Cost vs. human wageclaude-haiku-4-5-202510012/5Current AI systems would require significant integration and human oversight to monitor student work, meaning the total cost (tool subscription, prompt engineering, human review) does not yet undercut the marginal cost of faculty time for this high-stakes supervisory function.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute performing this supervisory role, so cost comparison favors the human by default since AI cannot deliver equivalent output.
Technical feasibility todayclaude-haiku-4-5-202510012/5No production systems reliably perform end-to-end supervision of teaching, internship, and research work. AI tools exist for scheduling and basic feedback generation, but deployed products cannot assess research quality, guide student development, or exercise the professional judgment inherent in supervision.
Technical feasibility todayclaude-sonnet-51/5No deployed product supervises students' academic or clinical work autonomously; this remains a human faculty responsibility in every institution today.

Perform administrative duties, such as serving as department head.

12

CI 3–21 · exposure 13 · augmentation 50 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Higher education remains a laggard sector for AI adoption; while individual faculty use AI assistants for administrative aids, institutional displacement of department head functions through automation is minimal and shows no signs of deep, rapid adoption.
Sector adoption velocityclaude-sonnet-52/5Higher education administration adopts AI slowly for leadership roles, though it may use AI tools for peripheral administrative tasks like scheduling or reporting.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can usefully assist department heads with scheduling, document drafting, data compilation, and routine communications, raising productivity on administrative overhead while the human retains full decision-making authority.
Augmentation potentialclaude-sonnet-53/5AI can assist with routine administrative subtasks like drafting reports, scheduling, and data summarization that support a department head's broader responsibilities.
Task automatabilityclaude-haiku-4-5-202510012/5While some administrative components (scheduling, document drafting, email triage) can be partially automated, serving as a department head requires strategic judgment, personnel management, budget allocation, and navigating complex organizational politics that current AI cannot handle end-to-end at equal quality with 50%+ time savings.
Task automatabilityclaude-sonnet-51/5Serving as department head requires complex interpersonal leadership, political navigation, personnel decisions, and institutional judgment that current AI cannot execute end-to-end.'
Adoption barriersclaude-haiku-4-5-202510015/5Department head positions are typically held by tenured faculty and explicitly require a licensed human with legal accountability for hiring, personnel decisions, and institutional governance; regulatory and organizational requirements mandate human authority in this role.
Adoption barriersclaude-sonnet-54/5Department head roles typically require formal institutional appointment, tenure status, and accountability structures that legally and organizationally require a qualified human.
Cost vs. human wageclaude-haiku-4-5-202510011/5The salary of a department head significantly exceeds the cost of AI assistance for discrete administrative tasks; full replacement would require AI systems that do not yet exist reliably, making any economic comparison unfavorable to automation.
Cost vs. human wageclaude-sonnet-51/5AI cannot substitute for the human role itself, so any comparison is moot—the full task requires a human incumbent, making AI substitution cost effectively infinite.
Technical feasibility todayclaude-haiku-4-5-202510012/5Deployed products can assist with specific administrative subtasks (calendar management, basic report generation) but no mature system performs the full portfolio of department head duties reliably in production; real-world performance remains limited and narrow.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs departmental leadership or headship duties; at best AI tools assist with scheduling or document drafting components.

Collaborate with colleagues to address teaching and research issues.

9

CI 5–13 · exposure 5 · augmentation 38 · importance 3.7/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Academic institutions adopt AI slowly and selectively; collaborative problem-solving on research and teaching remains a protected domain of faculty judgment. No measured displacement of collegial interaction by AI in postsecondary settings.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools for specific tasks like literature review or drafting, but collaborative governance and interpersonal faculty coordination see minimal AI penetration.
Augmentation potentialclaude-haiku-4-5-202510012/5AI can assist by drafting synthesis documents or literature reviews for colleagues to discuss, but offers limited augmentation of the core task—genuine intellectual exchange and consensus-building require human presence and accountability.
Augmentation potentialclaude-sonnet-53/5AI can assist by summarizing research, drafting meeting agendas, synthesizing literature, or preparing materials that inform collaborative discussions, moderately boosting productivity around the edges of this task.
Task automatabilityclaude-haiku-4-5-202510011/5Collaboration on teaching and research issues requires human judgment, nuanced understanding of interpersonal dynamics, and creative problem-solving that current AI cannot perform end-to-end. AI cannot substitute for the genuine dialogue and consensus-building that characterize collegial engagement.
Task automatabilityclaude-sonnet-51/5This is an interpersonal, judgment-driven collaborative activity requiring relationship-building, negotiation, and shared decision-making among faculty that AI cannot perform end-to-end today.
Adoption barriersclaude-haiku-4-5-202510014/5Academia has strong norms requiring human intellectual ownership and accountability for research and pedagogical decisions; institutions expect faculty to directly engage with peers on these issues. Tenure, accreditation, and professional ethics standards reinforce human-led collaboration.
Adoption barriersclaude-sonnet-54/5Faculty governance, tenure structures, and academic norms require human colleagues to jointly deliberate on curriculum and research direction, creating strong organizational and institutional barriers to automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of oversight, prompt engineering, and human correction to validate AI-mediated collaboration would likely exceed the wage value of time saved, given the high stakes and context sensitivity of academic collaboration.
Cost vs. human wageclaude-sonnet-51/5There is no AI system that can replace this collaborative function, so cost comparison favors the human process entirely.
Technical feasibility todayclaude-haiku-4-5-202510012/5While AI can draft meeting agendas or summarize research literature, no deployed system reliably orchestrates multi-stakeholder collaboration on substantive academic issues. Current products lack the contextual awareness and social reasoning needed for this task at scale.
Technical feasibility todayclaude-sonnet-51/5No deployed product substitutes for human collegial collaboration on teaching/research strategy; this remains fundamentally a human social process.

Maintain regularly scheduled office hours to advise and assist students.

8

CI 0–16 · exposure 5 · augmentation 50 · importance 3.7/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Higher education has shown minimal adoption of AI-driven advising or office-hour replacement; institutions prioritize the human relationship and accountability in student mentoring.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI chatbots for basic support but faculty office hours remain largely untouched by automation initiatives.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist by scheduling appointments, summarizing student records, or providing reference materials, but the faculty member remains central to the advising conversation and judgment.
Augmentation potentialclaude-sonnet-53/5AI can help by answering routine student questions, scheduling, and providing supplementary explanations, freeing faculty time for higher-value in-person advising during office hours.
Task automatabilityclaude-haiku-4-5-202510011/5This task fundamentally requires human judgment, empathy, and relationship-building to advise students on complex academic and personal matters. Current AI cannot replace the nuanced counseling and mentoring interactions that define office hours.
Task automatabilityclaude-sonnet-51/5This task requires synchronous, personal human presence and relationship-building with students for advising, mentorship, and institutional guidance that AI cannot substitute for as a scheduled human commitment.'
Adoption barriersclaude-haiku-4-5-202510015/5Students, institutions, and accreditors expect direct faculty engagement and mentoring; there is a strong institutional and professional expectation that faculty themselves maintain office hours, not delegate to automation.
Adoption barriersclaude-sonnet-54/5Institutional accreditation, tenure/faculty responsibilities, and student expectations of personal mentorship create strong organizational and professional barriers to full substitution.
Cost vs. human wageclaude-haiku-4-5-202510011/5The human faculty member must be paid regardless; any AI assistance (scheduling, basic info) adds cost without eliminating the professor's salary, making the ratio unfavorable for full task replacement.
Cost vs. human wageclaude-sonnet-52/5While AI chat tools are cheap, they cannot replace the human advising function itself, so the relevant cost comparison still requires paying the human; only marginal FAQ deflection saves cost.
Technical feasibility todayclaude-haiku-4-5-202510012/5While chatbots can provide basic information and scheduling assistance, no deployed system can reliably conduct the advising and emotional support that office hours entail. AI may handle scheduling or FAQ responses, but not the core task.
Technical feasibility todayclaude-sonnet-51/5No deployed product replaces a faculty member's office hours; chatbots exist for FAQs but not for the personalized academic/career advising and availability this task requires.

Act as advisers to student organizations.

4

CI 0–7 · exposure 0 · augmentation 38 · importance 3.4/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Educational institutions have not adopted AI for core advising roles; this task remains firmly human-centered in practice. The sector treats student advising as a relationship-based responsibility unsuitable for automation, and adoption signals are absent.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools for administrative support but advisory/mentorship roles for student organizations show little displacement trend.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could marginally assist advisers by organizing meeting notes, drafting communications, or providing resource suggestions, but the core advising relationship remains entirely human-driven, limiting meaningful productivity augmentation.
Augmentation potentialclaude-sonnet-53/5AI can help advisors with scheduling, drafting communications, budgeting templates, or event planning support, moderately easing administrative burden.
Task automatabilityclaude-haiku-4-5-202510011/5Acting as an adviser to student organizations requires relationship-building, mentorship, real-time problem-solving, and interpersonal judgment that cannot be replicated by current AI systems. This task fundamentally depends on human trust and emotional intelligence in a context where students seek guidance on personal and organizational matters.
Task automatabilityclaude-sonnet-51/5Advising student organizations requires ongoing relationship-building, mentorship, institutional judgment, and personal presence at meetings/events that current AI cannot replicate end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5This task has strong legal and institutional barriers: faculty advisers hold formal organizational responsibility, liability for student welfare decisions, and legal standing that only humans can provide. Universities require credentialed staff to sign off on organizational governance and student matters.
Adoption barriersclaude-sonnet-54/5Institutions typically require a designated faculty/staff advisor for liability, accreditation, and governance reasons, creating strong structural barriers to full automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of deploying and maintaining an AI system to attempt this task, combined with the liability and oversight required when human judgment fails, would exceed the salary cost of having a faculty member provide genuine advising.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute performing this role, so cost comparison favors the human by default; any AI use would only supplement, not replace, at added cost.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI product reliably performs the role of organizational adviser in production settings. While chatbots can provide generic information, they cannot substitute for the human relationship, accountability, and contextual organizational knowledge that students expect from faculty advisers.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs the role of a faculty advisor to student organizations; this remains a human relational and administrative function.

Participate in campus and community events.

3

CI 0–5 · exposure 0 · augmentation 25 · importance 3.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5This task involves human presence and community engagement, sectors and activities where AI adoption has no meaningful foothold.
Sector adoption velocityclaude-sonnet-51/5This is an inherently physical, interpersonal task with no meaningful AI adoption trend in this context.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could marginally assist with event logistics (scheduling, reminders, summarizing attendance data) but offers minimal productivity gain for the core task of meaningful participation.
Augmentation potentialclaude-sonnet-52/5AI can help with scheduling, event planning logistics, or drafting talking points, but offers minimal assistance to the actual act of participating.
Task automatabilityclaude-haiku-4-5-202510011/5Participation in campus and community events requires physical presence, real-time interpersonal engagement, and contextual decision-making about which events matter and how to represent the institution—tasks that current AI cannot perform end-to-end.
Task automatabilityclaude-sonnet-51/5Physical presence, networking, and in-person representation at events cannot be performed by AI systems today.'
Adoption barriersclaude-haiku-4-5-202510015/5Institutional representation, community relationship-building, and presence at events are inherently human responsibilities that cannot be delegated to machines; organizational and social norms create hard barriers.
Adoption barriersclaude-sonnet-54/5Institutional expectations, faculty role norms, and community relationship-building require a human presence, creating strong organizational and social barriers to automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5There is no AI alternative to human participation in community events; the comparison is not applicable, and any attempted substitution would be ineffective.
Cost vs. human wageclaude-sonnet-51/5There is no AI equivalent output for physical event participation, so cost comparison favors the human by default.
Technical feasibility todayclaude-haiku-4-5-202510011/5No AI system can autonomously attend and meaningfully participate in physical events; this task is fundamentally human and relational.
Technical feasibility todayclaude-sonnet-51/5No deployed AI product substitutes for a human attending or participating in campus/community events.

Supervise laboratory sessions.

1

CI 0–3 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Educational institutions, especially those with laboratory components, move slowly on automating instructor-led supervision due to accreditation, safety liability, and student-outcome concerns. Adoption in this sector remains minimal and confined to augmentation (e.g., recording equipment), not replacement.
Sector adoption velocityclaude-sonnet-52/5Higher education, particularly hands-on health science instruction, adopts AI slowly for physical supervisory tasks despite faster adoption of AI for content creation or grading elsewhere in academia.
Augmentation potentialclaude-haiku-4-5-202510012/5AI can assist lab supervision marginally—recording sessions for review, flagging anomalies in equipment readings, or organizing pre-lab materials—but cannot meaningfully augment the core supervisory function of real-time safety oversight, student guidance, and adaptive instruction that requires human judgment and presence.
Augmentation potentialclaude-sonnet-52/5AI can help prepare lab materials, quizzes, or simulations beforehand, but offers minimal real-time assistance during actual supervision of physical lab work.
Task automatabilityclaude-haiku-4-5-202510011/5Supervising laboratory sessions requires real-time human presence, safety monitoring, student engagement assessment, and dynamic problem-solving that current AI cannot perform end-to-end. AI lacks the embodied presence needed to oversee hands-on work, respond to emergencies, and provide immediate feedback in a physical lab environment.
Task automatabilityclaude-sonnet-51/5Supervising a live laboratory session requires physical presence, real-time safety monitoring, hands-on correction of student technique, and judgment calls that current AI cannot perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Strong regulatory and legal barriers protect this task: institutional liability for laboratory safety, accreditation requirements for educator-led supervision, and legal responsibility for student welfare mean that a licensed/credentialed instructor must legally oversee lab work. No AI system can satisfy these requirements.
Adoption barriersclaude-sonnet-55/5Lab safety regulations, institutional liability, accreditation standards, and student safety requirements mandate qualified human supervisors physically present in health science labs.
Cost vs. human wageclaude-haiku-4-5-202510011/5AI deployment for lab supervision would be more expensive than a human instructor when accounting for camera infrastructure, real-time monitoring systems, liability frameworks, and required human oversight backup. The cost of automation would exceed the loaded wage of a teaching assistant or lab instructor.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute performing this task, so cost comparison favors the human by default; any AI component would be additive, not substitutive.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably supervises laboratory sessions autonomously today. While AI can assist with pre-lab materials or post-session analysis, the task of active supervision—monitoring student technique, ensuring safety compliance, managing group dynamics—remains strictly human-dependent in practice.
Technical feasibility todayclaude-sonnet-51/5No deployed product supervises health science laboratory sessions autonomously; at most AI provides supplementary materials or virtual simulations alongside human supervision.

Serve on academic or administrative committees that deal with institutional policies, departmental matters, and academic issues.

0

CI 0–0 · exposure 0 · augmentation 38 · importance 3.7/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Committee service is a core governance function in higher education institutions with entrenched human participation requirements; no adoption of AI committee members has occurred in production academic settings.
Sector adoption velocityclaude-sonnet-51/5Academic governance structures are slow-moving, tradition-bound, and show essentially no movement toward AI participation in committee roles.
Augmentation potentialclaude-haiku-4-5-202510012/5AI might assist in preparing meeting materials, summarizing policies, or drafting documentation, but offers minimal assistance during deliberation and decision-making itself, which is the core of committee work.
Augmentation potentialclaude-sonnet-53/5AI can help summarize meeting materials, draft policy language, or prepare briefing documents for committee members, offering moderate productivity support around the edges of the task.
Task automatabilityclaude-haiku-4-5-202510011/5Committee service requires judgment-based deliberation, consensus-building, institutional knowledge, and decision-making on policy trade-offs that demand human discretion and accountability. No current AI system can meaningfully participate in or replace human committee membership.
Task automatabilityclaude-sonnet-51/5This requires real-time human deliberation, judgment, negotiation, and institutional political awareness that AI cannot substitute for; committee membership itself is inherently a human role.
Adoption barriersclaude-haiku-4-5-202510015/5Academic committees involve fiduciary responsibility, institutional governance, legal liability for institutional decisions, and explicit faculty governance structures that require human participation and accountability by role.
Adoption barriersclaude-sonnet-55/5Committee service requires institutional authorization, faculty status, shared governance rules, and accountability structures that legally and organizationally require a human faculty member's participation.
Cost vs. human wageclaude-haiku-4-5-202510011/5Committee service is performed by salaried faculty as part of their institutional role; there is no comparable AI cost structure or established substitute service to measure against.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute for actual committee participation, so the cost comparison is moot—only a human can fulfill this role, making AI comparatively unusable rather than cheaper.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs committee service autonomously; this requires legal authority, institutional accountability, and human discretionary judgment that cannot be delegated to AI in production.
Technical feasibility todayclaude-sonnet-51/5No deployed product serves as a committee member exercising judgment and voting on institutional policy; this is not a task category AI products address today.

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.