Computer Science Teachers, Postsecondary

25-1021.00
Median wage $96,980/yr35,480 employed (US)Rank #280 of 923 scored · top 30% by substitution

Teach courses in computer science. May specialize in a field of computer science, such as the design and function of computers or operations and research analysis. Includes both teachers primarily engaged in teaching and those who do a combination of teaching and research.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution34
Exposure31
Augmentation65

Substitution — the headline: capability discounted by cost, barriers and adoption.

Exposure — technical capability alone, regardless of whether anyone deploys it.

Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.

Tasks on the substitution scale

26 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

15%

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%31

panel mean rating 2.3/5 → substitution pressure 31/100

Technical feasibility todayw 20%31

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

Cost vs. human wagew 15%38

panel mean rating 2.5/5 → substitution pressure 38/100

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%35

panel mean rating 2.4/5 → substitution pressure 35/100

Task breakdown (26 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.

90

CI 85–95 · exposure 100 · augmentation 63 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510015/5Adoption is near-universal in postsecondary education; essentially all colleges and universities use automated student information systems and LMS platforms for this purpose, making it one of the earliest and most deeply embedded institutional automations.
Sector adoption velocityclaude-sonnet-55/5Higher education has nearly universally adopted digital LMS platforms for record-keeping, representing deep, mature adoption across the sector.
Augmentation potentialclaude-haiku-4-5-202510013/5AI assists instructors by automatically organizing and flagging attendance patterns or grade anomalies, reducing manual review time, but the core task of record maintenance is already delegated to automated systems rather than AI augmenting human judgment.
Augmentation potentialclaude-sonnet-54/5AI-enabled gradebooks and LMS tools substantially streamline record maintenance for instructors, though a human still needs to input grades and verify accuracy.
Task automatabilityclaude-haiku-4-5-202510015/5This task is highly automatable today. Modern Learning Management Systems (Canvas, Blackboard, etc.) and student information systems (Workday, Banner) can automatically track attendance, synchronize grades, and maintain records with minimal human intervention, easily exceeding the 50% time-saving threshold.
Task automatabilityclaude-sonnet-55/5Recording and maintaining attendance and grades is a structured data-entry task already fully handled by learning management systems (LMS) and gradebook software with automated calculation and record-keeping.
Adoption barriersclaude-haiku-4-5-202510014/5Significant barriers exist: FERPA (Family Educational Rights and Privacy Act) compliance and institutional policy requirements mandate careful handling and authorization of student records, and many institutions require human verification and sign-off on grade reporting despite system automation.
Adoption barriersclaude-sonnet-52/5Some institutional policy requires instructor verification/sign-off on final grades, but the underlying record maintenance itself faces minimal regulatory or licensing barriers.
Cost vs. human wageclaude-haiku-4-5-202510015/5The cost of automated record-keeping (system licensing, occasional data entry, minimal oversight) is orders of magnitude cheaper than paying an administrative staff member or instructor time to manually maintain attendance, grades, and records.
Cost vs. human wageclaude-sonnet-55/5Automated LMS record-keeping costs a small fraction of the instructor time it would take to manually maintain these records, representing well over an order-of-magnitude savings.
Technical feasibility todayclaude-haiku-4-5-202510015/5Deployed products reliably perform this task at scale in educational institutions worldwide. Student information systems and LMS platforms are mature, production-grade solutions that are standard in postsecondary settings.
Technical feasibility todayclaude-sonnet-55/5Products like Canvas, Blackboard, and Moodle already perform automated grade tracking, attendance logging, and record maintenance reliably in production at virtually all universities.

Compile bibliographies of specialized materials for outside reading assignments.

84

CI 71–97 · exposure 83 · augmentation 100 · importance 3.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Higher education and information-rich sectors show moderately fast AI adoption; faculty are already using generative AI for content preparation, and bibliography generation is a low-friction, visible use case in digitized academic environments.
Sector adoption velocityclaude-sonnet-53/5Higher education is adopting AI tools for course prep and content curation, but adoption is uneven and mostly informal/pilot-level rather than institutionalized workflow integration.
Augmentation potentialclaude-haiku-4-5-202510015/5AI dramatically augments faculty productivity by rapidly generating preliminary bibliographies that instructors can refine, expand, or filter—maintaining pedagogical control while eliminating routine compilation work.
Augmentation potentialclaude-sonnet-55/5AI tools substantially speed up bibliography compilation by suggesting relevant sources, summarizing content, and formatting citations, while the instructor still curates and validates selections.
Task automatabilityclaude-haiku-4-5-202510015/5Compiling bibliographies involves structured information retrieval, organization, and formatting—tasks where current AI systems (search agents, LLMs, citation managers) can reliably identify, filter, and format academic materials at speed with >50% time savings and equal or better quality than manual compilation.
Task automatabilityclaude-sonnet-54/5AI can generate topical reading lists and bibliographies quickly given a course topic, drawing on broad knowledge of the field, though verifying accuracy and relevance still requires human review.:
Adoption barriersclaude-haiku-4-5-202510012/5Few regulatory or legal barriers exist; instructors retain autonomy over assignment design and can choose to use or oversee AI-generated bibliographies. However, some institutional preference for human curation and QA adds minor friction.
Adoption barriersclaude-sonnet-51/5There is no licensing, liability, or regulatory requirement tying this task to a credentialed human; it's an administrative/preparatory task with no legal barrier to AI assistance.
Cost vs. human wageclaude-haiku-4-5-202510015/5The cost of AI inference and integration for bibliography compilation is negligible (pennies per task) compared to a faculty member's loaded hourly wage ($60–150+), making AI at least 100× cheaper for this routine task.
Cost vs. human wageclaude-sonnet-55/5Generating a bibliography via an AI tool costs a fraction of a cent compared to the faculty time otherwise spent searching and compiling reading lists.
Technical feasibility todayclaude-haiku-4-5-202510015/5Deployed products like ChatGPT, Claude, Perplexity, and integrated citation management tools (Zotero, Mendeley with AI features) demonstrably perform bibliography compilation at scale in production environments, with reliable performance on standard academic formatting and source identification.
Technical feasibility todayclaude-sonnet-53/5General-purpose LLMs and citation tools can produce bibliography drafts today, but they sometimes hallucinate sources or miss the latest specialized materials, so reliability is imperfect in production use.

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

77

CI 76–79 · exposure 75 · augmentation 100 · importance 4.6/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Higher education and information sectors show rapid AI adoption; surveys and reports document widespread use of AI writing tools by faculty for admin and instructional tasks. Adoption is already visible in practice, not merely in pilots.
Sector adoption velocityclaude-sonnet-53/5Higher education is adopting AI tools for content creation at a moderate pace, with growing but inconsistent institutional policies and individual faculty adoption.
Augmentation potentialclaude-haiku-4-5-202510015/5AI dramatically augments productivity: faculty can iterate on syllabus design, generate diverse problem sets, and adapt materials across courses in minutes rather than hours, keeping human judgment central to pedagogical intent and content review.
Augmentation potentialclaude-sonnet-55/5AI substantially speeds up drafting syllabi, assignments, and handouts while instructors retain control over final content, learning objectives, and academic rigor.
Task automatabilityclaude-haiku-4-5-202510014/5AI can generate syllabi, homework assignments, and handouts at scale with minimal human input, meeting the ≥50% time-saving threshold. Current LLMs reliably produce structured course materials from brief specifications, though human review for accuracy and institutional alignment remains necessary.
Task automatabilityclaude-sonnet-54/5LLMs can draft syllabi, homework problem sets, and handouts for standard CS curricula quickly, requiring mostly review and light editing rather than creation from scratch.5
Adoption barriersclaude-haiku-4-5-202510012/5Few hard regulatory or legal barriers exist; course materials are not professionally licensed outputs. Some institutional governance (curriculum committees, departmental review) and academic norms favoring human authorship create friction, but no legal requirement mandates human-only preparation.
Adoption barriersclaude-sonnet-52/5No licensing requirement blocks AI-assisted material prep, though faculty typically retain authorship responsibility and academic integrity/accreditation norms create mild institutional friction.
Cost vs. human wageclaude-haiku-4-5-202510015/5AI inference cost per syllabus or assignment set is negligible (cents) compared to the loaded hourly wage of a professor ($50–100+ per hour), yielding at least a 10–100× cost advantage once integration overhead is amortized.
Cost vs. human wageclaude-sonnet-55/5Generating draft syllabi and assignments via AI costs pennies compared to hours of faculty or TA time at academic wage rates.
Technical feasibility todayclaude-haiku-4-5-202510014/5Deployed products (ChatGPT, Claude, specialized educational platforms) demonstrably generate course materials in production. While output quality varies and requires editing, many educators already use these tools operationally to draft and refine syllabi and assignments.
Technical feasibility todayclaude-sonnet-54/5Tools like ChatGPT, Claude, and specialized ed-tech products are widely used by instructors today to generate course materials, though instructors still customize for pedagogy and institutional requirements.

Develop and maintain Web sites for online courses.

72

CI 64–80 · exposure 67 · augmentation 100 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Higher education and tech-forward institutions are actively adopting AI coding assistants and automated CMS tools for course delivery. Universities already integrate LMS automation and developer tools into production workflows, reflecting relatively fast adoption in the information/education sectors.
Sector adoption velocityclaude-sonnet-53/5Higher education is a mixed-adoption sector—some institutions use modern AI-assisted web/LMS tools while others rely on legacy systems and manual processes, so adoption is moderate and uneven.
Augmentation potentialclaude-haiku-4-5-202510015/5AI significantly augments faculty and instructional designers by generating code templates, automating routine updates, suggesting accessibility improvements, and handling technical maintenance tasks. The human instructor remains in the loop for pedagogical decisions while AI transforms productivity on the technical components.
Augmentation potentialclaude-sonnet-55/5AI tools substantially speed up creating layouts, generating content stubs, debugging code, and maintaining consistency across course pages, while instructors retain control over final content and pedagogy.
Task automatabilityclaude-haiku-4-5-202510013/5AI can automate significant portions of website development and maintenance—code generation, template selection, basic content updates, and testing—but typically requires human oversight for course design, pedagogical decisions, and platform-specific customization. The task involves both technical (automatable) and instructional design (human-required) elements, falling short of the 50% time-saving threshold for the complete task.
Task automatabilityclaude-sonnet-54/5Building and maintaining course websites (content structure, LMS pages, basic HTML/CSS, course materials organization) is largely templated work that AI coding assistants and site builders can do end-to-end with significant time savings, though custom integrations still need human oversight.
Adoption barriersclaude-haiku-4-5-202510012/5Few hard regulatory or legal barriers exist; faculty and IT departments can adopt AI-assisted tools freely. However, institutional inertia, accreditation requirements for course quality assurance, and IT governance policies create moderate friction to rapid substitution.
Adoption barriersclaude-sonnet-51/5No licensing, legal, or human-contact requirement governs building/maintaining a course website; it's purely an administrative/technical task with no regulatory protection.
Cost vs. human wageclaude-haiku-4-5-202510014/5Cloud-based web development tools, code generation APIs, and automated maintenance scripts cost a fraction of a full-time developer or instructional technician salary. AI-assisted approaches are substantially cheaper than manual website management, though not quite order-of-magnitude savings when integration and oversight are factored in.
Cost vs. human wageclaude-sonnet-54/5AI-assisted site building and maintenance tools cost a small fraction of a faculty member's or web developer's time compared to manual site development and updates.
Technical feasibility todayclaude-haiku-4-5-202510014/5Mature products like GitHub Copilot, generative code tools, CMS automation, and AI-assisted web builders perform website development and maintenance reliably in production. However, deployment at higher education institutions often requires integration with specific learning management systems and institutional workflows, preventing a perfect 5.
Technical feasibility todayclaude-sonnet-54/5Deployed tools like LMS platforms (Canvas, Moodle) with AI-assisted authoring, website builders, and AI coding assistants (e.g., GitHub Copilot, low-code site builders) are already used in production to build and update course sites reliably.

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

65

CI 55–75 · exposure 62 · augmentation 88 · importance 4.6/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Universities and online education platforms have rapidly adopted automated exam and grading tools over the past decade, with LMS integration now standard in higher education. This sector is highly digitized and adoption is deep and accelerating, particularly post-COVID.
Sector adoption velocityclaude-sonnet-53/5Higher education has adopted autograding and quiz-generation tools moderately, particularly in CS departments, but broad, deep integration of AI into exam administration remains uneven across institutions.
Augmentation potentialclaude-haiku-4-5-202510015/5AI significantly augments instructors by handling bulk grading, generating feedback summaries, flagging outlier performance, and freeing time for higher-level assessment tasks. The instructor remains in control of rubric setting and can review AI-flagged borderline cases, substantially raising productivity.
Augmentation potentialclaude-sonnet-54/5AI substantially aids in generating question banks, detecting plagiarism, and autograding coding assignments, greatly speeding up an instructor's workflow while they retain final grading authority.
Task automatabilityclaude-haiku-4-5-202510014/5AI systems can fully automate exam compilation from question banks, generate grading rubrics, and automatically grade objective and short-answer questions with high consistency. The main remaining human element is oversight of fairness and calibration, which current systems can significantly streamline, meeting the 50% time-saving threshold for large-scale exam administration.
Task automatabilityclaude-sonnet-53/5AI can draft exam questions and auto-grade objective/coding assignments effectively, but administering exams and grading nuanced free-response or code-design work still requires human oversight for validity and academic integrity.'
Adoption barriersclaude-haiku-4-5-202510012/5Few legal or regulatory barriers exist to automating grading; the main friction is institutional (some faculty prefer manual grading, students may demand human review appeals) and pedagogical (instructors may retain control to ensure alignment with learning objectives). These are soft friction points, not hard blockers.
Adoption barriersclaude-sonnet-52/5No licensing barrier prevents AI-assisted grading, though academic integrity policies and institutional accreditation standards create some oversight requirements for final grade determination.
Cost vs. human wageclaude-haiku-4-5-202510014/5Learning management systems and automated grading tools cost far less per exam than faculty time spent grading large classes. For an instructor grading 300 exams at $50/hour loaded wage versus ~$2–5 in system costs, the AI cost ratio is substantially favorable, often orders of magnitude cheaper at scale.
Cost vs. human wageclaude-sonnet-53/5Autograding tools reduce grading time substantially for standardized code tests, but exam compilation and oversight of AI-generated content still require faculty time, keeping costs only moderately lower than fully manual grading.
Technical feasibility todayclaude-haiku-4-5-202510014/5Multiple mature products (Turnitin, Blackboard, Canvas, specialized AI grading tools) demonstrably handle exam creation, distribution, and automated grading in production at scale across universities. Some error rates remain on complex essay grading, but the core workflow is reliable and widely deployed.
Technical feasibility todayclaude-sonnet-53/5Products like autograders, LMS quiz tools, and AI-assisted question generators (e.g., Gradescope, GitHub Classroom autograding) are deployed in production, but full compilation and grading of complex CS assignments still needs instructor review.

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

61

CI 35–87 · exposure 58 · augmentation 75 · importance 3.6/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Educational institutions and corporate procurement teams have adopted e-procurement and procurement automation tools at significant scale; many universities already use automated purchase order systems that reduce manual procurement work substantially.
Sector adoption velocityclaude-sonnet-52/5Higher education administrative processes adopt AI slowly outside of pilot programs; procurement systems remain largely manual or use traditional e-procurement software, not AI agents.
Augmentation potentialclaude-haiku-4-5-202510015/5AI can powerfully assist by automatically surfacing vendor options, comparing prices and specifications, flagging inventory levels, and generating draft orders—allowing a human procurement officer to validate and authorize faster than manual research and order composition.
Augmentation potentialclaude-sonnet-53/5AI can meaningfully assist by suggesting textbooks, comparing curricula-aligned resources, and drafting supply lists, improving efficiency of the selection portion of the task.
Task automatabilityclaude-haiku-4-5-202510015/5This task involves straightforward procurement workflows: identifying materials, checking inventory, placing orders with vendors, and tracking delivery. Current AI agents can fully automate supplier searches, catalog lookups, order placement through e-commerce APIs, and purchase order generation—achieving ≥50% time savings at equal quality.
Task automatabilityclaude-sonnet-52/5AI can help identify and recommend textbooks or equipment based on course needs, but actually selecting appropriate materials requires pedagogical judgment and the physical/procurement act of obtaining supplies cannot be automated end-to-end.6
Adoption barriersclaude-haiku-4-5-202510012/5Most procurement workflows, especially in educational institutions, still require a human approver or purchase authority signature on orders and have institutional compliance/approval gates. However, these are oversight barriers rather than hard licensing requirements, so substitution is possible with modest process changes.
Adoption barriersclaude-sonnet-52/5No licensing requirement, but institutional purchasing policies, budget approval chains, and vendor relationships create organizational friction that slows automation.
Cost vs. human wageclaude-haiku-4-5-202510015/5The cost of an AI agent to perform searches, comparisons, and order placement is minimal compared to the loaded hourly wage of a postsecondary faculty member or administrative staff member who would traditionally handle this task.
Cost vs. human wageclaude-sonnet-52/5AI assistance for research/comparison is cheap, but the procurement, vendor negotiation, and physical acquisition steps still require human administrative labor, keeping overall cost comparable to human-driven processes.
Technical feasibility todayclaude-haiku-4-5-202510014/5Procurement automation systems and e-procurement platforms are widely deployed in educational institutions and corporate environments. While mature systems exist and perform reliably, some integration friction with legacy institutional procurement policies and vendor systems means the rating is not quite perfect-5.
Technical feasibility todayclaude-sonnet-52/5No deployed product handles full procurement workflows for academic course materials; recommendation tools exist but are not integrated into purchasing/ordering systems used by postsecondary faculty.

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

59

CI 54–64 · exposure 55 · augmentation 88 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510013/5Higher education has seen gradual adoption of automated grading for coding and multiple-choice content, with pilots expanding to essay analysis; adoption is institutional but not yet industry-wide standard.
Sector adoption velocityclaude-sonnet-53/5Higher education has moderate AI adoption for grading support (autograders in CS courses are common), but full trust in AI-only grading of papers and labs is still cautious and uneven across institutions.
Augmentation potentialclaude-haiku-4-5-202510014/5AI significantly assists instructors by pre-grading assignments, flagging outliers, and providing detailed feedback templates, allowing educators to focus effort on complex conceptual questions and student mentoring rather than routine scoring.
Augmentation potentialclaude-sonnet-55/5AI substantially speeds up grading through autograding of code, plagiarism detection, and draft feedback generation, letting instructors focus on edge cases and final review.
Task automatabilityclaude-haiku-4-5-202510013/5AI can automate grading of objective components (code correctness, syntax, test cases) and provide preliminary assessments of written work, but evaluating conceptual understanding, reasoning quality, and originality requires human judgment—current systems cannot reliably replace the full task while maintaining educational integrity.
Task automatabilityclaude-sonnet-53/5AI can grade code assignments via automated testing and provide feedback on structure/style, and LLMs can grade essays/papers reasonably well, but nuanced grading of open-ended lab work and edge cases still needs instructor review, so only partial time savings at equal quality is achieved.'
Adoption barriersclaude-haiku-4-5-202510013/5Educational institutions have growing comfort with AI grading for objective components, but pedagogical norms, accreditation expectations, and instructor preference for human review of conceptual work create moderate friction against full automation.
Adoption barriersclaude-sonnet-52/5No licensing requirement for grading, but academic integrity policies, appeals processes, and institutional norms around instructor accountability for grades create moderate friction against full automation.
Cost vs. human wageclaude-haiku-4-5-202510014/5AI-assisted grading tools are inexpensive to deploy and can process high volumes at a fraction of instructor labor cost; integration costs are modest, though oversight time reduces the ratio advantage.
Cost vs. human wageclaude-sonnet-54/5Automated grading tools and LLM-based feedback are inexpensive per assignment compared to instructor or TA grading time, though setup and rubric calibration add some cost.
Technical feasibility todayclaude-haiku-4-5-202510014/5Deployed products (plagiarism checkers, automated code grading systems like GradeScope, LMS integrations) handle portions of this task reliably in production; however, they typically focus on specific modalities and require human review of subjective elements.
Technical feasibility todayclaude-sonnet-53/5Products like autograders (Gradescope, GitHub Classroom autograding, CodeGrade) are used in production for code assignments, and LLM-based feedback tools exist, but reliability for grading nuanced conceptual work or detecting plagiarism/originality issues remains imperfect.

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

43

CI 41–45 · exposure 25 · augmentation 75 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510013/5Adoption of AI-assisted literature monitoring and summarization in academic circles is growing but uneven; many use tools like arXiv alerts and summarization assistants, but deep professional engagement remains largely human-driven. Production adoption is moderate rather than rapid.
Sector adoption velocityclaude-sonnet-53/5Higher education and academia are adopting AI research tools (e.g., for literature review) at a moderate pace, though slower than tech-sector norms due to conservative institutional culture.
Augmentation potentialclaude-haiku-4-5-202510014/5AI significantly assists this task through automated paper summarization, keyword tracking, conference program filtering, and citation analysis, allowing professors to cover more ground faster while maintaining their own judgment and collegial input. The human expert remains in the loop and becomes more productive.
Augmentation potentialclaude-sonnet-54/5AI literature search, summarization, and alert tools substantially speed up staying current with the field, meaningfully augmenting this task even though full replacement isn't feasible.
Task automatabilityclaude-haiku-4-5-202510012/5AI can assist with literature summarization and conference abstract digestion, but the core task—staying abreast through dialogue with colleagues and evaluating field direction—requires human judgment, relationship-building, and contextual interpretation of emerging trends. No current system can replace the networking and discernment aspects meaningfully.
Task automatabilityclaude-sonnet-52/5AI can help summarize papers and surface relevant literature, but genuine professional engagement, conference networking, and synthesizing field developments into teaching judgment still requires human involvement.
Adoption barriersclaude-haiku-4-5-202510012/5There are minimal legal or regulatory barriers to using AI for literature review and professional development support. However, professional norms and intrinsic motivation to personally engage with the field create practical friction against full automation.
Adoption barriersclaude-sonnet-52/5No licensing barrier, but professional norms around participating in academic community, networking, and peer engagement create moderate friction to full automation.
Cost vs. human wageclaude-haiku-4-5-202510014/5AI-powered literature monitoring and summarization tools are very cheap compared to the time cost of manual reading and conference attendance. A researcher's time spent on this task is expensive; AI can dramatically reduce that cost per unit of information intake.
Cost vs. human wageclaude-sonnet-53/5AI-assisted literature review and summarization tools are cheap relative to a professor's time spent reading, but the full task including networking and conference attendance isn't substitutable, limiting cost savings.
Technical feasibility todayclaude-haiku-4-5-202510012/5While AI can summarize papers and generate topic alerts, no deployed product reliably performs the full task of 'keeping abreast' in the way a human academic does. Tools exist for literature monitoring but cannot replicate collegial discussion or the synthesis and judgment required to assess field significance.
Technical feasibility todayclaude-sonnet-52/5Tools like AI paper summarizers and literature alert systems exist and are used, but they cover only part of the task; conference participation and colleague discussion are not automatable by deployed products.

Act as advisers to student organizations.

36

CI 11–60 · exposure 33 · augmentation 63 · importance 2.9/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Adoption of AI advising tools in higher education remains in the pilot and experimental phase across most institutions; while some forward-looking universities are testing chatbots, the broader sector is slow to adopt, and systematic displacement of advising roles has not yet occurred.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools for course content and administration, but faculty advising roles for student organizations show negligible AI displacement.
Augmentation potentialclaude-haiku-4-5-202510014/5AI tools can significantly augment student advising by drafting responses, organizing information, tracking requests, and surfacing patterns in student needs, allowing human advisers to focus on mentorship and high-judgment decisions while the AI handles routine administrative and informational tasks.
Augmentation potentialclaude-sonnet-53/5AI can help advisors with scheduling, drafting communications, budgeting suggestions, or event planning ideas, moderately aiding but not central to the advisory relationship.
Task automatabilityclaude-haiku-4-5-202510014/5An AI system could reliably handle much of the advisory workload by drafting guidance documents, responding to common questions, scheduling meetings, and flagging issues requiring escalation, though the nuanced mentorship and relationship-building that define real student advising would require human oversight, achieving approximately 50–70% time savings at comparable quality.
Task automatabilityclaude-sonnet-51/5Advising student organizations involves relationship-building, mentorship, event guidance, and real-time interpersonal judgment that AI cannot perform end-to-end today.
Adoption barriersclaude-haiku-4-5-202510013/5While there is no strict legal requirement that a human must perform student advising, institutional culture, accreditation expectations, and student preferences for authentic mentorship create moderate friction; additionally, liability concerns around AI providing guidance on sensitive matters (health, discrimination, academic standing) discourage full automation.
Adoption barriersclaude-sonnet-54/5Institutions typically require a named faculty/staff advisor for liability, signature authority, and institutional accountability, creating strong organizational and quasi-regulatory barriers.
Cost vs. human wageclaude-haiku-4-5-202510014/5The per-task inference cost of running an LLM-based advising assistant is low (pennies to dollars), and the integration overhead is modest; the loaded hourly cost of a faculty member or professional advisor ($60–$120+) is substantially higher, creating a cost advantage of 5–20× for routine advising tasks.
Cost vs. human wageclaude-sonnet-52/5There is no viable AI substitute providing equivalent output, so cost comparison favors the human despite AI's low per-query cost for peripheral tasks.
Technical feasibility todayclaude-haiku-4-5-202510013/5LLM-based chatbots and email assistants can draft responses to student questions and schedule tasks today, and some universities have deployed such tools; however, they still lack the contextual judgment and institutional knowledge to handle sensitive issues (mental health, conflicts, funding decisions) without human review, making real-world reliability limited.
Technical feasibility todayclaude-sonnet-51/5No deployed product acts as a substitute faculty advisor for student clubs; this remains outside current product scope.

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

30

CI 28–32 · exposure 25 · augmentation 88 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510013/5Universities and research organizations are actively experimenting with AI-assisted research tools (literature review, data analysis, writing support), but actual adoption of AI for independent research direction and publication remains in pilot phases. Significant institutional and epistemic resistance persists.
Sector adoption velocityclaude-sonnet-53/5Higher education and academic research are adopting AI writing/research assistants at a moderate pace, with pilots and tool use common but full workflow integration still limited.
Augmentation potentialclaude-haiku-4-5-202510014/5AI significantly augments researcher productivity through rapid literature synthesis, statistical analysis, manuscript drafting, and hypothesis testing—allowing humans to focus on creative direction, novelty assessment, and validation. Researchers increasingly rely on these tools to accelerate workflow while maintaining intellectual control.
Augmentation potentialclaude-sonnet-55/5AI substantially boosts productivity in literature synthesis, data analysis, coding, and manuscript drafting, making it a powerful augmentation tool throughout the research and publishing process.
Task automatabilityclaude-haiku-4-5-202510012/5Research generation and publication workflows have some automatable elements (literature review synthesis, data analysis, manuscript formatting), but the creative hypothesis formation, experimental design decisions, and novel theoretical contributions that define publishable research require sustained human expertise and judgment. Current AI cannot reliably generate original, peer-review-passing research end-to-end.
Task automatabilityclaude-sonnet-52/5AI can assist with literature review, coding experiments, and drafting text, but original research design, novel contribution, and rigorous validation still require human expertise and cannot be fully automated end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5Strong barriers exist: research institutions maintain strict policies requiring human accountability for novelty and integrity; peer review and authorship norms legally and professionally mandate human researchers; publishing venues require human sign-off on findings; and liability for false claims falls on human authors and institutions.
Adoption barriersclaude-sonnet-53/5No strict licensing requirement, but academic norms, peer review, authorship accountability, and institutional expectations of human intellectual contribution create meaningful friction against full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5Research-grade AI (literature analysis, data processing, writing assistance) has moderate upfront costs for setup and integration; human researchers remain essential for directing effort, validating results, and ensuring quality. The all-in cost per published research output remains comparable to or exceeds the cost of researcher time.
Cost vs. human wageclaude-sonnet-52/5AI tools reduce time on subtasks like drafting and summarizing, but the overall research process still requires substantial paid human expert time for design, experimentation, and validation, keeping costs comparable to human-led work.
Technical feasibility todayclaude-haiku-4-5-202510012/5While AI tools assist with literature summarization, data processing, and writing drafts, no deployed system performs independent research conception, execution, and publication without substantial human oversight and validation. Research institutions and journals require human accountability for findings and originality claims.
Technical feasibility todayclaude-sonnet-52/5Products like literature-review assistants and writing tools are used in research workflows, but no deployed system autonomously conducts full research studies and publishes reliable, peer-reviewed findings.

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

29

CI 25–34 · exposure 25 · augmentation 63 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Postsecondary education remains comparatively slow in AI adoption; while some institutions experiment with AI-assisted content generation, systematic curriculum planning and revision remain faculty-driven and resistant to outsourcing or automation.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts new tools slowly due to shared governance and accreditation cycles, with AI assistance in curriculum design still in early pilot stages.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can usefully assist faculty by drafting syllabus templates, generating learning objective options, and suggesting reading lists or assignment ideas, raising productivity on material-generation tasks while faculty retain judgment over pedagogical design and institutional fit.
Augmentation potentialclaude-sonnet-54/5AI tools are increasingly useful for brainstorming course content, generating example materials, and revising syllabi language, meaningfully speeding up an instructor's planning work.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can generate draft syllabi, learning objectives, and course materials at scale, curriculum planning fundamentally requires domain expertise, pedagogical judgment, and alignment with institutional constraints that current AI systems cannot reliably handle end-to-end without substantial human oversight and revision.
Task automatabilityclaude-sonnet-52/5AI can draft syllabi and suggest content updates, but institutional curriculum planning requires accreditation alignment, pedagogical judgment, and stakeholder negotiation that current AI cannot fully replicate end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5Postsecondary institutions have accreditation requirements, faculty governance norms, and institutional review processes that legally and organizationally mandate human (faculty) ownership of curriculum decisions; shared governance traditions create high organizational friction against full automation.
Adoption barriersclaude-sonnet-53/5Faculty governance, accreditation standards, and departmental approval processes create real institutional friction, though no formal license is required to draft curriculum content.
Cost vs. human wageclaude-haiku-4-5-202510013/5AI inference for generating draft materials is cheap, but the full task requires human expert review, iteration, and institutional integration; the loaded cost of human curriculum design expertise (faculty time) may not be decisively undercut by AI assistance alone.
Cost vs. human wageclaude-sonnet-52/5AI can cheaply draft materials, but the human oversight, review, and approval cycles needed for accredited curricula mean overall cost savings are modest.
Technical feasibility todayclaude-haiku-4-5-202510012/5AI content-generation tools (e.g., ChatGPT for drafting lectures) exist and see limited use, but deployed products lack the ability to coherently plan curricula, evaluate their effectiveness through student outcomes, and revise systematically—core elements of this task.
Technical feasibility todayclaude-sonnet-52/5Some ed-tech tools help generate course outlines or assessments, but no deployed product autonomously plans and revises full curricula reliably at scale in universities.

Participate in student recruitment, registration, and placement activities.

28

CI 25–30 · exposure 25 · augmentation 50 · importance 3.8/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Higher education has been slow to adopt AI for core student-facing recruitment and placement; adoption remains limited to supplemental tasks like form processing and scheduling. Most institutions still rely heavily on human advisors and recruiters for these activities, with few production deployments of AI agents in this space.
Sector adoption velocityclaude-sonnet-52/5Higher education admissions offices are adopting AI chatbots and CRM tools at a moderate pace, but faculty involvement in recruitment and placement is a slower-adopting, relationship-driven domain.
Augmentation potentialclaude-haiku-4-5-202510013/5AI tools can assist by summarizing student profiles, flagging placement opportunities, or drafting recruitment communications, raising teacher-advisor productivity in administrative aspects. However, the core judgment and relationship work limit the depth of augmentation—AI remains a useful but secondary tool in this task.
Augmentation potentialclaude-sonnet-53/5AI can help draft recruitment materials, analyze applicant data, or suggest placement matches, providing useful support while faculty retain decision-making authority.
Task automatabilityclaude-haiku-4-5-202510012/5Student recruitment and placement involve relationship-building, persuasion, and nuanced judgment about fit that remain difficult for AI. Automated email campaigns and basic registration workflows can handle narrow administrative parts, but the core activities—evaluating student potential, counseling on career paths, negotiating placement—require human judgment and cannot meet the 50% time-saving bar end-to-end.
Task automatabilityclaude-sonnet-52/5This task involves interpersonal outreach, admissions committee work, and institutional judgment calls about student fit that AI cannot fully replace, though some administrative sub-steps could be assisted.dicos
Adoption barriersclaude-haiku-4-5-202510014/5Recruitment and placement activities often require trusted human judgment, institutional accountability, and relationships with employers. University policies, accreditation standards, and employer expectations typically mandate human involvement in student advising and placement decisions, creating organizational and reputational barriers to full automation.
Adoption barriersclaude-sonnet-53/5No strict licensing requirement, but institutional policy, personal relationship-building with prospective students, and human judgment in admissions/placement decisions create organizational friction against full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5Building and maintaining AI recruitment and placement systems (fine-tuning, integration, oversight) still costs significantly more than leveraging existing administrative staff or third-party services. The human labor involved in recruitment and placement is relatively inexpensive compared to the integration cost of intelligent automation.
Cost vs. human wageclaude-sonnet-52/5The faculty-facing parts of this task (interviews, advising, personal outreach) still require human time; AI tools may reduce some administrative cost but not the core relational activity, so overall cost savings are modest.
Technical feasibility todayclaude-haiku-4-5-202510012/5While CRM systems and registration portals exist, autonomous AI agents reliably executing recruitment conversations, assessing student suitability, or negotiating placements with employers remain at a pilot or research stage. No mature production system demonstrates reliable end-to-end performance of these activities.
Technical feasibility todayclaude-sonnet-52/5Some CRM and enrollment-management software uses AI for lead scoring or chatbot outreach, but faculty participation in recruitment/placement decisions remains a human-driven, judgment-based activity with no mature end-to-end product.

Write grant proposals to procure external research funding.

28

CI 25–30 · exposure 25 · augmentation 75 · importance 3.8/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Higher education remains cautious about AI in research authorship due to integrity concerns and the high stakes of grant success. While some faculty experiment with AI writing assistants, institutional policies and funder guidelines on AI disclosure are still emerging, limiting deep adoption.
Sector adoption velocityclaude-sonnet-52/5Higher education is a relatively slow-adopting sector for AI-driven administrative and writing tasks, with grant writing specifically still handled largely by faculty and grant offices with only incidental AI assistance.
Augmentation potentialclaude-haiku-4-5-202510014/5AI tools meaningfully assist with outlining, drafting literature reviews, checking grammar, and generating alternative phrasings—raising faculty productivity in proposal writing. The human researcher remains the decision-maker, but AI-assisted drafting can substantially accelerate the writing process.
Augmentation potentialclaude-sonnet-54/5AI can meaningfully speed up drafting narrative sections, formatting, literature summaries, and editing, letting faculty focus on technical content and strategy, making it a strong augmentation tool even if not a full automation solution.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can draft sections of grant proposals (e.g., literature reviews, methodology descriptions), the task requires significant human judgment about research vision, institutional fit, and funder priorities. Current AI systems cannot reliably generate the persuasive, coherent narrative arc or novel scientific claims that distinguish competitive proposals, and human revision and domain expertise remain essential.
Task automatabilityclaude-sonnet-52/5AI can draft sections of grant proposals but crafting a competitive, tailored proposal requires deep domain expertise, strategic framing, and knowledge of funder priorities that current AI cannot fully replicate end-to-end at equal quality without heavy human revision.rait's below the 50% time-saving-at-equal-quality bar.
Adoption barriersclaude-haiku-4-5-202510014/5Grant proposals must be authored by credentialed researchers and signed by institutional officials; the funder's review process assumes human intellectual accountability. Institutional review, compliance with federal requirements (e.g., NIH biosketches), and the legal and reputational risk of misrepresentation create hard barriers to full automation.
Adoption barriersclaude-sonnet-53/5No licensing requirement, but institutional and funder norms typically require the PI's own scholarly voice, accountability, and signature; funding agencies expect genuine intellectual ownership, creating moderate friction against full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5The cost of AI systems plus human oversight and revision to produce a submission-ready grant proposal is comparable to or exceeds the time cost of a faculty member writing it directly, especially when accounting for the need to verify claims, ensure alignment with funder requirements, and integrate institutional context.
Cost vs. human wageclaude-sonnet-52/5AI tools are cheap per use, but the human oversight, subject-matter expertise, and iterative revision needed to produce a fundable proposal keep the effective cost comparable to or only modestly below a human-led process.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed product reliably writes complete, fundable grant proposals end-to-end. AI writing tools assist with drafting and editing, but grant review processes demand human authorship, accountability, and domain-specific credibility that current systems cannot substitute. Institutional grant offices do not yet rely on AI to produce submission-ready proposals.
Technical feasibility todayclaude-sonnet-52/5Some academics use AI writing assistants (ChatGPT, Grammarly, etc.) to draft or polish proposal text, but no deployed product reliably produces fundable grant proposals autonomously; human expertise remains central.

Provide professional consulting services to government or industry.

26

CI 25–28 · exposure 25 · 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 in government and industry remains highly relationship-driven and slow to automate. While some firms use AI for preliminary analysis, actual consulting delivery remains human-centric and adoption of full AI replacement is minimal.
Sector adoption velocityclaude-sonnet-53/5Higher education and professional services are moderately adopting AI tools for research and drafting support, but full-scale AI-driven consulting engagements remain rare and mostly pilot-stage.
Augmentation potentialclaude-haiku-4-5-202510014/5AI can meaningfully assist consulting work by analyzing data, drafting reports, and organizing research—substantially raising the productivity of human consultants who retain client contact and final judgment.
Augmentation potentialclaude-sonnet-54/5AI significantly enhances a professor-consultant's productivity by accelerating literature review, data analysis, report drafting, and scenario modeling, while the human retains responsibility for judgment and client interaction.
Task automatabilityclaude-haiku-4-5-202510012/5Consulting advice requires contextual judgment, client relationship management, and often real-time problem-solving. While AI can draft reports or analyze technical problems, end-to-end consulting—especially relationship building and accountability—remains heavily dependent on human expertise and presence.
Task automatabilityclaude-sonnet-52/5Consulting requires synthesizing expert judgment, contextual client knowledge, negotiation, and accountability that current AI cannot autonomously replicate end-to-end; AI can support research and drafting but not deliver the full consulting engagement.'
Adoption barriersclaude-haiku-4-5-202510014/5Consulting clients typically require a named human expert to be responsible for advice, especially in government and regulated industry contexts. Liability, professional accountability, and contractual requirements typically mandate human sign-off.
Adoption barriersclaude-sonnet-54/5Consulting for government/industry often involves credentialing, formal contracts, liability for advice given, and client expectations of accountable human expertise, creating substantial organizational and legal barriers to full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5Consulting commands high human hourly rates (often $150–500+ per hour for academic experts). AI could reduce some preparation work, but the core service—expert advice and accountability—still requires human involvement, making cost savings marginal.
Cost vs. human wageclaude-sonnet-52/5While AI can cut research and drafting time, the overall consulting output still requires expert oversight, client relationship management, and liability assumption, keeping all-in costs closer to human-comparable rather than order-of-magnitude cheaper.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed systems reliably deliver full consulting engagements. AI can assist with technical analysis or documentation, but actual consulting service provision requires human judgment, negotiation, and client sign-off that current tools handle only partially.
Technical feasibility todayclaude-sonnet-52/5No deployed product independently performs professional consulting engagements for government or industry; existing AI tools are assistive research/analysis aids used by human consultants, not autonomous consultants.

Prepare and deliver lectures to undergraduate or graduate students on topics such as programming, data structures, and software design.

25

CI 20–30 · exposure 25 · augmentation 75 · 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 adopt AI slowly in teaching roles; most use cases remain supplementary (grading assistance, office-hours chatbots). Full lecture delivery by AI is rare and resisted due to institutional inertia, faculty concerns, and regulatory/accreditation barriers.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools slowly for core teaching functions, with more experimentation in administrative or content-support roles than actual lecture delivery.
Augmentation potentialclaude-haiku-4-5-202510014/5AI tools meaningfully assist instructors by generating lecture outlines, creating practice problems, explaining code examples, and automating grading—substantially raising productivity while the faculty member remains the primary educator and interactive guide.
Augmentation potentialclaude-sonnet-54/5AI substantially aids lecture prep, generating slides, examples, code demonstrations, and quiz questions, improving instructor efficiency and content quality significantly.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can generate lecture content and slides, delivering effective lectures requires real-time interaction, student engagement, live debugging, and adaptation to audience understanding—tasks that current AI systems cannot reliably perform end-to-end with 50% time savings at equal quality. AI can assist with preparation but cannot replace the teaching performance itself.
Task automatabilityclaude-sonnet-52/5AI can help draft lecture content and slides, but live delivery, adaptive pacing, in-class Q&A, and personal rapport with students require human presence and judgment that current systems cannot fully replace.
Adoption barriersclaude-haiku-4-5-202510015/5Universities require faculty credentials, accreditation standards, and institutional accountability for teaching quality and student outcomes. Educational regulations and accreditor expectations mandate human faculty responsibility; AI cannot legally or organizationally substitute for credentialed instructors.
Adoption barriersclaude-sonnet-53/5Accreditation, tenure structures, and institutional expectations for instructor-led teaching create moderate friction, though no strict licensing law mandates a human lecturer specifically.
Cost vs. human wageclaude-haiku-4-5-202510012/5The cost of AI systems capable of generating content plus the human oversight needed to ensure pedagogical quality and correctness, combined with student support, remains comparable to or more expensive than hiring an instructor, especially considering liability for educational misguidance.
Cost vs. human wageclaude-sonnet-52/5While AI content generation is cheap, the overall task still requires a paid instructor for delivery, oversight, and interaction, so cost savings are limited to prep work only.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed product reliably delivers full lectures to students at production scale; recorded lectures exist but lack interactivity and real-time responsiveness. Chatbots can answer questions but cannot structure, deliver, and adapt an entire lecture course with pedagogical effectiveness.
Technical feasibility todayclaude-sonnet-52/5Some universities pilot AI-generated course materials or recorded AI tutoring, but no deployed product autonomously delivers full lecture courses in production at scale.

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

25

CI 16–34 · exposure 17 · augmentation 63 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Postsecondary institutions have been slow to automate advising; while some universities pilot chatbots and advising tools, production adoption remains limited due to legal, reputational, and accreditation concerns, and faculty resistance to removing human judgment from career guidance.
Sector adoption velocityclaude-sonnet-52/5Higher education is a moderate-to-slow adopter of AI for advising functions, with pilots more common than full production deployment for career-related guidance.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist faculty advisors by surfacing relevant program information, career statistics, or prerequisite requirements, thereby freeing advising time for deeper conversations and personalized guidance—a genuinely supportive role even if not transformative.
Augmentation potentialclaude-sonnet-54/5AI can help faculty advisors by summarizing degree requirements, suggesting course pathways, and drafting career resources, meaningfully boosting productivity while the human remains central to the interaction.
Task automatabilityclaude-haiku-4-5-202510011/5Advising students on academic/vocational curricula and career issues requires deep contextual understanding of individual student circumstances, aspirations, institutional constraints, and nuanced judgment about fit—tasks that current AI cannot perform reliably end-to-end without significant human oversight and decision-making.
Task automatabilityclaude-sonnet-52/5Advising involves relational trust-building, understanding individual student context, and institutional knowledge that current AI cannot fully replicate end-to-end, though chatbots can handle basic FAQ-style advising.'
Adoption barriersclaude-haiku-4-5-202510014/5Academic advising involves significant human-contact expectations, institutional liability (advising errors can affect student progression and outcomes), regulatory accreditation requirements around advising quality, and strong organizational norms that faculty maintain direct advising relationships with students.
Adoption barriersclaude-sonnet-53/5No strict licensing requirement for academic advising, but institutional norms, accreditation expectations, and student preference for human mentorship create moderate friction against full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI systems can deliver some low-touch informational assistance, but the cost of integrating, maintaining, and oversighting such systems for personalized advising approaches or exceeds the marginal cost of faculty time spent on routine guidance, especially given liability concerns.
Cost vs. human wageclaude-sonnet-53/5AI tools for basic advising (chatbots, degree-audit systems) are cheap, but genuine personalized career counseling requires human faculty time that AI doesn't fully replace, keeping costs comparable when quality is held constant.
Technical feasibility todayclaude-haiku-4-5-202510012/5While AI can provide generic information about programs and career paths (e.g., via chatbots), deployed systems lack the ability to conduct meaningful personalized advising that accounts for a student's full profile, institutional policies, and career trajectory needs at production quality.
Technical feasibility todayclaude-sonnet-52/5Some universities deploy AI advising chatbots for scheduling and basic curriculum questions, but nuanced career and academic guidance is still handled by human advisors/faculty in production settings.'

Maintain computer equipment used in instruction.

21

CI 7–35 · exposure 13 · augmentation 38 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Educational institutions tend to be slower adopters of automation, and IT maintenance remains largely manual across most colleges and universities. Budget constraints and risk aversion limit rapid AI-driven maintenance adoption in academic settings.
Sector adoption velocityclaude-sonnet-52/5IT equipment maintenance in academic settings sees slow AI adoption, mostly limited to diagnostic software rather than full automation.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist by automating diagnostic reports, predicting maintenance needs through data analysis, and prioritizing work queues, helping technicians and faculty work more efficiently without replacing hands-on repair decisions.
Augmentation potentialclaude-sonnet-52/5AI diagnostic tools and knowledge bases can help identify issues or guide troubleshooting steps, but physical maintenance still requires human action.
Task automatabilityclaude-haiku-4-5-202510012/5Maintaining physical computer equipment requires hands-on diagnostics, hardware replacement, and troubleshooting in varied environments. While AI can assist with documentation and remote diagnostics, the task fundamentally depends on physical intervention that current AI systems cannot perform.
Task automatabilityclaude-sonnet-51/5Physical maintenance of computer hardware and lab equipment requires manual diagnosis, part replacement, and troubleshooting that AI cannot perform end-to-end today.'
Adoption barriersclaude-haiku-4-5-202510014/5Educational institutions often require certified IT personnel to handle equipment maintenance for liability and warranty reasons. Institutional procurement policies and vendor support agreements create significant friction against full substitution.
Adoption barriersclaude-sonnet-52/5No licensing requirement, but physical presence and hands-on troubleshooting create practical barriers to remote AI substitution.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI-assisted diagnostics can reduce some overhead, but technician labor dominates the cost structure. Full-scale robotic maintenance would be capital-intensive; current AI solutions are not yet cost-competitive with trained maintenance staff per task completed.
Cost vs. human wageclaude-sonnet-51/5AI cannot substitute for the physical labor involved, so costs are not comparable; human technicians remain necessary regardless of AI tools.
Technical feasibility todayclaude-haiku-4-5-202510012/5Remote monitoring and diagnostic software exist, but deployed systems handle only routine checks and alerting; actual maintenance decisions and physical repairs still require human technicians. No product reliably performs end-to-end equipment maintenance autonomously today.
Technical feasibility todayclaude-sonnet-51/5No deployed product autonomously maintains physical computer equipment; this remains a hands-on IT/technician function.

Initiate, facilitate, and moderate classroom discussions.

19

CI 14–25 · exposure 20 · augmentation 50 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Postsecondary computer science departments remain cautious about delegating classroom facilitation to AI despite digitization of broader education. Adoption is pilot-stage at best, with instructor skepticism and accreditation friction limiting deep deployment.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools for content prep and tutoring pilots, but classroom facilitation itself sees minimal displacement; adoption in live teaching interaction is slow.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can meaningfully assist instructors by generating discussion questions, flagging participation gaps, summarizing themes in real-time, or suggesting follow-up topics—useful augmentation that raises instructor productivity without replacing the human facilitator's core role.
Augmentation potentialclaude-sonnet-53/5AI can help instructors prepare discussion questions, summarize readings, and suggest talking points, meaningfully aiding prep but not the live facilitation itself.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can generate discussion prompts and summarize points, it cannot authentically facilitate live classroom discourse that requires real-time judgment, responsiveness to student emotional states, and pedagogical adaptation. Current systems lack the embodied presence and genuine dialogue management needed for end-to-end facilitation meeting the 50% time-saving threshold.
Task automatabilityclaude-sonnet-52/5Leading a live, responsive classroom discussion requires reading the room, adapting to student personalities, and real-time social judgment that current AI cannot replicate end-to-end, though AI could generate discussion prompts beforehand.rating stays low.
Adoption barriersclaude-haiku-4-5-202510014/5Strong barriers exist: faculty autonomy and professional standards expect a credentialed educator to own classroom discourse; institutional liability and accreditation frameworks assume human instructors direct learning interactions; and student expectations center on human mentorship and responsiveness in synchronous discussions.
Adoption barriersclaude-sonnet-54/5Accreditation, instructor-of-record requirements, and the expectation of live human engagement in postsecondary education create strong institutional and credentialing barriers to full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5Deploying AI discussion facilitation would require significant custom integration, oversight, and likely human moderation backup, making the all-in cost comparable to or higher than a postsecondary instructor's hourly wage for this pedagogically critical task.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute performing this task today, so no meaningful cost comparison favors AI; a human instructor is required for the live interaction.
Technical feasibility todayclaude-haiku-4-5-202510012/5Some deployed products assist with discussion prompts or asynchronous moderation, but no mainstream AI system reliably facilitates live, synchronous classroom discussions with the nuance, rapport-building, and conflict resolution that teaching requires. Products remain narrow and experimental in production classroom settings.
Technical feasibility todayclaude-sonnet-51/5No deployed product autonomously runs live in-person classroom discussions for postsecondary courses; existing AI is limited to text-based chatbot Q&A, not human facilitation dynamics.

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

16

CI 7–25 · exposure 13 · augmentation 50 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Higher education adoption of AI for core supervisory functions remains limited; most institutions use traditional faculty-led models with only supplementary digital tools for administrative support, reflecting cultural and structural resistance to automating mentorship roles.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools for content generation and grading assistance, but formal supervisory roles for students remain largely untouched by AI adoption trends.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can meaningfully assist by automating administrative documentation, analyzing student submissions for patterns, and tracking progress metrics, but the core supervision role—providing mentorship, evaluating quality, and making developmental decisions—remains substantially human-centered and benefits moderately from augmentation tools.
Augmentation potentialclaude-sonnet-53/5AI can help supervisors by drafting feedback, summarizing student progress, suggesting research directions, or flagging plagiarism, meaningfully aiding but not replacing the supervisory relationship.
Task automatabilityclaude-haiku-4-5-202510012/5Supervision of teaching and research involves complex judgment about student progress, mentorship quality, and research direction that requires human expertise and contextual understanding. AI can assist with administrative tracking and documentation, but cannot replicate the nuanced evaluation and personalized guidance central to effective academic supervision.
Task automatabilityclaude-sonnet-51/5Supervising students' teaching, internships, and research requires ongoing relational mentorship, judgment calls on individual progress, and institutional accountability that current AI cannot perform end-to-end.'
Adoption barriersclaude-haiku-4-5-202510014/5Academic institutions have strong regulatory, accreditation, and professional norms requiring qualified faculty to directly supervise student work and research. Institutional policies, tenure structures, and educational standards legally mandate human faculty oversight of undergraduate and graduate supervision.
Adoption barriersclaude-sonnet-54/5Academic accreditation, mentorship responsibilities, and institutional policies typically require a qualified faculty member to formally supervise students, creating strong structural and credentialing barriers to automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI tools that could assist supervision (learning management systems, document analysis) have comparable or higher costs relative to the faculty time they might save, especially when accounting for integration and the need for human oversight of critical decisions.
Cost vs. human wageclaude-sonnet-51/5Since AI cannot substitute for the supervisory role, there is no viable cost comparison—the human must remain the responsible supervisor, making AI substitution costs irrelevant or additive rather than replacing labor.
Technical feasibility todayclaude-haiku-4-5-202510012/5While AI can help with scheduling, documentation, and progress monitoring, no deployed system can reliably perform comprehensive academic supervision including assessment of student learning, research quality evaluation, and mentorship decisions. Current tools support these functions but do not execute supervision independently.
Technical feasibility todayclaude-sonnet-51/5No deployed product independently supervises student research or teaching work; at best AI tools assist with feedback on drafts or code review, not supervisory oversight itself.

Collaborate with colleagues to address teaching and research issues.

16

CI 7–25 · exposure 13 · augmentation 50 · importance 3.7/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Even in digitized academic environments, adoption of AI for core collegial collaboration remains limited; institutions are still in early pilots of AI-assisted administrative tasks, not production displacement of faculty deliberation.
Sector adoption velocityclaude-sonnet-52/5Higher education is a slower-adopting sector for AI in core faculty governance and collaborative functions, though administrative AI tools are creeping in for scheduling and document management.
Augmentation potentialclaude-haiku-4-5-202510013/5AI tools can usefully assist by synthesizing prior discussions, organizing research papers or syllabi comparisons, or generating talking points—providing moderate productivity gains while faculty remain the decision-makers.
Augmentation potentialclaude-sonnet-53/5AI can help by summarizing research literature, drafting agendas, taking meeting notes, or synthesizing curriculum data to inform discussions, providing moderate productivity support.
Task automatabilityclaude-haiku-4-5-202510012/5Collaboration on teaching and research issues requires nuanced communication, conflict resolution, and consensus-building among peers—areas where current AI can assist with drafting and organization but cannot meaningfully replace the interpersonal judgment and negotiation that define effective academic collaboration.
Task automatabilityclaude-sonnet-51/5This is an inherently interpersonal, relationship-based collaborative activity requiring shared institutional context, trust-building, and negotiation among colleagues that AI cannot perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5Academic culture strongly privileges peer collaboration and shared decision-making as core to research and teaching governance; institutional inertia, governance traditions, and the expectation that faculty drive their own priorities create high organizational and cultural barriers to automation.
Adoption barriersclaude-sonnet-54/5Faculty governance, tenure processes, and academic collegiality strongly favor human-only collaboration; institutional norms and accreditation expectations reinforce this.
Cost vs. human wageclaude-haiku-4-5-202510012/5The AI cost of supporting collaboration through drafting and synthesis tools is modest, but the marginal benefit over unassisted collaboration is small relative to the cognitive overhead of oversight, making the cost-benefit ratio unfavorable for replacement.
Cost vs. human wageclaude-sonnet-51/5There is no AI product that replaces this task, so cost comparison favors the human doing it entirely, with AI only offering marginal support tools that don't reduce overall cost significantly.
Technical feasibility todayclaude-haiku-4-5-202510012/5While AI can help facilitate discussions (e.g., summarizing concerns, generating agenda items), no deployed product reliably performs end-to-end collaborative problem-solving in academic settings; such work remains fundamentally dependent on human deliberation and relationship dynamics.
Technical feasibility todayclaude-sonnet-51/5No deployed product substitutes for human faculty collaboration on teaching/research strategy; AI tools at best support scheduling or document sharing, not the collaboration itself.

Supervise students' laboratory work.

10

CI 0–20 · exposure 8 · augmentation 38 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Despite digitization in higher education, laboratory supervision remains a core human function protected by institutional policy and accreditation standards; there is minimal adoption pressure or evidence of displacement.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools for content and grading assistance, but in-person lab supervision remains a slow-adopting, physically-grounded activity.
Augmentation potentialclaude-haiku-4-5-202510012/5AI tools could assist with grading code artifacts or flagging common mistakes, but the primary task—real-time supervision of student work for safety and learning—offers limited scope for meaningful AI augmentation.
Augmentation potentialclaude-sonnet-53/5AI can assist by providing coding hints, debugging suggestions, or answering conceptual questions to students during lab time, helping the instructor manage a larger or more independent group.
Task automatabilityclaude-haiku-4-5-202510011/5Supervising laboratory work requires real-time presence, judgment of student competence, safety oversight, and intervention in complex problem-solving—activities that demand human judgment and physical proximity that current AI cannot provide end-to-end.
Task automatabilityclaude-sonnet-52/5Supervising lab work requires real-time observation, hands-on troubleshooting, and safety oversight of students physically working, which current AI cannot perform end-to-end.'
Adoption barriersclaude-haiku-4-5-202510015/5Institutional liability, accreditation requirements, and duty-of-care obligations typically mandate human supervision of laboratory work in educational settings, creating hard legal and regulatory barriers to automation.
Adoption barriersclaude-sonnet-54/5Universities require a qualified instructor of record present for liability, safety, and accreditation purposes during lab sessions, creating strong institutional and legal barriers to full automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of any AI system capable of remote monitoring, combined with required human oversight and liability coverage, would far exceed the loaded cost of a teaching assistant or instructor actually present in the lab.
Cost vs. human wageclaude-sonnet-52/5Since AI cannot replace the supervisory function itself, any deployed AI tool adds cost on top of the human instructor rather than substituting for the labor, making it not cheaper for the actual supervisory task.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably supervises hands-on laboratory work; this task fundamentally requires a human present to monitor safety, assess understanding, and intervene dynamically with students.
Technical feasibility todayclaude-sonnet-51/5No deployed product provides autonomous physical supervision of student lab sessions; at most AI can answer coding questions or grade submitted lab reports after the fact.

Maintain regularly scheduled office hours to advise and assist students.

6

CI 0–11 · exposure 0 · augmentation 38 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Educational institutions have shown minimal adoption of AI for core advising functions. Faculty office hours remain a hallmark of postsecondary education, and institutions actively resist replacing human advising with automated systems.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools slowly for core teaching/advising duties; while some AI tutoring supplements exist, institutional office-hour requirements remain largely unchanged.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could modestly assist faculty by pre-processing student questions, drafting responses to FAQs, or organizing student records, but it does not meaningfully transform faculty productivity during the interactive advising session itself, which remains fundamentally human-centered.
Augmentation potentialclaude-sonnet-53/5AI can help professors prepare answers, triage common questions, or provide supplementary help outside office hours, but doesn't transform the office-hour interaction itself.
Task automatabilityclaude-haiku-4-5-202510011/5Office hours require real-time, empathetic interaction with individual students to address their unique academic and personal concerns. This task fundamentally depends on human judgment, emotional intelligence, and adaptive responsiveness that current AI cannot replicate at the quality level required for student advising.
Task automatabilityclaude-sonnet-51/5Office hours require synchronous human presence, relationship-building, and personalized mentorship that AI cannot substitute for as an institutional obligation, even though AI can supplement content help.4
Adoption barriersclaude-haiku-4-5-202510015/5Strong barriers exist: institutional culture and accreditation standards expect human faculty-student interaction; there are implicit professional obligations for advising and mentoring; and student outcomes depend on human judgment about individual circumstances that AI cannot legally or ethically be asked to handle independently.
Adoption barriersclaude-sonnet-54/5Universities require faculty to hold office hours as part of contractual/tenure duties and accreditation expectations, and students expect direct human access to their instructor for advising.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of deploying and maintaining an AI system to handle office hour interactions, combined with the need for human oversight and error correction, would far exceed the marginal cost of the faculty member's existing scheduled time.
Cost vs. human wageclaude-sonnet-52/5While AI chat tools are cheap per query, they cannot replace the human obligation itself, so cost comparison for the actual task (a professor's presence) doesn't favor AI meaningfully.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs the full scope of scheduled office hours—providing personalized academic advising, career guidance, and mentoring to students with diverse needs. While chatbots exist, they cannot substitute for the nuanced, contextual advising that faculty provide.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs 'holding office hours' as a substitute for faculty; chatbots exist for Q&A but do not fulfill the advising/mentorship role or institutional requirement.

Direct research of other teachers or of graduate students working for advanced academic degrees.

4

CI 0–7 · exposure 0 · augmentation 50 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Universities remain highly traditional institutions with strong norms around faculty research mentorship; adoption of AI for supervisory roles is minimal, and institutional structures actively resist replacing human researchers in leadership roles.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools for research assistance but the supervisory/mentorship role itself sees minimal automation adoption.
Augmentation potentialclaude-haiku-4-5-202510013/5AI tools can assist faculty by drafting feedback, organizing literature, flagging methodological issues, or summarizing student progress, raising productivity in parts of the supervisory workflow while the faculty member retains full direction authority.
Augmentation potentialclaude-sonnet-53/5AI can help by summarizing literature, checking code, drafting feedback, or flagging data issues, meaningfully supporting but not replacing the advisor's directive role.
Task automatabilityclaude-haiku-4-5-202510011/5Directing research requires nuanced judgment about research direction, mentorship of individuals, course corrections based on emerging results, and interpersonal feedback—tasks that demand human contextual understanding and cannot be automated end-to-end with current AI systems.
Task automatabilityclaude-sonnet-51/5Directing others' research requires mentorship, judgment on novel research directions, and relational trust-building that current AI cannot perform end-to-end.'
Adoption barriersclaude-haiku-4-5-202510015/5Academic research direction is legally and institutionally tied to faculty authority; universities require an accredited faculty member to formally supervise graduate research and sign off on dissertations, creating hard legal and regulatory barriers.
Adoption barriersclaude-sonnet-54/5Academic advising and degree certification require an accountable, credentialed faculty member; institutional and accreditation rules effectively mandate human oversight of graduate research.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of an AI system to replace a tenured faculty researcher's supervisory role would far exceed the human cost, as it would require substantial domain expertise, integration, and human oversight to avoid research misdirection.
Cost vs. human wageclaude-sonnet-51/5There is no AI substitute performing this managerial/mentorship function, so cost comparison favors the human by default.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs the holistic role of directing academic research supervision, which involves real-time decision-making, adaptive guidance, and accountability relationships that require human expertise and authority.
Technical feasibility todayclaude-sonnet-51/5No deployed product supervises graduate students or peer researchers autonomously; AI tools assist with literature review or coding but do not direct research programs.

Perform administrative duties, such as serving as department head.

3

CI 0–6 · exposure 0 · augmentation 50 · importance 3.8/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Academic institutions, bound by tradition and regulatory requirements, are laggards in automating leadership and administrative roles. No measurable displacement of department heads by AI exists, and organizational culture strongly resists removing humans from these positions.
Sector adoption velocityclaude-sonnet-52/5Higher education administration adopts AI slowly for governance and leadership roles, though back-office AI tools (scheduling, reporting) are increasingly used to support administrators.
Augmentation potentialclaude-haiku-4-5-202510012/5AI can provide modest assistance with specific administrative tasks—agenda generation, meeting summarization, policy document drafting—but the core work of leadership, personnel decisions, and institutional strategy remains inherently human. Augmentation is limited to peripheral support.
Augmentation potentialclaude-sonnet-54/5AI can meaningfully assist department heads with drafting reports, scheduling, data analysis, and communications, improving efficiency while the human retains decision authority.
Task automatabilityclaude-haiku-4-5-202510011/5Administrative duties as a department head involve complex human judgment, interpersonal negotiation, strategic planning, and institutional decision-making that current AI cannot perform end-to-end. While AI can assist with scheduling and document drafting, the core responsibilities—hiring, evaluating faculty, resolving conflicts, and setting departmental direction—require human authority and accountability.
Task automatabilityclaude-sonnet-51/5Department headship involves personnel decisions, budget allocation, strategic planning, and interpersonal negotiation that current AI cannot execute end-to-end.It requires accountable human judgment and authority that AI systems cannot hold.
Adoption barriersclaude-haiku-4-5-202510015/5Hard legal and institutional barriers protect this role: universities require a credentialed human faculty member to serve as department head with fiduciary and hiring authority; accreditation, employment law, and institutional governance mandate human decision-making and accountability in these duties.
Adoption barriersclaude-sonnet-55/5Department head roles require formal institutional appointment, accountability, and often tenure/faculty governance structures that legally and organizationally require a human to hold the position.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of an AI system capable of approximating department head functions (requiring custom integration, legal review, and continuous oversight) would far exceed the cost-benefit analysis, given the task's embedded human accountability and leadership requirements that justify the human salary.
Cost vs. human wageclaude-sonnet-52/5While AI can cheaply handle scheduling or report drafting, the core leadership and decision-making functions still require a salaried human, so overall cost savings are limited.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI system can reliably perform the full scope of department head duties in a production environment. AI products exist for narrow administrative tasks like email management or scheduling, but no system can legally or operationally replace a human department head in an academic institution.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs departmental administrative leadership; existing AI tools only assist with sub-tasks like scheduling or document drafting, not the role itself.

Participate in campus and community events.

3

CI 0–5 · exposure 0 · augmentation 13 · importance 3.1/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5No adoption of AI for this task is occurring because it is intrinsically human-centric and socially grounded. Institutions cannot replace faculty presence at events with AI systems.
Sector adoption velocityclaude-sonnet-51/5Higher education is a slow-adopting sector for physical/in-person engagement tasks, and this task category sees essentially no AI displacement.
Augmentation potentialclaude-haiku-4-5-202510011/5AI offers minimal assistance in participating in events themselves; while it might help prepare materials or gather information beforehand, it does not augment the core activity of human engagement and presence at the event.
Augmentation potentialclaude-sonnet-52/5AI can help with scheduling, event promotion materials, or preparing talking points, but offers little assistance for the actual act of participating.
Task automatabilityclaude-haiku-4-5-202510011/5Participating in campus and community events requires genuine human presence, interpersonal interaction, and contextual judgment that cannot be automated. AI cannot physically attend events or meaningfully engage with stakeholders in ways that fulfill the social and institutional purposes of such participation.
Task automatabilityclaude-sonnet-51/5Physical/social presence at events requires embodiment and genuine human relationship-building that current AI cannot perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Hard barriers exist: faculty participation in campus and community events is typically a contractual or institutional requirement embedded in job expectations, and only a human faculty member can fulfill the role and represent the institution in these contexts.
Adoption barriersclaude-sonnet-54/5Faculty presence at events is an institutional and social expectation tied to identity, mentorship, and community standing, creating strong non-regulatory but firm organizational barriers.
Cost vs. human wageclaude-haiku-4-5-202510011/5There is no meaningful cost comparison here since AI cannot perform this task end-to-end. The task inherently requires human attendance and participation, making any attempt to cost-optimize the task fundamentally misaligned with its purpose.
Cost vs. human wageclaude-sonnet-51/5AI cannot perform this task at all, so it offers no cost savings relative to a human attending in person.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI system can autonomously participate in campus or community events in the way required for an academic role. While AI can assist with event planning or promotion, it cannot substitute for a faculty member's live presence and engagement.
Technical feasibility todayclaude-sonnet-51/5No deployed product substitutes for a person attending and participating in campus or community events.

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

0

CI 0–0 · exposure 0 · augmentation 50 · importance 3.4/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Academic governance structures are slow to change and require human judgment; there is no meaningful adoption of AI in committee roles even in early-adopter institutions.
Sector adoption velocityclaude-sonnet-51/5Higher education governance structures are slow-moving and there is no observed trend of AI systems replacing committee members.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist committee members by preparing policy briefs, summarizing prior discussions, or drafting agendas, but the human faculty member remains the accountable decision-maker and participant.
Augmentation potentialclaude-sonnet-53/5AI can help summarize policy documents, draft meeting minutes, or prepare briefing materials, aiding preparation even though the deliberative role itself remains human.
Task automatabilityclaude-haiku-4-5-202510011/5Committee service requires complex institutional judgment, interpersonal negotiation, and accountability for policy decisions. These demand human discretion, consensus-building, and responsibility that AI cannot assume today.
Task automatabilityclaude-sonnet-51/5Committee service requires interpersonal deliberation, institutional judgment, and representing stakeholder interests, none of which AI can perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Institutional bylaws, accreditation standards, and legal requirements mandate that tenured or appointed faculty members serve on committees; these roles require licensed professionals and formal organizational authority.
Adoption barriersclaude-sonnet-55/5Committee membership is tied to faculty governance rights, tenure status, and institutional bylaws requiring human faculty representation, a hard structural barrier.
Cost vs. human wageclaude-haiku-4-5-202510011/5The task involves salaried faculty time allocated to institutional governance; AI systems cannot serve as committee members or reduce this burden without removing the human entirely from required roles.
Cost vs. human wageclaude-sonnet-51/5There is no AI product performing this role, so cost comparison favors the human by default since AI cannot substitute at all.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably participates in or substitutes for committee membership, which requires legal authority, institutional accountability, and voting power that only humans can hold.
Technical feasibility todayclaude-sonnet-51/5No deployed product substitutes for a human member on academic or administrative committees; this remains entirely a human institutional role.

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.