Business Teachers, Postsecondary

25-1011.00
Median wage $99,080/yr82,150 employed (US)Rank #284 of 923 scored · top 31% by substitution

Teach courses in business administration and management, such as accounting, finance, human resources, labor and industrial relations, marketing, and operations research. Includes both teachers primarily engaged in teaching and those who do a combination of teaching and research.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution34
Exposure32
Augmentation66

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

25 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

20%

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%33

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

Technical feasibility todayw 20%30

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

Cost vs. human wagew 15%37

panel mean rating 2.5/5 → substitution pressure 37/100

Adoption barriersw 20%inverted — strong barriers lower the score41

panel mean rating 3.4/5 (barrier strength) → substitution pressure 41/100

Sector adoption velocityw 10%31

panel mean rating 2.2/5 → substitution pressure 31/100

Task breakdown (25 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.

95

CI 95–95 · exposure 100 · augmentation 88 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510015/5Postsecondary institutions have rapidly and deeply adopted LMS and automated record-keeping systems. Nearly all accredited institutions now use digital attendance and grade tracking, reflecting fast adoption in a highly digitized sector.
Sector adoption velocityclaude-sonnet-55/5Higher education has near-universal adoption of digital LMS platforms for attendance and grade management already embedded in standard workflows.
Augmentation potentialclaude-haiku-4-5-202510015/5AI systems augment faculty productivity by automatically capturing attendance, organizing grades, and generating reports. Instructors remain in the loop to review and validate, but AI transforms the efficiency of these administrative tasks, freeing time for teaching and student interaction.
Augmentation potentialclaude-sonnet-54/5AI-enabled gradebooks and analytics tools substantially reduce administrative burden and surface insights (e.g., at-risk students) while instructors retain oversight of grading judgments.
Task automatabilityclaude-haiku-4-5-202510015/5Maintaining student attendance records, grades, and other required records is largely data entry and record-keeping that current AI systems can automate end-to-end. Learning management systems (Canvas, Blackboard) and AI-driven attendance tracking already perform these functions with >50% time savings through automated capture and database management.
Task automatabilityclaude-sonnet-55/5Recording, aggregating, and calculating attendance and grades is a structured data-management task that off-the-shelf LMS/gradebook automation already handles with minimal human input beyond initial data entry or grading judgment.
Adoption barriersclaude-haiku-4-5-202510012/5There are minimal hard legal barriers to automating record maintenance; however, some institutional friction exists around data privacy compliance (FERPA in the US) and the expectation that faculty verify final grades. These friction points are surmountable and do not legally require human sign-off on the maintenance task itself.
Adoption barriersclaude-sonnet-52/5Some institutional policy requires instructor sign-off on final grades, but the record-keeping mechanics themselves face few regulatory or licensing barriers.
Cost vs. human wageclaude-haiku-4-5-202510015/5AI-based record-keeping solutions cost orders of magnitude less than paying a human to manually track attendance and grades. Cloud-based LMS systems are cheap per student, and once deployed, the marginal cost per additional record is negligible compared to loaded faculty time.
Cost vs. human wageclaude-sonnet-55/5Automated gradebook/attendance software costs a small fraction of the instructor time it would take to maintain these records manually, especially at scale.
Technical feasibility todayclaude-haiku-4-5-202510015/5Mature, widely deployed products reliably perform this task in production. LMS platforms with integrated grade tracking, automated attendance systems, and record management are standard in most postsecondary institutions and work at scale without material error rates.
Technical feasibility todayclaude-sonnet-55/5Learning management systems (Canvas, Blackboard, PowerSchool, etc.) already automate attendance tracking, grade calculation, and recordkeeping in production at scale across universities.

Compile bibliographies of specialized materials for outside reading assignments.

83

CI 76–90 · exposure 83 · augmentation 100 · importance 3.6/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510013/5Higher education and professional services are moderately digitized, but adoption of AI for administrative faculty tasks like bibliography compilation remains in the pilot/early-adoption phase rather than mainstream production, though awareness and experimentation are growing.
Sector adoption velocityclaude-sonnet-53/5Higher education is adopting AI tools for research and content support but institutional adoption is uneven, with mixed policies on AI use in course preparation.
Augmentation potentialclaude-haiku-4-5-202510015/5AI significantly augments faculty productivity by rapidly surfacing candidate sources, suggesting thematic groupings, and handling citation formatting, allowing instructors to focus on pedagogical selection and annotation—a clear human-in-the-loop win.
Augmentation potentialclaude-sonnet-55/5AI substantially speeds up literature discovery, summarization, and organization, letting instructors refine and curate lists rather than manually searching, greatly boosting productivity.
Task automatabilityclaude-haiku-4-5-202510014/5AI can reliably compile structured bibliographies from academic databases and sources with high speed and consistency, meeting the 50% time-saving threshold for many cases. However, the task requires some human review to ensure accuracy of citations and appropriateness of selections for specific course contexts.
Task automatabilityclaude-sonnet-55/5AI systems can generate topic-relevant bibliographies and reading lists quickly, searching and organizing sources in a fraction of the time a human would take, meeting the equal-quality/50%-time-savings bar for most cases.
Adoption barriersclaude-haiku-4-5-202510012/5Few regulatory or legal barriers exist; faculty retain full autonomy over assignment design and can easily oversee AI-generated lists. Minimal organizational friction; most institutions already permit or encourage use of research tools and databases.
Adoption barriersclaude-sonnet-51/5No licensing, regulatory, or liability barriers exist for compiling reading lists; it is a routine administrative/academic task with no legal requirement for human execution.
Cost vs. human wageclaude-haiku-4-5-202510015/5AI-based bibliography compilation costs pennies per task (API calls or subscription fees) versus the loaded hourly wage of a faculty member or librarian spending 20–60 minutes on manual research and compilation, yielding an order-of-magnitude cost advantage.
Cost vs. human wageclaude-sonnet-55/5Generating a bibliography via AI costs cents to a few dollars in compute versus a faculty member's or assistant's hourly wage for manual literature searching and compiling.
Technical feasibility todayclaude-haiku-4-5-202510014/5Mature products (e.g., Zotero with AI plugins, ChatGPT, specialized academic research tools) can generate bibliographies at scale in production environments. Minor errors in formatting or source verification remain, but the core output is reliably deployable for most use cases.
Technical feasibility todayclaude-sonnet-54/5Tools like citation managers, AI research assistants, and literature-search products (e.g., Elicit, Semantic Scholar, ChatGPT with browsing) are deployed and used routinely for this purpose, though occasional citation inaccuracies require verification.

Select and obtain materials and supplies, such as textbooks.

79

CI 65–92 · exposure 78 · augmentation 88 · importance 3.8/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Higher education and institutional procurement have rapidly adopted e-procurement platforms and AI-assisted supplier management; adoption of automation in purchasing is already common in many larger educational systems.
Sector adoption velocityclaude-sonnet-52/5Higher education adoption of AI for administrative/curricular support tasks like material selection is still emerging and inconsistent across departments and institutions.
Augmentation potentialclaude-haiku-4-5-202510014/5AI assists significantly by surfacing relevant materials, comparing costs and availability, and automating order placement, leaving instructors and administrators to make final pedagogical and budget-prioritization decisions.
Augmentation potentialclaude-sonnet-55/5AI significantly speeds up searching, comparing, and summarizing textbook options and supplementary materials, letting instructors make final selections faster and more thoroughly.
Task automatabilityclaude-haiku-4-5-202510015/5Material procurement and textbook selection can be largely automated through AI systems that analyze curriculum requirements, compare supplier databases, process purchase orders, and manage inventory tracking. Off-the-shelf e-procurement platforms with AI integration already enable 50%+ time savings on equivalent output.
Task automatabilityclaude-sonnet-54/5Identifying, comparing, and recommending textbooks/course materials based on syllabus goals is well within current LLM capabilities, especially with web/catalog search access, though final selection often needs human judgment on fit and cost negotiation.
Adoption barriersclaude-haiku-4-5-202510012/5While institutions may prefer human judgment on curriculum materials and have internal approval workflows, there are no legal or regulatory barriers preventing AI from executing the selection and procurement process with institutional sign-off.
Adoption barriersclaude-sonnet-51/5No licensing or regulatory requirement mandates human selection of textbooks; it's an administrative task with no legal sign-off requirement.
Cost vs. human wageclaude-haiku-4-5-202510015/5AI-driven procurement automation, once integrated, operates at a fraction of the cost of manual materials selection, order processing, and inventory management by human staff.
Cost vs. human wageclaude-sonnet-54/5Using an AI assistant to research and shortlist materials costs a fraction of an instructor's time compared to manual review of catalogs and reviews, though final purchasing/ordering still requires human/administrative steps.
Technical feasibility todayclaude-haiku-4-5-202510014/5Mature procurement and supply-chain platforms with AI decision support are deployed at scale in educational institutions today; however, final approval and institutional-specific selections often retain human oversight, preventing a perfect 5.
Technical feasibility todayclaude-sonnet-53/5AI tools can generate curated reading lists and compare textbook options today, but few institutions have deployed dedicated production systems for this narrow procurement task; it's typically done via general-purpose AI assistance rather than a specialized product.

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

76

CI 76–76 · exposure 75 · augmentation 100 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510013/5Postsecondary institutions are in the middle adoption phase—many faculty are experimenting with AI-generated assignment and syllabus drafts, but systematic, production-level deployment remains patchier than in tech and finance sectors. Institutional inertia and variability in faculty tech adoption slow penetration.
Sector adoption velocityclaude-sonnet-53/5Higher education is adopting AI writing tools steadily for course prep, but usage is uneven across institutions and often informal rather than integrated into official workflows.
Augmentation potentialclaude-haiku-4-5-202510015/5AI significantly enhances faculty productivity by drafting customizable templates, generating multiple assignment variations, and adapting materials to different student levels, all while instructors retain full control over content, tone, and alignment with course goals.
Augmentation potentialclaude-sonnet-55/5AI is already widely used by postsecondary instructors to draft and iterate on syllabi, assignments, and handouts, substantially cutting prep time while faculty retain final control over content and pedagogy.
Task automatabilityclaude-haiku-4-5-202510014/5AI can generate substantial portions of syllabi, assignments, and handouts at scale with minimal human oversight, including structure, content scaffolding, and formatting. While typical course materials still benefit from instructor review for disciplinary nuance and institutional alignment, the time savings from AI-generated drafts clearly exceed 50% of the manual creation effort.
Task automatabilityclaude-sonnet-54/5Generating syllabi, homework assignments, and handouts from a course outline is a well-structured text generation task that current LLMs handle well, needing only instructor review and customization.
Adoption barriersclaude-haiku-4-5-202510012/5Few hard legal or regulatory barriers exist; course material authorship is not strictly licensed or regulated. However, institutional quality standards, departmental approval workflows, and faculty ownership norms create moderate friction to full automation.
Adoption barriersclaude-sonnet-52/5No licensing requirement dictates who writes course materials, but institutional norms, accreditation standards, and academic freedom expectations create some friction around fully outsourcing content creation.
Cost vs. human wageclaude-haiku-4-5-202510015/5The cost of API calls or institutional subscriptions to generate course materials is negligible compared to faculty hourly rates for the same work, making AI at least an order of magnitude cheaper on a per-task basis.
Cost vs. human wageclaude-sonnet-55/5Generating drafts of syllabi and assignments via AI costs pennies compared to hours of faculty time, an order-of-magnitude cost advantage even after factoring in review time.
Technical feasibility todayclaude-haiku-4-5-202510014/5Deployed LLMs and specialized educational tools reliably produce usable course materials, syllabi, and assignment templates in production at many institutions. Error rates are low for structural and routine content, though subject-matter specificity and tone alignment still require human review, placing this squarely in mature but not fully autonomous territory.
Technical feasibility todayclaude-sonnet-54/5Products like ChatGPT, Claude, and specialized ed-tech tools (e.g., syllabus generators, course design assistants) are already used by faculty to draft these materials reliably, though instructors still edit for accuracy and alignment with institutional policy.

Develop and maintain course Web sites.

74

CI 72–75 · exposure 75 · augmentation 100 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Higher education institutions and business schools are actively adopting AI-assisted website development tools, content management systems with AI features, and automated maintenance solutions. Adoption is substantial and accelerating in the professional and educational technology sectors.
Sector adoption velocityclaude-sonnet-53/5Higher education has adopted AI tools for course content and web tools moderately, with many faculty using LMS templates or AI assistance, but full delegation of site maintenance is still uneven across institutions.
Augmentation potentialclaude-haiku-4-5-202510015/5AI significantly augments human productivity in course website development by automating code writing, generating layout suggestions, and handling routine maintenance, allowing instructors and IT staff to focus on pedagogical design and content strategy. This is a core use case where AI enhancement is already transforming workflow efficiency.
Augmentation potentialclaude-sonnet-55/5AI tools substantially speed up drafting page content, updating syllabi, generating layouts, and maintaining consistency, while the instructor still reviews and finalizes materials.
Task automatabilityclaude-haiku-4-5-202510014/5AI can automate most technical aspects of course website development—code generation, layout design, content structuring, and maintenance tasks—using tools like GitHub Copilot and generative UI builders. However, instructional design decisions and content curation still require human oversight, preventing full end-to-end automation; the ≥50% time saving threshold is clearly met for technical work.
Task automatabilityclaude-sonnet-54/5Building and maintaining a course website (syllabus pages, content organization, basic templates) is a well-structured, repetitive web-development task that current AI coding/website tools can largely handle, though some manual setup and integration with LMS platforms is often needed.
Adoption barriersclaude-haiku-4-5-202510012/5Minimal legal or regulatory barriers exist to automating course website development; instructors or IT departments can freely adopt these tools. The main friction points are organizational adoption policies and potential preference for human IT support, but nothing legally prevents automation.
Adoption barriersclaude-sonnet-52/5No licensing or legal requirement mandates a human create course websites; institutional IT/branding policies and LMS integration create some friction but not hard barriers.
Cost vs. human wageclaude-haiku-4-5-202510014/5AI-powered website development and maintenance tools cost orders of magnitude less per task than hiring a developer or technical staff member, even when accounting for integration and oversight. The per-task cost is dramatically reduced compared to loaded human wages for equivalent output.
Cost vs. human wageclaude-sonnet-54/5AI-assisted website builders and generative coding tools cost a small fraction of an hour of a postsecondary instructor's time for what is often a routine, templated task.
Technical feasibility todayclaude-haiku-4-5-202510014/5Deployed products (AI-assisted code generation, no-code website builders with AI features, automated content management) reliably handle website development and maintenance at scale in production environments. These tools are mature and widely used, though deployment quality varies by complexity and customization needs.
Technical feasibility todayclaude-sonnet-54/5Products like AI website builders, LMS content generators, and coding assistants (e.g., GitHub Copilot, Wix ADI, course-authoring tools with AI features) reliably generate and update simple educational websites in production today.

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

69

CI 59–79 · exposure 62 · augmentation 75 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Higher education is digitizing rapidly, and many institutions already deploy automated testing and grading systems. Adoption accelerates in large universities and online programs where scale favors efficiency; smaller institutions lag, but the trajectory is toward widespread AI-assisted assessment.
Sector adoption velocityclaude-sonnet-53/5Higher education is adopting AI grading and exam tools at a moderate pace, with growing pilots in large courses but resistance and inconsistent institutional policies slow deeper deployment.
Augmentation potentialclaude-haiku-4-5-202510014/5AI substantially augments instructor productivity by generating diverse questions, flagging student misconceptions from performance data, and reducing manual grading burden, freeing faculty to focus on feedback and teaching rather than logistics.
Augmentation potentialclaude-sonnet-54/5AI substantially speeds up drafting exam questions, creating rubrics, and providing first-pass grading feedback, meaningfully boosting instructor productivity while they retain final oversight.
Task automatabilityclaude-haiku-4-5-202510014/5Current AI systems can compile exams from question banks, generate diverse questions by topic, administer proctored assessments online, and grade objective and even some subjective responses with high speed and consistency. While complex essay grading still benefits from human review, the bulk of exam workflow (creation, administration, scoring of structured items) is largely automatable, achieving >50% time savings at comparable quality.
Task automatabilityclaude-sonnet-53/5AI can generate exam questions and grade objective or even essay-type responses with reasonable quality, but compiling exams requiring judgment about course-specific learning objectives and grading nuanced business case analyses still needs human oversight for a full end-to-end solution.
Adoption barriersclaude-haiku-4-5-202510012/5Exams are integral to accreditation and institutional policy, but few hard legal barriers prevent AI automation. Academic integrity concerns, faculty preference to retain pedagogical control, and institutional inertia create friction, but these are organizational and cultural rather than regulatory blockers.
Adoption barriersclaude-sonnet-52/5No licensing requirement mandates a human grade exams, though institutional academic integrity policies and grade appeals create some friction and expectation of instructor accountability.
Cost vs. human wageclaude-haiku-4-5-202510015/5AI exam administration and grading cost (licensing + infrastructure) is orders of magnitude lower than the faculty labor hours required to manually create, proctor, and grade exams for large cohorts, making substitution economically compelling.
Cost vs. human wageclaude-sonnet-54/5Once set up, AI-based question generation and grading is dramatically cheaper per exam than faculty or TA time, though initial rubric design and spot-checking add some human cost.
Technical feasibility todayclaude-haiku-4-5-202510014/5Multiple deployed products (learning management systems with AI proctoring, automated grading engines, question-generation tools) perform these functions reliably in higher education at scale. Platforms like Canvas, Blackboard, and AI-driven assessment tools are widely used in production, though some limitation remains in nuanced essay evaluation and security-critical proctoring.
Technical feasibility todayclaude-sonnet-53/5Products like AI-assisted grading tools (e.g., Gradescope, ChatGPT-based rubric graders) are used in production for quizzes and short-answer grading, but reliability for complex business exams (essays, case studies) still has notable error rates and requires instructor review.

Write grant proposals to procure external research funding.

48

CI 37–59 · exposure 38 · augmentation 88 · importance 2.6/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Adoption of AI for grant writing in academic institutions remains in pilot phase, with few institutions systematically integrating large-language models into grant workflow. Sector adoption is slower due to risk aversion and the high stakes of funding failures.
Sector adoption velocityclaude-sonnet-53/5Academia has moderate AI adoption for writing assistance; usage is growing among faculty but is uneven across institutions and disciplines, with formal policies around AI use in grants still developing.
Augmentation potentialclaude-haiku-4-5-202510014/5AI excels at drafting sections, summarizing literature, structuring budgets, and iterating language—tasks that can meaningfully accelerate proposal preparation while faculty retain control over intellectual direction and strategic positioning. This is a strong augmentation scenario.
Augmentation potentialclaude-sonnet-55/5AI substantially speeds up drafting, editing, literature summarization, and formatting of grant proposals, making it a strong productivity multiplier while the researcher retains ownership of ideas and final quality control.
Task automatabilityclaude-haiku-4-5-202510012/5AI can draft text and structure arguments, but grant proposals require deep knowledge of specific research agendas, institutional context, funder priorities, and novel intellectual contribution that demand substantial human judgment and revision. AI cannot reliably replace the core persuasion and strategic positioning that determines funding success.
Task automatabilityclaude-sonnet-53/5AI can draft substantial portions of grant text (background, literature synthesis, boilerplate sections) but framing novel research contributions, budgets, and institutional specifics still requires significant human input and iteration, limiting full end-to-end automation.
Adoption barriersclaude-haiku-4-5-202510013/5While funders do not legally require human authorship, institutional cultures, funder expectations, and faculty accountability for the intellectual content of proposals create significant friction against full automation. Many institutions and grants still expect demonstrable human expertise and commitment in the proposal narrative.
Adoption barriersclaude-sonnet-52/5No licensing requirement blocks AI-assisted drafting, though funders often require original human intellectual contribution and there can be institutional or funder policies on disclosure of AI use in proposals.
Cost vs. human wageclaude-haiku-4-5-202510014/5AI inference and fine-tuning costs are negligible compared to the loaded wage of a faculty member or grant-writing specialist spending days on a proposal. Even with oversight, AI assistance amortizes to a small fraction of human labor cost.
Cost vs. human wageclaude-sonnet-54/5AI drafting assistance costs very little compared to the many hours a faculty member or grant writer would spend, though human review and strategic input remain necessary, tempering the savings somewhat.
Technical feasibility todayclaude-haiku-4-5-202510012/5Deployed AI writing tools (ChatGPT, Claude) can generate grant-like text, but production-grade grant proposal systems with track records of improving funding outcomes are not yet common in academic institutions. Existing systems lack deep integration with institutional research databases and funder intelligence.
Technical feasibility todayclaude-sonnet-53/5Products like ChatGPT, Claude, and specialized grant-writing tools are used by faculty today to draft proposals, but reliability varies and human revision for accuracy, novelty, and compliance with funder requirements is still essential.

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

43

CI 32–54 · exposure 42 · augmentation 75 · importance 4.6/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510013/5Higher education is gradually piloting AI grading assistants, but adoption remains mostly experimental and limited to early-adopter institutions; widespread production use is still uncommon and progress is slower than in other professional sectors.
Sector adoption velocityclaude-sonnet-53/5Higher education is adopting AI grading and feedback tools unevenly—some business schools pilot AI tools actively, but widespread production use across postsecondary business courses remains limited compared to faster-moving industries like finance or tech.
Augmentation potentialclaude-haiku-4-5-202510014/5AI can meaningfully assist instructors by generating initial feedback, flagging common errors, and summarizing assignment trends, allowing humans to focus on higher-level evaluation and personalized comments—substantially raising instructor productivity while maintaining human oversight.
Augmentation potentialclaude-sonnet-54/5AI substantially speeds up grading workflows by pre-drafting feedback, flagging plagiarism, and summarizing common errors, letting instructors focus on final judgment and edge cases.
Task automatabilityclaude-haiku-4-5-202510012/5AI can assist with mechanical grading (spelling, formatting) and provide draft feedback on straightforward assignments, but meaningful evaluation of student work—especially papers requiring assessment of argument quality, originality, and domain understanding—requires human judgment and context that current systems cannot reliably replicate at equal quality.
Task automatabilityclaude-sonnet-53/5AI can draft grades and feedback for structured assignments (essays, short answers) with reasonable quality, but nuanced grading of original arguments, case analyses, or presentations still needs human judgment and oversight, so it only partially meets the 50% time-saving-at-equal-quality bar.
Adoption barriersclaude-haiku-4-5-202510014/5Institutions typically require instructor ownership of grades for accreditation and student-facing accountability; liability for incorrect assessment, institutional policy, and educator union contracts create substantial friction against full automation of grading authority.
Adoption barriersclaude-sonnet-53/5No licensing requirement blocks AI-assisted grading, but academic integrity policies, accreditation standards, and instructor accountability for final grades create moderate institutional friction.
Cost vs. human wageclaude-haiku-4-5-202510012/5An instructor's time spent grading is already embedded in salary; AI tools reduce that workload but don't eliminate the need for review, and the integration and oversight costs are non-trivial relative to the incremental time savings.
Cost vs. human wageclaude-sonnet-54/5Once integrated, AI grading tools can process large volumes of assignments far cheaper than paying faculty/TA hours per paper, though setup, calibration, and oversight costs offset some savings.
Technical feasibility todayclaude-haiku-4-5-202510013/5Products like Gradescope and learning management systems with basic AI feedback exist in production, but error rates on nuanced assessment remain material, and educators typically cannot fully delegate evaluation without human review and override.
Technical feasibility todayclaude-sonnet-53/5AI writing/grading assistants (e.g., Turnitin AI feedback, Gradescope AI-assisted grading) are deployed in some universities, but reliability varies by assignment type and instructors still review outputs closely, indicating narrow, imperfect deployment.

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

31

CI 25–38 · exposure 25 · augmentation 63 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Academic institutions have been slow to adopt automation for professional development. Most educators continue traditional methods (reading, conferences, committees) with minimal AI augmentation; digitization is partial and institutional inertia is high.
Sector adoption velocityclaude-sonnet-53/5Higher education and academia are moderate adopters of AI tools for research assistance, with growing but not yet deep integration into faculty professional development workflows.
Augmentation potentialclaude-haiku-4-5-202510013/5AI-powered literature summarization, research paper recommendations, and conference scheduling tools can meaningfully assist educators in filtering and organizing information, but they do not transform the core activity of staying current, which remains primarily human-driven reflection and networking.
Augmentation potentialclaude-sonnet-54/5AI significantly aids literature review, summarization, and trend-spotting, helping instructors stay current more efficiently even though the social and experiential components remain human-driven.
Task automatabilityclaude-haiku-4-5-202510012/5Reading literature and identifying key developments could be partially automated via summarization tools, but actively participating in professional discussions and networking requires human judgment and social presence that current AI cannot meaningfully replicate. At best, AI could surface relevant papers or summarize conference agendas, but the core task involves synthesizing information and building professional relationships.
Task automatabilityclaude-sonnet-52/5AI can help surface and summarize literature, but the task's core—synthesizing understanding, building professional relationships, and engaging in discourse at conferences—requires human presence and judgment that current AI cannot replace end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5Professional development and staying current are institutional expectations tied to tenure, accreditation, and teaching quality; they are not externally licensed but are embedded in employment agreements and professional norms. Colleagues and conference organizers also expect human participation for relationship-building and credibility.
Adoption barriersclaude-sonnet-52/5No licensing barrier, but professional norms and the inherently social/human nature of conferences and colleague interactions create moderate friction against full substitution.
Cost vs. human wageclaude-haiku-4-5-202510012/5Summarization and filtering tools are cheap per query, but educators must still spend significant time reading, interpreting, and attending events themselves. The cost of AI assistance is low but does not approach the cost of the total human time spent on this task.
Cost vs. human wageclaude-sonnet-52/5AI tools for literature summarization are cheap, but the task also includes attending conferences and human networking, which have no AI cost-substitute, keeping overall cost comparable to human time.
Technical feasibility todayclaude-haiku-4-5-202510012/5While AI can summarize texts and filter information, no deployed product reliably synthesizes field developments into actionable knowledge for a specific educator's needs or replaces the judgment required to interpret what matters. Literature screening tools exist but are narrow and require substantial human oversight.
Technical feasibility todayclaude-sonnet-52/5AI research assistants and summarization tools exist and are used for literature discovery, but no deployed product autonomously performs the full 'keeping current' task including networking and conference participation.

Participate in student recruitment, registration, and placement activities.

30

CI 30–30 · exposure 25 · augmentation 50 · 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 is generally slower to adopt AI automation compared to information and finance sectors. Most institutions use CRM and enrollment systems but have not deeply deployed AI agents for recruitment, registration, or placement; adoption remains pilot-stage in most schools.
Sector adoption velocityclaude-sonnet-52/5Higher education is a relatively slow-adopting sector for full automation of student-facing recruitment and placement, though some institutions pilot AI chat tools for admissions support.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist with resume screening, candidate-to-program matching recommendations, and initial outreach scheduling, raising a placement officer's efficiency. However, the human must remain central to relationship-building and final placement decisions, limiting transformative potential.
Augmentation potentialclaude-sonnet-53/5AI can assist with drafting recruitment materials, managing communications, and matching student data to placement opportunities, meaningfully aiding but not replacing faculty involvement.
Task automatabilityclaude-haiku-4-5-202510012/5AI can automate narrow components like email outreach and form processing, but the task fundamentally requires human judgment on student fit, relationship-building, and placement matching. Current systems cannot end-to-end handle the counseling, persuasion, and contextual decision-making needed across recruitment, registration, and placement with 50% time savings at equal quality.
Task automatabilityclaude-sonnet-52/5This task involves interpersonal outreach, admissions decisions, and relationship-building that require human judgment and presence; AI can support parts (drafting materials, scheduling) but cannot autonomously execute recruitment or placement activities end-to-end.
Adoption barriersclaude-haiku-4-5-202510013/5Educational institutions have regulatory and accreditation requirements around student recruitment and placement disclosures, and reputation risk from poor placement outcomes creates material liability. However, no explicit legal prohibition prevents AI assistance, though human accountability for placements is typically expected.
Adoption barriersclaude-sonnet-53/5No strict licensing requirement, but institutional policies, personalized advising expectations, and accreditation-related human oversight of admissions create moderate friction against full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI deployment for recruitment and placement requires significant integration, data management, and human oversight of outcomes. The loaded cost of a business teacher performing this task is moderate; AI systems would likely approach parity or exceed it once oversight and error-correction are factored in.
Cost vs. human wageclaude-sonnet-52/5While AI chatbots can cheaply handle routine inquiries, the faculty-specific advising, networking, and placement coordination still require costly human time, keeping overall cost savings modest.
Technical feasibility todayclaude-haiku-4-5-202510012/5Limited production systems exist for this task; chatbots can handle basic FAQs and registration forms, but no mature deployed product reliably performs student counseling, placement matching, or recruitment outreach at scale. Most implementations remain pilots or narrow integrations.
Technical feasibility todayclaude-sonnet-52/5Some CRM and chatbot tools exist for handling inquiries and initial screening, but no deployed AI system reliably manages full recruitment, registration, and placement processes without heavy human involvement.

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

28

CI 25–30 · exposure 25 · augmentation 63 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5While academic institutions experiment with AI-assisted writing and analysis tools, adoption of AI for autonomous research and publishing remains minimal. The sector values human expertise and originality, and there is considerable institutional resistance to replacing human researchers.
Sector adoption velocityclaude-sonnet-52/5Higher education and academic research are relatively slow adopters of AI for core research tasks, though AI writing and analysis tools are gaining pilot use in scholarly workflows.
Augmentation potentialclaude-haiku-4-5-202510013/5AI tools meaningfully assist with specific research subtasks—literature review automation, data visualization, manuscript organization, and writing refinement—but augmentation plateaus because human researchers must drive hypothesis formation, methodology design, and interpretation of results.
Augmentation potentialclaude-sonnet-54/5AI significantly aids literature reviews, data analysis, editing, and drafting sections of papers, meaningfully boosting researcher productivity while the human retains intellectual ownership and judgment.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can assist with literature review, data analysis, and manuscript drafting, the core intellectual work of original research discovery, interpretation, and novel insight generation remains fundamentally dependent on human expertise and judgment. AI cannot yet independently conduct novel empirical or theoretical research at the level required for publication in top-tier journals.
Task automatabilityclaude-sonnet-52/5AI can assist with literature review, data analysis, and drafting, but original research design, data collection, novel insight generation, and peer-reviewed publication require human expertise and cannot be fully automated end-to-end today.
Adoption barriersclaude-haiku-4-5-202510014/5Strong barriers exist: research integrity standards, peer review processes, institutional accountability for published findings, and the requirement that researchers maintain professional credentials and reputation. Academic publishing and research dissemination are heavily regulated and human-credentialed processes.
Adoption barriersclaude-sonnet-53/5No formal licensing requirement, but academic norms, peer review, authorship credit, and institutional expectations create significant friction against AI substitution for original research.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI tools for research support (literature analysis, writing assistance) cost relatively little, but their use still requires highly paid researchers to provide direction, validate findings, and conduct the core intellectual work, making the total cost still dominated by human labor.
Cost vs. human wageclaude-sonnet-52/5While AI tools reduce some costs for literature review and drafting, the bulk of the task (original research, analysis, and academic vetting) still requires costly expert human labor, keeping overall cost savings modest.
Technical feasibility todayclaude-haiku-4-5-202510012/5Some AI tools assist with writing and data analysis, but no deployed system can autonomously conduct research and publish findings. Products exist for text generation and literature summarization, but these operate at narrow scopes and still require substantial human direction and validation of research quality.
Technical feasibility todayclaude-sonnet-52/5Products like AI writing assistants and literature-search tools exist and are used by researchers, but no deployed system reliably conducts original scholarly research and gets it published without extensive human direction.

Advise students on academic and vocational curricula and career issues.

28

CI 25–30 · exposure 25 · augmentation 75 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Postsecondary institutions have adopted chatbots and portal tools for basic inquiries, but genuine advising displacement remains minimal; the sector prioritizes human relationships and institutional accountability, limiting rapid AI substitution in this core function.
Sector adoption velocityclaude-sonnet-52/5Higher education is a relatively slow-adopting sector for replacing advising roles with AI, though some institutions pilot chatbots for basic student services.
Augmentation potentialclaude-haiku-4-5-202510014/5AI can meaningfully assist advisors by pre-screening student records, surfacing curriculum options, flagging prerequisite issues, and preparing career-match recommendations before meetings, substantially raising advisor productivity while the advisor retains judgment and relationship responsibility.
Augmentation potentialclaude-sonnet-54/5AI can meaningfully assist by summarizing course catalogs, generating personalized recommendations, and drafting career resources, boosting advisor efficiency while the human remains central to the relationship.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can provide general curriculum information and career data, advising requires understanding individual student circumstances, aspirations, constraints, and context-specific guidance that demands nuanced judgment. Current systems lack the depth of personalized assessment needed to meet the 50% time-saving threshold for the full task.
Task automatabilityclaude-sonnet-52/5Advising requires personalized, contextual judgment about a student's history, goals, and institutional constraints that current AI cannot fully replicate end-to-end, though it can assist with information lookup and drafting.atable pieces exist but not the full task.
Adoption barriersclaude-haiku-4-5-202510014/5Many institutions legally require that degree audits and major recommendations be signed off by a human advisor; accreditation standards, FERPA compliance, and duty-of-care expectations around student outcomes create strong regulatory and liability barriers to full automation.
Adoption barriersclaude-sonnet-53/5No strict licensing requires a human advisor, but institutional policy, liability for poor advice, and student/parent preference for human interaction create moderate friction.
Cost vs. human wageclaude-haiku-4-5-202510012/5Current advisory AI tools require significant setup, customization to institutional curricula, and human oversight to avoid errors; total cost per advisory session remains comparable to or exceeds the cost of a faculty advisor's time when all integration and liability concerns are factored in.
Cost vs. human wageclaude-sonnet-52/5While AI chat tools are cheap per interaction, the need for human review and the relationship-based nature of advising means realistic all-in costs remain comparable to or only modestly cheaper than human advisors for quality outcomes.
Technical feasibility todayclaude-haiku-4-5-202510012/5Chatbots and career-matching tools exist but operate at surface level; they cannot reliably substitute for the relational, diagnostic work of academic advising. No deployed product consistently handles the full spectrum of student needs (major selection, prerequisite planning, career alignment, personal constraints) at institutional quality standards.
Technical feasibility todayclaude-sonnet-52/5Chatbots and advising tools exist for basic curriculum FAQs, but no deployed product reliably handles nuanced career/academic advising in production without heavy human oversight.

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

25

CI 25–25 · exposure 25 · augmentation 63 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Postsecondary education has shown slow adoption of AI for core academic functions like curriculum design; most adoption is confined to pilots in administrative tasks or supplemental tools for content drafting. Faculty resistance to algorithmic curriculum decisions and the complexity of institutional change slow velocity.
Sector adoption velocityclaude-sonnet-52/5Placeholder
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist by drafting course outlines, suggesting updates based on industry trends, generating assessment rubrics, and analyzing student feedback—useful augmentations that lighten planning burden. However, the human instructor must critically evaluate and integrate these suggestions, keeping core pedagogical and evaluation decisions in human hands.
Augmentation potentialclaude-sonnet-54/5Placeholder
Task automatabilityclaude-haiku-4-5-202510012/5While AI can draft course materials and suggest content updates, curriculum planning and evaluation require judgment about pedagogical goals, student outcomes, institutional fit, and alignment with accreditation standards. Current systems cannot autonomously revise curricula to meet diverse stakeholder needs and learning objectives at 50% time savings with equal quality.
Task automatabilityclaude-sonnet-52/5AI can draft syllabi and suggest content, but the holistic evaluation and revision of curricula requires institutional judgment, accreditation alignment, and pedagogical expertise that current AI cannot fully replicate end-to-end."},"feasibility":{"rating":2,"rationale":"Some ed-tech tools assist with course design suggestions, but no deployed product reliably plans and revises full postsecondary curricula in production without heavy faculty oversight."},"cost_ratio":{"rating":2,"rationale":"AI can cut drafting time for materials, but overall curriculum planning still requires substantial faculty time for review, alignment, and institutional approval, limiting cost savings."},"barriers":{"rating":4,"rationale":"Curriculum decisions typically require faculty governance, accreditation compliance, and departmental approval, creating strong organizational and quasi-regulatory barriers to full automation."},"adoption_velocity":{"rating":2,"rationale":"Higher education adopts AI tools unevenly and cautiously, with curriculum design remaining a slow, committee-driven process resistant to rapid AI-driven change."},"augmentation":{"rating":4,"rationale":"AI tools can meaningfully assist in generating course outlines, learning objectives, and content ideas, substantially speeding up the drafting phase while faculty retain final judgment."}},
Adoption barriersclaude-haiku-4-5-202510014/5Academic institutions typically require faculty (often with credentials or tenure) to own curriculum decisions, and accrediting bodies mandate human responsibility for curriculum design and assessment. Institutional governance structures and regulatory expectations place curriculum authority with credentialed educators, creating legal and organizational barriers to full automation.
Adoption barriersclaude-sonnet-54/5Placeholder
Cost vs. human wageclaude-haiku-4-5-202510012/5AI content generation is cheap per unit, but curriculum planning requires domain expertise oversight and human validation that adds significant cost. The all-in cost of AI assistance with necessary human review and revision remains comparable to or exceeds a faculty member's incremental time on this task.
Cost vs. human wageclaude-sonnet-52/5Placeholder
Technical feasibility todayclaude-haiku-4-5-202510012/5AI tools exist for generating lesson outlines and reviewing materials, but no production system reliably evaluates and revises entire curricula independently. Deployed products assist with content generation but lack the contextual, educational, and institutional judgment needed to truly plan and evaluate curricula at scale.
Technical feasibility todayclaude-sonnet-52/5Placeholder

Prepare and deliver lectures to undergraduate or graduate students on topics such as financial accounting, principles of marketing, and operations management.

22

CI 14–30 · exposure 25 · augmentation 88 · importance 4.7/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Postsecondary institutions have shown minimal production adoption of AI for autonomous lecture delivery; institutions remain heavily invested in human faculty structures, and cultural/regulatory resistance to full automation of teaching is substantial.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools slowly for core teaching functions relative to fast-moving sectors like finance or tech, though usage for course prep is growing.
Augmentation potentialclaude-haiku-4-5-202510014/5AI tools demonstrably assist instructors in preparing lectures through content research, slide generation, and example synthesis, raising preparation efficiency while the instructor retains control over pedagogical decisions, delivery, and student interaction.
Augmentation potentialclaude-sonnet-55/5AI substantially aids lecture prep, generating slides, examples, quizzes, and explanations, letting instructors focus more time on delivery and student interaction.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can generate lecture content and draft slides, end-to-end delivery requires real-time student interaction, pedagogical adaptation, and live question-answering that current systems cannot reliably perform without substantial human oversight. AI cannot achieve the 50% time-saving threshold when accounting for quality parity in live instruction.
Task automatabilityclaude-sonnet-52/5AI can help draft lecture content and slides, but live delivery, adapting to student questions, and classroom presence require a human presence; only content-prep portions meet the time-saving bar.
Adoption barriersclaude-haiku-4-5-202510015/5Universities require credentialed faculty for course delivery and institutional accreditation; students expect and often demand human instruction; liability for educational outcomes rests on human instructors; regulatory and accreditation bodies mandate human faculty involvement in degree-granting instruction.
Adoption barriersclaude-sonnet-53/5No licensing mandates a human lecturer, but accreditation standards, student expectations of live instruction, and institutional norms create moderate friction against full replacement.
Cost vs. human wageclaude-haiku-4-5-202510011/5The all-in cost of AI lecture systems (infrastructure, quality assurance, live adaptation) currently exceeds the cost of a postsecondary instructor's wage when factoring in overhead and the necessity of human presence for accreditation and student experience.
Cost vs. human wageclaude-sonnet-52/5AI can cheaply generate supporting materials, but the delivery component still requires a paid instructor, so overall cost savings are partial rather than transformative.
Technical feasibility todayclaude-haiku-4-5-202510012/5AI can assist in lecture preparation (content drafting, slide generation) but no deployed product reliably delivers full lectures autonomously with student engagement comparable to human instructors. Some experimental systems exist, but production adoption by universities remains negligible.
Technical feasibility todayclaude-sonnet-52/5Tools like ChatGPT and Khanmigo can generate lecture outlines and explanations, but no deployed product autonomously delivers full postsecondary lectures in production.

Perform administrative duties, such as serving as department head.

16

CI 7–25 · exposure 13 · augmentation 50 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Academic institutions have been slow to adopt AI for administrative automation; adoption is primarily limited to non-core administrative support (scheduling, document processing) rather than leadership functions, and cultural resistance to algorithmic decision-making in academia remains high.
Sector adoption velocityclaude-sonnet-52/5Higher education administration adopts AI slowly for governance-related roles; AI usage is largely confined to support tasks like scheduling or reporting, not leadership functions.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can meaningfully assist a department head with agenda drafting, data analysis for budgeting, and email triage, moderately raising productivity, though the task remains fundamentally human-centered and judgment-driven.
Augmentation potentialclaude-sonnet-53/5AI can assist with administrative sub-tasks like drafting reports, scheduling, and data analysis, improving efficiency of a department head's supporting work, though the core leadership function remains human.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can assist with scheduling, correspondence, and document management, the core duties of a department head—budget allocation, personnel decisions, strategic planning, and institutional representation—require human judgment, accountability, and authority that current AI systems cannot exercise end-to-end.
Task automatabilityclaude-sonnet-51/5Serving as department head involves leadership, personnel decisions, budget oversight, and interpersonal negotiation that require human judgment, authority, and accountability AI cannot replicate end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5Department head roles carry legal and fiduciary duties (budget oversight, hiring compliance, institutional liability) that typically require a credentialed human to sign off; governance structures and accreditation standards often mandate human accountability in leadership positions.
Adoption barriersclaude-sonnet-54/5Institutional governance structures, tenure/faculty governance rules, and accountability for personnel and budget decisions require a human office-holder, creating strong organizational and quasi-regulatory barriers.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI can reduce clerical overhead by ~20–30%, but the principal cost is the salaried position itself—not eliminated by tools; the all-in cost of oversight and human accountability exceeds the marginal AI savings.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute performing this leadership role, so cost comparison favors the human by default.
Technical feasibility todayclaude-haiku-4-5-202510012/5Deployed AI tools can support narrow administrative workflows (email drafting, meeting summaries, data compilation), but no production system reliably performs full department head duties; these require context-dependent human decision-making and fiduciary responsibility.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs the role of a department head; AI tools may assist with scheduling or drafting but cannot function as the administrator itself.

Initiate, facilitate, and moderate classroom discussions.

14

CI 4–25 · exposure 8 · augmentation 50 · importance 4.6/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Higher education has been slow to adopt AI for core instructional roles; faculty autonomy, accreditation constraints, and institutional caution mean even pilot programs for AI-led classroom discussions remain rare and typically experimental rather than mainstream practice.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI slowly for pedagogical roles; discussion facilitation itself sees minimal AI penetration compared to administrative or grading tasks.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can usefully assist instructors by generating discussion prompts, summarizing themes, flagging student engagement patterns, or preparing background materials, but the core task of real-time facilitation and moderation remains fundamentally human-led with AI as a supporting tool rather than a transformer of productivity.
Augmentation potentialclaude-sonnet-53/5AI can help instructors prepare discussion prompts, generate case questions, or summarize discussion threads afterward, offering moderate productivity support without replacing live facilitation.
Task automatabilityclaude-haiku-4-5-202510011/5Facilitating genuine classroom discussions requires real-time social judgment, emotional intelligence, conflict resolution, and adaptive responsiveness to student contributions that current AI cannot reliably replicate. Even agents struggle with the nuanced art of drawing out quiet students, redirecting tangents while preserving learning, or responding authentically to unexpected intellectual challenges.
Task automatabilityclaude-sonnet-52/5Live, real-time facilitation of dynamic classroom discussion requires reading social cues, adapting to student personalities, and managing group dynamics in-person, which current AI cannot do end-to-end reliably.ureau
Adoption barriersclaude-haiku-4-5-202510015/5Postsecondary teaching is a credentialed, institutionally regulated role; accreditation bodies, faculty governance, and legal liability for student learning outcomes all require a qualified human educator to be responsible for classroom instruction and discussion facilitation.
Adoption barriersclaude-sonnet-53/5No formal licensing barrier for AI facilitation, but strong organizational and pedagogical norms mandate human presence and judgment in live classroom management, and students expect instructor-led interaction.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI-generated discussion prompts or asynchronous facilitation tools cost little, but live moderation by AI would require significant infrastructure and oversight to meet institutional standards, making all-in cost comparable to or exceeding a human instructor's marginal effort.
Cost vs. human wageclaude-sonnet-52/5Since AI cannot perform the core in-person facilitation task, any partial AI tools (e.g., discussion boards) add cost on top of the instructor's wage rather than replacing it.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably moderates live university classroom discussions end-to-end. Chatbots can answer questions or generate prompts, but orchestrating a dynamic discussion—reading room dynamics, managing dominance, ensuring equity, and steering intellectual progression—remains beyond production-grade AI systems.
Technical feasibility todayclaude-sonnet-51/5There are no deployed products that autonomously moderate live in-person postsecondary classroom discussions; existing chatbot discussion tools are auxiliary and used online, not as substitutes for live facilitation.

Maintain regularly scheduled office hours to advise and assist students.

14

CI 11–16 · exposure 5 · augmentation 50 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Postsecondary institutions have been slow to adopt AI for advising roles; most deployments remain pilots or AI-assisted tools rather than replacements. Faculty autonomy and student-centeredness values in academic culture slow displacement.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI slowly for interpersonal advising functions; while some administrative tools are being piloted, actual replacement of scheduled office hours is rare and adoption is cautious.
Augmentation potentialclaude-haiku-4-5-202510013/5AI could assist by pre-screening routine questions, drafting responses to FAQs, or summarizing student records before office hours, raising faculty efficiency. However, the core value of office hours—human dialogue and mentorship—limits how much AI can transform the activity without a human present.
Augmentation potentialclaude-sonnet-53/5AI chatbots and scheduling tools can handle routine student questions and free up time for deeper advising, but the core interpersonal task still requires the instructor's presence and judgment.
Task automatabilityclaude-haiku-4-5-202510011/5Office hours require real-time, contextual dialogue with individual students on topics ranging from course content to career guidance and personal circumstances. Current AI cannot reliably replicate the adaptive, empathetic, and judgment-laden advising that characterizes effective office hours.
Task automatabilityclaude-sonnet-51/5This task requires physical or synchronous presence and personalized human interaction/mentorship that current AI cannot substitute for in a way that meets equal quality with 50% time savings; the core value is human availability and relational trust.dependence.hop
Adoption barriersclaude-haiku-4-5-202510014/5Students expect direct access to faculty, and many institutions view office hours as a core obligation of employment. Institutional culture, student expectations, and accreditation norms treating faculty advising as essential create strong friction against full substitution.
Adoption barriersclaude-sonnet-54/5Faculty roles typically carry contractual/institutional requirements to hold office hours, accreditation expectations around student access to instructors, and strong preference for human mentorship, creating significant organizational and normative barriers.
Cost vs. human wageclaude-haiku-4-5-202510012/5Deploying a functional AI advising system requires significant integration, customization, and human oversight per institution. The cost of developing, maintaining, and monitoring such a system—plus mandatory human backup for edge cases—approaches or exceeds the faculty salary cost for the same advising output.
Cost vs. human wageclaude-sonnet-52/5AI chat tools are cheap per query, but they cannot replace the assigned duty of holding office hours, so the relevant cost comparison for the actual task (not just Q&A) still favors human labor for institutional compliance and mentorship value.
Technical feasibility todayclaude-haiku-4-5-202510012/5While chatbots can answer routine questions about course logistics or syllabus details, no deployed product reliably handles the nuanced, personalized advising that office hours demand. Production systems lack the ability to build ongoing rapport and handle complex student situations.
Technical feasibility todayclaude-sonnet-51/5No deployed product replaces a professor's scheduled office hours for advising; chatbots exist for FAQ-style support but not as substitutes for maintaining office hours as an institutional role.

Mentor new faculty.

13

CI 5–20 · exposure 8 · augmentation 50 · importance 3.1/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Academic institutions adopt technology slowly for core mentoring roles and resist automating human guidance and relationship-building in faculty development. Mentoring remains dominated by human-to-human interaction with minimal displacement by AI tools, reflecting sector conservatism and the high stakes of career guidance.
Sector adoption velocityclaude-sonnet-52/5Higher education is a slow-adopting sector for AI in interpersonal, relationship-based faculty development activities, though some administrative aspects use AI tools.
Augmentation potentialclaude-haiku-4-5-202510013/5AI could assist mentors by preparing reading lists, drafting development plans, or surfacing institutional resources, moderately raising productivity in the administrative aspects of mentoring. However, the core mentoring relationship—listening, judgment, and personalized counsel—remains fundamentally human, limiting augmentation scope.
Augmentation potentialclaude-sonnet-53/5AI can help mentors prepare resources, draft feedback, or organize career development plans, but the core mentoring interaction remains human-driven.
Task automatabilityclaude-haiku-4-5-202510012/5Mentoring involves personalized relationship-building, nuanced feedback, and contextual guidance. While AI can suggest frameworks or draft generic advice, it cannot reliably replicate the trust, judgment, and adaptive support that effective mentoring requires. Significant human involvement would remain essential.
Task automatabilityclaude-sonnet-51/5Mentoring involves relational trust, career guidance, institutional politics, and personalized advice built on long-term human rapport that current AI cannot replicate end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5Faculty mentoring is deeply interpersonal and mission-critical to institutional culture and professional development. Universities and academic institutions prioritize human mentorship as central to faculty success; organizational and cultural expectations, professional norms, and the relational nature of the task create strong resistance to full substitution.
Adoption barriersclaude-sonnet-54/5While not licensed in a legal sense, mentoring requires institutional trust, tenure/promotion guidance, and interpersonal judgment that strongly resist automation and require a credible human mentor.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI systems might reduce marginal support costs through templates or resource aggregation, but mentoring is a high-touch task where the mentor's time and expertise set the cost floor. Even with AI assistance, total cost per effective mentoring relationship remains substantial relative to what a generalist AI could provide.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI equivalent to compare cost against, so AI cannot deliver comparable output at any cost, making it effectively more expensive by not being a substitute.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs authentic mentoring end-to-end. AI can generate text or suggest resources, but mentoring's core—sustained interpersonal relationship, deep institutional knowledge, career-specific navigation—remains a human-centric domain with no production systems substituting for human mentors.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs faculty mentorship; this remains an inherently human relational activity with no production AI substitute.

Collaborate with colleagues to address teaching and research issues.

11

CI 5–16 · exposure 8 · augmentation 50 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Postsecondary teaching environments are slow to adopt AI-mediated collaboration; faculty retain strong preferences for direct peer engagement, and institutional structures (departments, committees) are designed around human collegial interaction rather than algorithmic coordination.
Sector adoption velocityclaude-sonnet-52/5Higher education is a relatively slow-adopting sector for AI in core collegial and governance functions, though administrative support tools are spreading gradually.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can provide useful assistance by drafting collaboration materials, organizing research findings, or highlighting overlaps in colleague interests—improving efficiency without removing the human from the collaborative loop, but not transforming the core deliberative process.
Augmentation potentialclaude-sonnet-53/5AI can help by summarizing research, drafting agendas, analyzing curriculum data, or scheduling, offering moderate productivity support to the collaborative process itself.
Task automatabilityclaude-haiku-4-5-202510012/5Collaboration requires nuanced interpersonal negotiation, shared context-building, and joint problem-framing that current AI cannot meaningfully perform autonomously. AI can assist with drafting agendas or synthesizing research summaries, but cannot replace the substantive discussion and consensus-building between faculty colleagues.
Task automatabilityclaude-sonnet-51/5Collaboration among colleagues on teaching and research issues is a relational, judgment-based activity requiring shared institutional context, trust, and negotiation that current AI cannot perform end-to-end.aviors.','2':1},
Adoption barriersclaude-haiku-4-5-202510014/5Strong barriers exist: academia values collegial deliberation and shared governance, institutions require faculty-to-faculty dialogue for accreditation and curriculum decisions, and there is high organizational and professional norm attachment to human collegial process in research and teaching governance.
Adoption barriersclaude-sonnet-54/5Academic governance, tenure processes, and departmental decision-making require human faculty participation and judgment, creating strong organizational and professional barriers to automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5The task is human expertise-intensive and low-volume per person; AI tools that might assist (document drafting, literature synthesis) cost similarly to or exceed the marginal value they provide, with human oversight still required for the collaborative negotiation.
Cost vs. human wageclaude-sonnet-51/5There is no AI-only substitute for this interpersonal task, so cost comparison to a human collaborator is not meaningful and AI cannot replace this labor.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs end-to-end collaborative problem-solving between human colleagues on academic teaching and research matters. Chatbots can simulate conversation but cannot genuinely coordinate human team decision-making in production.
Technical feasibility todayclaude-sonnet-51/5No deployed product substitutes for human collegial collaboration on academic issues; at best AI tools assist individual components like scheduling or drafting notes.

Collaborate with members of the business community to improve programs, to develop new programs, and to provide student access to learning opportunities, such as internships.

11

CI 5–16 · exposure 8 · augmentation 50 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Higher education remains among the slowest-adopting sectors for operational automation, and stakeholder-facing program development is inherently human-contact work with deep organizational and cultural constraints against displacement.
Sector adoption velocityclaude-sonnet-52/5Higher education administrative and partnership-building functions adopt AI slowly, with most AI use in this space limited to communications drafting, not partnership management itself.
Augmentation potentialclaude-haiku-4-5-202510013/5AI could assist by drafting partnership proposals, analyzing labor market trends to inform program design, or aggregating feedback from internship placements, but the actual collaboration and negotiation remain fundamentally human.
Augmentation potentialclaude-sonnet-53/5AI can help draft partnership proposals, curriculum materials, outreach emails, and summarize industry trends to inform program development, providing moderate support.
Task automatabilityclaude-haiku-4-5-202510012/5This task requires sustained interpersonal negotiation, contextual judgment about program alignment, and relationship-building with external stakeholders. While AI could draft communications or analyze program data, the core work—collaborative decision-making and stakeholder engagement—cannot be automated end-to-end today.
Task automatabilityclaude-sonnet-51/5This requires building real relationships, negotiating with external employers, and in-person institutional networking that AI cannot perform end-to-end today.
Adoption barriersclaude-haiku-4-5-202510014/5Strong barriers exist: external stakeholders (businesses, community partners) expect human contact and accountability; institutional policy typically requires faculty or staff sign-off on partnerships; liability and contract authority rest with licensed personnel; and trust in academic-business relationships is human-mediated.
Adoption barriersclaude-sonnet-54/5Institutional accreditation processes, faculty governance, and external stakeholder trust require a credentialed human representative, creating strong organizational and reputational barriers to automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of AI systems capable of genuine stakeholder collaboration, legal/contractual oversight, and relationship management would far exceed the loaded wage of an academic administrator or faculty member performing this task.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute performing this relational/negotiation task, so cost comparison favors the human by default.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs multi-party stakeholder collaboration, program co-development, or relationship management at scale. This requires real-time negotiation, trust-building, and situated business context that current AI systems cannot handle in production.
Technical feasibility todayclaude-sonnet-51/5No deployed product manages business-community partnerships, internship placements, or program co-development autonomously; this remains a human relationship-driven activity.

Provide professional consulting services to government or industry.

9

CI 7–11 · exposure 0 · augmentation 75 · importance 3.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Consulting firms are experimenting with AI for research and drafting support, but deployment of autonomous or semi-autonomous consulting services remains limited. Firms continue to rely on human partners for client-facing strategy work, indicating slow displacement on the core consulting task.
Sector adoption velocityclaude-sonnet-52/5Higher education and consulting-adjacent academic work adopt AI tools slowly and unevenly, with pilots for research support but little production-level substitution of the consulting role itself.
Augmentation potentialclaude-haiku-4-5-202510014/5AI can meaningfully assist consultants by accelerating research, synthesizing data, drafting case studies, and preparing analysis. These tools boost consultant productivity and allow focus on higher-value client engagement and judgment, while the consultant retains responsibility and client relationship ownership.
Augmentation potentialclaude-sonnet-54/5AI substantially aids research synthesis, data analysis, report drafting, and scenario modeling, boosting a business professor-consultant's productivity while they retain client-facing judgment.
Task automatabilityclaude-haiku-4-5-202510011/5Consulting requires sustained relationship-building, negotiating client needs, recommending strategy, and high-stakes judgment tailored to specific organizational contexts. Current AI cannot independently conduct discovery meetings, establish trust, or take accountability for advice that shapes client decisions.
Task automatabilityclaude-sonnet-51/5Consulting requires original judgment, client-specific context, relationship building, and accountability for advice that AI cannot independently perform end-to-end today.
Adoption barriersclaude-haiku-4-5-202510014/5Consulting engagements typically require a licensed or credentialed professional to sign off on recommendations, maintain client confidentiality, and accept liability for advice. Organizational risk-aversion and client preference for human expertise create strong adoption barriers to fully autonomous consulting AI.
Adoption barriersclaude-sonnet-54/5Clients expect a credentialed academic/expert to stand behind recommendations, and government contracts often require named qualified individuals, creating strong professional and contractual barriers.
Cost vs. human wageclaude-haiku-4-5-202510011/5Professional consulting commands premium fees ($150–500+ per hour) because clients pay for expertise, accountability, and relationship value. AI inference cost is negligible, but integration, prompt engineering, and human oversight for high-stakes decisions make the all-in cost uncompetitive with the value delivered by experienced consultants.
Cost vs. human wageclaude-sonnet-52/5AI can cut research and drafting time but the billable value of consulting lies in the human's credentialed judgment and liability, so overall cost savings versus a human consultant's fee are limited.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI product reliably performs professional consulting end-to-end. AI can assist with research and drafting, but cannot independently engage clients, assess organizational politics, or deliver strategic consulting recommendations that clients would act upon without human intermediaries.
Technical feasibility todayclaude-sonnet-51/5No deployed product autonomously delivers professional consulting engagements to government or industry clients; AI is at best a research/drafting aid used by human consultants.

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

8

CI 0–16 · exposure 8 · augmentation 50 · importance 3.3/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Educational institutions operate under regulatory and accreditation constraints that mandate faculty supervision roles; there is no measurable displacement of supervisory responsibilities to AI in production settings today.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools for administrative and content tasks but has been slow to delegate core supervisory and mentorship responsibilities to AI systems.
Augmentation potentialclaude-haiku-4-5-202510013/5AI tools can assist supervisors with administrative tasks like scheduling, tracking milestones, plagiarism detection, and literature review support, moderately improving productivity on the administrative periphery while the human retains full supervisory authority.
Augmentation potentialclaude-sonnet-53/5AI can help track student progress, provide feedback drafts, or analyze research data, offering moderate assistance while the supervisory relationship remains human-led.
Task automatabilityclaude-haiku-4-5-202510012/5AI cannot replace the core supervision function that requires real-time judgment, mentorship, and accountability for student/researcher performance and safety. While AI could assist with scheduling, documentation, and progress tracking, the interpersonal accountability and decision-making that define this task remain firmly in human domain.
Task automatabilityclaude-sonnet-51/5Supervising students' teaching, internships, and research requires ongoing personal mentorship, evaluative judgment, and relationship-building that AI cannot perform end-to-end today.qq
Adoption barriersclaude-haiku-4-5-202510015/5Postsecondary institutions have legal, accreditation, and fiduciary obligations requiring a licensed faculty member to supervise teaching and research. Institutional liability, accreditor requirements, and accreditation standards create hard barriers to substitution.
Adoption barriersclaude-sonnet-54/5Academic institutions require credentialed faculty to supervise students for accreditation, degree certification, and liability reasons, creating strong structural barriers to automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5AI cannot perform this task at scale, making cost comparison moot; any meaningful supervision still requires a credentialed human faculty member, whose loaded wage far exceeds any marginal AI assistance cost.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute performing this supervisory function, so cost comparison favors the human by default since AI cannot deliver the output.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs unsupervised supervision of teaching, internship, or research work. Supervision inherently requires human authority, legal responsibility, and relationship-based guidance that AI systems today cannot substitute for.
Technical feasibility todayclaude-sonnet-51/5No deployed product autonomously supervises student teaching or research work; this remains inherently a human faculty responsibility with institutional accountability.

Act as advisers to student organizations.

3

CI 0–5 · exposure 0 · augmentation 25 · 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 remain highly conservative and human-centered in advising roles; there is no evidence of adoption of AI systems to replace human advisers in student organizations.
Sector adoption velocityclaude-sonnet-51/5Higher education advising roles show minimal AI displacement; this is a low-digitization, relationship-driven task with no adoption momentum.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could provide limited assistance (e.g., drafting meeting agendas, summarizing policies, or organizational planning tools), but the interpersonal core of advising—listening, mentoring, and building trust—remains fundamentally human-driven.
Augmentation potentialclaude-sonnet-52/5AI can help with scheduling, drafting communications, or brainstorming event ideas, but offers only marginal support to the core advisory relationship.
Task automatabilityclaude-haiku-4-5-202510011/5Advising student organizations requires understanding organizational dynamics, providing mentorship, and navigating interpersonal conflicts—tasks that demand human judgment, contextual awareness, and trust-building that current AI systems cannot perform end-to-end.
Task automatabilityclaude-sonnet-51/5Advising student organizations requires ongoing relationship-building, mentorship, event guidance, and institutional representation that cannot be meaningfully executed by current AI end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Institutional policies typically require a human faculty or staff member to officially advise student organizations, and liability concerns around student welfare create strong legal and organizational barriers to full automation.
Adoption barriersclaude-sonnet-54/5Institutions typically require a designated faculty member to serve as an official advisor for liability, mentorship, and accreditation purposes, creating strong organizational and quasi-regulatory barriers.
Cost vs. human wageclaude-haiku-4-5-202510011/5An AI system providing student advisory services would require significant customization, continuous oversight, and likely human escalation; the all-in cost would exceed a faculty adviser's role efficiency.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute performing this role, so cost comparison favors the human by default.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs the full role of a student organization adviser, which involves relationship-building, institutional knowledge, and real-time problem-solving in human organizational contexts.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs the role of a faculty advisor to a student club; this remains a human relational and institutional function.

Participate in campus and community events.

3

CI 0–5 · exposure 0 · augmentation 25 · importance 3.1/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Adoption velocity is near zero because the task is inherently social and participatory in ways that conflict with automation. Educational institutions have no incentive to displace human faculty presence at campus events.
Sector adoption velocityclaude-sonnet-51/5Higher education community and event engagement is a low-digitization, relationship-driven activity with minimal AI adoption pressure.
Augmentation potentialclaude-haiku-4-5-202510012/5Limited augmentation is possible—AI could help with pre-event promotion, logistics planning, or follow-up—but the core participation task offers minimal room for AI assistance while the human remains engaged.
Augmentation potentialclaude-sonnet-52/5AI can help with scheduling, event planning logistics, or drafting talking points, but offers little assistance to the actual act of participating in events.
Task automatabilityclaude-haiku-4-5-202510011/5Participating in campus and community events requires physical presence, interpersonal interaction, relationship-building, and contextual judgment that current AI systems cannot perform end-to-end. While AI could assist with event logistics, the core task of meaningful participation is irreducibly human.
Task automatabilityclaude-sonnet-51/5Physically attending and participating in campus/community events requires human presence, networking, and social engagement that AI cannot perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Strong barriers exist: institutional culture and expectations require faculty presence at events, professional norms demand personal engagement with students and community, and the institution would face reputational risk substituting AI for human participation in relationship-building activities.
Adoption barriersclaude-sonnet-54/5Participation implies representing the institution in person, building relationships and community trust, which strongly favors a human presence and institutional expectation.
Cost vs. human wageclaude-haiku-4-5-202510011/5AI has no cost advantage because the task cannot be automated; the cost comparison is moot. A human instructor must attend these events, and AI offers no substitute pathway.
Cost vs. human wageclaude-sonnet-51/5There is no AI substitute for this task, so cost comparison favors the human by default since AI cannot deliver the output at all.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI system can autonomously participate in campus or community events as a replacement for a human. This task depends on embodied presence and genuine social engagement that is not technically achievable today.
Technical feasibility todayclaude-sonnet-51/5No deployed AI product substitutes for a person's physical or social participation in events; this is inherently a human-presence task.

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 3.8/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5There is no meaningful sector adoption of AI committee service because the task is inherently tied to tenured faculty roles and institutional governance structures that are not digitizing this function.
Sector adoption velocityclaude-sonnet-51/5Higher education governance structures are slow-moving and highly resistant to structural change, with essentially no adoption of AI as committee participants.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could assist with agenda preparation, policy research, or document drafting before meetings, but the core deliberation, voting, and accountability cannot be augmented—the human must perform the governance act itself.
Augmentation potentialclaude-sonnet-53/5AI can help draft policy documents, summarize meeting minutes, research precedents, or prepare briefing materials to support a faculty member's committee work, though it cannot replace the deliberative role.
Task automatabilityclaude-haiku-4-5-202510011/5Committee service fundamentally requires human judgment on institutional policy, departmental politics, and academic governance—decisions that demand contextual understanding, negotiation, and accountability that AI cannot replicate end-to-end.
Task automatabilityclaude-sonnet-51/5Committee service requires collegial deliberation, political judgment, institutional relationship-building, and real-time consensus-building that current AI cannot perform as a participant.ractional
Adoption barriersclaude-haiku-4-5-202510015/5Academic governance is legally and structurally required to involve duly appointed human faculty members; institutional bylaws, accreditation standards, and legal liability all mandate human decision-makers and signatories on committee decisions.
Adoption barriersclaude-sonnet-55/5Committee membership typically requires faculty governance status, institutional bylaws, and shared governance norms that legally and organizationally restrict participation to qualified human faculty.
Cost vs. human wageclaude-haiku-4-5-202510011/5Committee service is a governance duty tied to faculty position, not a discrete task priced against labor; substituting AI would require removing human faculty authority entirely, making the comparison inapplicable.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute performing this role, so cost comparison favors the human entirely; AI cannot replace the committee seat itself.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed system can autonomously serve on committees or make binding institutional decisions; this requires legal authority, fiduciary responsibility, and human presence that no current AI product possesses.
Technical feasibility todayclaude-sonnet-51/5No deployed AI product substitutes for a human faculty member's presence and voting/deliberative role on institutional committees 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.