Sociology Teachers, Postsecondary

25-1067.00

Median wage $84,290/yr11,850 employed (US)Rank #402 of 923 scored · top 44% by substitution

Teach courses in sociology. Includes both teachers primarily engaged in teaching and those who do a combination of teaching and research.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution30

Exposure26

Augmentation61

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

24 rated tasks, binned by substitution score.

Position among all scored occupations

Distribution of 923 occupation scores; the marker is this occupation.

Tasks with substitution ≥ 70

13%

Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.

Why this score

The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.

Task automatabilityw 35%26

panel mean rating 2.1/5 → substitution pressure 26/100

Technical feasibility todayw 20%27

panel mean rating 2.1/5 → substitution pressure 27/100

Cost vs. human wagew 15%33

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

Adoption barriersw 20%inverted — strong barriers lower the score37

panel mean rating 3.5/5 (barrier strength) → substitution pressure 37/100

Sector adoption velocityw 10%27

panel mean rating 2.1/5 → substitution pressure 27/100

Task breakdown (24 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.

CI 95–100 · exposure 100 · augmentation 75 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	5/5	Adoption of LMS and SIS is near-universal in postsecondary education; almost all instructors already enter grades and attendance records digitally via institutional platforms, making this an already largely automated or institutionally systematized task across the sector.
Sector adoption velocity	claude-sonnet-5	5/5	Higher education has near-universal adoption of digital gradebooks and attendance systems, representing deep, mature deployment rather than pilots.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can substantially assist by auto-flagging attendance patterns, suggesting grade calculations, flagging data entry errors, and generating summary reports—reducing instructor cognitive load and error rate while the instructor retains final authority and oversight.
Augmentation potential	claude-sonnet-5	4/5	Even where instructors input some data manually, automated calculation, syncing, and reporting substantially reduce time spent on this administrative task.
Task automatability	claude-haiku-4-5-20251001	5/5	Record maintenance—tracking attendance, grades, and administrative data—is almost entirely automatable using Learning Management Systems (LMS) and student information systems that already exist. Current AI can transcribe attendance rosters, process grade submissions, and file records with >50% time savings and equal quality.
Task automatability	claude-sonnet-5	5/5	Recording attendance, calculating grades, and maintaining records is a structured, rules-based clerical task that off-the-shelf LMS and gradebook software already automates fully.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Most institutions legally mandate use of their official student information system for compliance and auditing, but this is not a barrier to automation—it is the standard infrastructure. Few hard legal barriers prevent the substitution or augmentation of human record-keeping with IT systems already in place.
Adoption barriers	claude-sonnet-5	1/5	No licensing or legal requirement mandates a human personally maintain these records; software-based record management is already standard practice.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	Institutional systems amortize record-keeping costs across thousands of users; the per-task cost for AI-assisted or fully automated record management is far lower than manual instructor entry and archival, often $0.01–$0.10 per record versus >$1 in human time.
Cost vs. human wage	claude-sonnet-5	5/5	Automated LMS-based record-keeping costs a small fraction of the instructor time it would take to manually log grades and attendance, an order of magnitude cheaper.
Technical feasibility today	claude-haiku-4-5-20251001	5/5	Mature, production-grade LMS and student information systems (Canvas, Blackboard, Banner, etc.) are deployed at scale in virtually all higher education institutions and reliably maintain these records in institutional workflows.
Technical feasibility today	claude-sonnet-5	5/5	Learning management systems (Canvas, Blackboard, PowerSchool, etc.) reliably handle attendance tracking and grade record-keeping in production at scale across universities today.

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

CI 76–81 · exposure 75 · augmentation 100 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Adoption is emerging in higher education with pilots and early adopters, but remains uneven. Many instructors have experimented with AI drafting, yet institutional policies and faculty resistance to full automation remain common, slowing production-scale deployment.
Sector adoption velocity	claude-sonnet-5	3/5	Higher education is adopting generative AI for course prep unevenly—many individual faculty use it informally, but institutional-level deployment and policy remain in pilot or ad hoc stages.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI provides substantial augmentation: instructors can instantly generate multiple assignment drafts, syllabus variants, and formatted handouts, then refine them—transforming the speed and flexibility of course material development while the instructor retains full pedagogical control.
Augmentation potential	claude-sonnet-5	5/5	AI substantially speeds up drafting of syllabi, assignments, and handouts, letting instructors generate first drafts quickly and focus their time on tailoring content to course-specific learning objectives.
Task automatability	claude-haiku-4-5-20251001	4/5	AI can generate syllabi, homework assignments, and handouts with minimal human input using prompts and templates. Large language models can draft complete course materials at high quality, saving 60–80% of preparation time while requiring only subject-matter review and customization by the instructor.
Task automatability	claude-sonnet-5	4/5	Drafting syllabi, homework assignments, and handouts is largely text generation from structured inputs, which current LLMs handle well with minor human editing, saving significant time versus writing from scratch.But full customization to institutional policies and specific pedagogical intent still requires review.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few hard barriers exist; course material authorship is typically within instructor discretion, though institutions may have style/compliance requirements for syllabi. Academic norms and potential concerns about AI-generated pedagogy create some friction, but no legal or licensing barrier prevents adoption.
Adoption barriers	claude-sonnet-5	1/5	No licensing or legal requirement mandates a human sociology instructor personally draft these materials; institutions freely allow use of templates, TAs, or AI tools for this administrative task.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	The cost of AI inference for generating course materials is negligible (pennies per document), compared to instructor labor at $40–100+ per hour, achieving orders-of-magnitude savings when factoring in reduced preparation time.
Cost vs. human wage	claude-sonnet-5	5/5	Generating a draft syllabus or assignment set via AI costs cents in compute versus the substantial hourly cost of faculty time, making AI drastically cheaper per draft produced.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed products (ChatGPT, Claude, specialized educational AI) reliably generate course materials in production. Academic institutions and individual instructors routinely use these tools to draft syllabi and assignments; error rates on content coherence are low, though human review remains standard practice.
Technical feasibility today	claude-sonnet-5	4/5	Deployed tools like ChatGPT, Claude, and education-specific platforms (e.g., Course Hero AI, Canva for handouts) are widely used by instructors today to draft these materials reliably, though outputs need instructor customization and fact-checking.

Compile bibliographies of specialized materials for outside reading assignments.

CI 65–84 · exposure 70 · augmentation 100 · importance 3.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Academic and professional services sectors show rapid adoption of AI-assisted research and bibliography tools; many universities and faculty have already integrated ChatGPT or similar systems into course preparation workflows.
Sector adoption velocity	claude-sonnet-5	2/5	Higher education adoption of AI for course prep tasks is growing but still uneven, with many instructors continuing manual curation due to habit, citation accuracy concerns, or institutional caution.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI dramatically assists faculty by generating draft bibliographies that can be reviewed, refined, and customized for specific reading assignments, significantly reducing time spent on database searches while preserving faculty judgment over content selection and relevance.
Augmentation potential	claude-sonnet-5	5/5	AI substantially speeds up and broadens bibliography compilation, letting instructors quickly draft and refine reading lists while retaining final selection judgment.
Task automatability	claude-haiku-4-5-20251001	4/5	AI can generate comprehensive bibliographies with minimal human intervention by searching academic databases, cross-referencing sources, and formatting citations automatically. While verification of relevance and accuracy may require brief oversight, the time-saving threshold is easily met for the core compilation work.
Task automatability	claude-sonnet-5	4/5	AI can quickly generate topical bibliographies and reading lists using literature knowledge and web/database search tools, requiring only light instructor verification for accuracy and relevance.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few regulatory or licensing barriers exist for bibliography compilation; most academic institutions have adopted or could adopt AI tools for this task with minimal friction. No legal requirement mandates human sign-off, though academic norms around source quality may create modest institutional friction.
Adoption barriers	claude-sonnet-5	1/5	No licensing, regulatory, or liability barriers prevent using AI to compile reading lists; it's a low-stakes administrative/academic task.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	The per-task cost of AI-generated bibliographies (seconds of inference time, minimal API cost) is orders of magnitude cheaper than paying a faculty member or librarian to manually search databases and compile citations.
Cost vs. human wage	claude-sonnet-5	4/5	Generating a bibliography via AI takes minutes at near-zero marginal cost versus substantial faculty time for manual literature search and curation.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Multiple deployed systems (ChatGPT, Claude, specialized academic AI tools, and library databases with AI-assisted features) reliably generate bibliographies at scale. Output quality is generally high, though occasional citation errors or missing sources require spot-checking, placing it slightly below full reliability.
Technical feasibility today	claude-sonnet-5	3/5	Tools like reference managers with AI search, ChatGPT, and academic search assistants can produce bibliographies today, but citation accuracy and completeness still require human verification, limiting fully reliable deployment.

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

CI 51–66 · exposure 55 · augmentation 88 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Higher education is a digitized, information-intensive sector with rapid LMS and assessment tool adoption. Many institutions already use automated grading and exam administration platforms. COVID-era remote learning accelerated this trend; adoption is demonstrably fast in this sector compared to others.
Sector adoption velocity	claude-sonnet-5	2/5	Higher education adopts AI tools unevenly; many institutions pilot AI grading assistance but full adoption for grading remains limited due to academic integrity concerns and faculty resistance.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI substantially augments faculty productivity by auto-generating question banks, flagging outlier grades for review, and handling bulk objective grading—allowing instructors to focus on curating high-quality assessments and providing meaningful feedback on subjective work. This is already common practice in postsecondary institutions.
Augmentation potential	claude-sonnet-5	4/5	AI substantially helps with exam question generation, rubric creation, and first-pass grading of short-answer/multiple-choice items, meaningfully speeding up the overall task even when a human finalizes essay grades.
Task automatability	claude-haiku-4-5-20251001	3/5	AI can automate substantial parts of this task: generating exam questions from course materials, administering computer-based assessments, and grading objective items (multiple choice, fill-in-the-blank). However, grading subjective sociology essays with nuance and consistency requires human judgment, and designing pedagogically valid exams requires faculty oversight, preventing full end-to-end automation.
Task automatability	claude-sonnet-5	3/5	AI can generate exam questions and grade objective/short-answer responses reasonably well, but grading essays (common in sociology) at equivalent quality with nuanced conceptual judgment still requires human oversight, limiting full end-to-end automation.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Faculty retain institutional expectations and often contractual authority over assessment design and final grade determination. Some accreditation bodies and institutions prefer or require human review of subjective assessment. However, no hard legal barrier prevents AI use for exam administration and grading assistance, and many institutions already deploy such systems.
Adoption barriers	claude-sonnet-5	2/5	No strict licensing requirement mandates a human grade exams, though academic integrity policies and institutional norms create some friction against fully automated grading.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI-powered exam administration and grading costs are substantially lower than hiring teaching assistants or faculty time. A single system handles hundreds of students; the marginal cost per student assessment is minimal, especially for objective items. This easily undercuts human labor for routine grading and administration.
Cost vs. human wage	claude-sonnet-5	4/5	AI-assisted exam compilation and grading tools are inexpensive per use compared to faculty or TA time, though some human review remains necessary, slightly limiting the cost advantage.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Multiple deployed products reliably handle exam administration (LMS platforms like Canvas, Blackboard) and objective grading (auto-graders in educational software). AI tools can generate questions and grade essays with reasonable consistency in production. Minor limitations exist for complex rubric-based grading and exam validity design, but core feasibility is demonstrated at scale.
Technical feasibility today	claude-sonnet-5	3/5	Products like AI-assisted quiz generators and automated essay scoring tools (e.g., Gradescope, Turnitin) exist and are used in production, but essay grading in social science disciplines still has notable error rates and requires instructor review.

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

CI 25–54 · exposure 38 · augmentation 63 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Higher education lags in broad AI adoption for instruction; grading automation pilots exist but widespread production deployment in sociology departments remains rare, with most institutions maintaining traditional human grading as standard practice.
Sector adoption velocity	claude-sonnet-5	3/5	Higher education is adopting AI grading tools unevenly; some large courses and ed-tech-forward institutions use them, but many postsecondary faculty still grade manually, especially for essay-based social science courses.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI tools can assist by flagging plagiarism, suggesting grammatical corrections, and organizing feedback, improving faculty efficiency; however, the core evaluative task—assessing sociological reasoning and critical engagement—remains faculty-driven and only partially augmented.
Augmentation potential	claude-sonnet-5	4/5	AI can efficiently draft feedback comments, flag plagiarism, and summarize common errors across a class, meaningfully speeding up an instructor's grading workflow while they retain final judgment.
Task automatability	claude-haiku-4-5-20251001	2/5	Grading routine assignments with clear rubrics could be partially automated (e.g., multiple-choice, basic essay structure checks), but evaluating sociological arguments, critical thinking, and nuanced written analysis requires human judgment that current AI cannot reliably replicate at equal quality without substantial human review.
Task automatability	claude-sonnet-5	3/5	AI can draft feedback and score structured assignments like quizzes, but grading nuanced sociology papers requiring judgment of argumentation, originality, and disciplinary nuance still needs substantial human review, limiting full automation.'
Adoption barriers	claude-haiku-4-5-20251001	4/5	Institutional accreditation standards, faculty contractual obligations, and educational liability expectations (students have appeal rights and expect expert feedback) create legal and organizational friction; instructors remain accountable for grade decisions regardless of AI assistance.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement bars AI grading, but academic integrity policies, FERPA concerns, and institutional norms requiring instructor accountability for grades create moderate friction.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Integration of grading assistants into institutional learning management systems and the overhead of human verification of AI-generated grades approach or exceed the cost of direct faculty grading, especially for small cohorts typical in postsecondary teaching.
Cost vs. human wage	claude-sonnet-5	4/5	AI grading assistance costs a small fraction of an instructor's or TA's hourly wage, especially at scale across large lecture courses, though oversight adds some cost.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	AI can perform basic plagiarism detection and generate comments on grammar/structure, but production systems for comprehensive evaluation of sociology papers with meaningful pedagogical feedback remain immature and require heavy human oversight to avoid quality degradation.
Technical feasibility today	claude-sonnet-5	3/5	AI writing assistants and grading tools (e.g., Gradescope, GPT-based rubric graders) are used in production for essay feedback, but error rates and inconsistency mean instructors still verify grades before finalizing.

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

CI 25–47 · exposure 33 · augmentation 63 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Higher education procurement remains fragmented and relationship-driven; adoption of AI-assisted selection is nascent, with most institutions still relying on manual processes and established vendor channels rather than AI agents.
Sector adoption velocity	claude-sonnet-5	2/5	Higher education administrative/procurement functions adopt AI slowly compared to fields like finance or tech, with most current use limited to research and content assistance rather than logistics.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can meaningfully assist by comparing textbook options, surfacing equipment alternatives based on price and specifications, and flagging availability—useful productivity gains while faculty retain final selection authority.
Augmentation potential	claude-sonnet-5	4/5	AI can efficiently help instructors search, compare, and shortlist textbooks and equipment options, meaningfully speeding up part of the task even though final procurement stays human-driven.
Task automatability	claude-haiku-4-5-20251001	2/5	Selecting appropriate textbooks and lab equipment requires domain expertise, budget awareness, and understanding of pedagogical goals—tasks that resist full automation. While AI could assist with sourcing and comparison, final selection remains heavily dependent on human judgment about course fit and institutional constraints.
Task automatability	claude-sonnet-5	3/5	AI can suggest textbooks, compare course materials, and even draft purchase orders, but final selection requires pedagogical judgment and institutional procurement processes that remain manual.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Faculty retain pedagogical authority and responsibility for curriculum materials; institutional approval processes, vendor relationships, and budget governance create organizational friction that legally and operationally protects human decision-making in this domain.
Adoption barriers	claude-sonnet-5	2/5	No licensing barrier, but institutional purchasing rules, budget approval chains, and faculty preference create moderate organizational friction.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	The human labor cost for selection (a faculty member's time) is relatively low and already incorporated; AI systems for procurement integration and comparison would add cost without displacing most of the actual decision-making burden.
Cost vs. human wage	claude-sonnet-5	3/5	AI recommendation tools are cheap, but the overall task still involves human ordering, budget approval, and vendor interaction, keeping costs roughly comparable to doing it manually.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed AI system reliably performs end-to-end selection and procurement of academic materials independently; existing procurement tools are narrowly scoped or require substantial human oversight of vendor quality and material appropriateness.
Technical feasibility today	claude-sonnet-5	2/5	Some AI-assisted curriculum planning and procurement tools exist, but no deployed product reliably automates the full material selection-and-acquisition workflow for postsecondary instructors today.

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

CI 25–44 · exposure 30 · augmentation 75 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Academia and postsecondary institutions are slower adopters of AI automation; while literature monitoring tools are used, conference attendance and collegial exchange remain non-negotiable practices. Adoption of AI for this task in higher education is still marginal.
Sector adoption velocity	claude-sonnet-5	2/5	Higher education is a comparatively slow-adopting sector for AI tools in scholarly workflows, with pilots for research assistance emerging but not deeply embedded in faculty routines yet.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI excels at augmenting this task: literature alerts, summarization of papers, semantic clustering of topics, and identification of emerging research trends can dramatically accelerate a faculty member's awareness while they retain decision-making and networking roles. Productivity gains are substantial without removing the human from the loop.
Augmentation potential	claude-sonnet-5	4/5	AI literature review tools, summarization engines, and recommendation systems substantially speed up staying current with publications, meaningfully augmenting this task even though the human remains central.
Task automatability	claude-haiku-4-5-20251001	2/5	AI can automate literature monitoring and summarization (reading current publications), but the collaborative aspects (talking with colleagues, networking at conferences) require human presence and judgment. Only a subset of the task (passive information gathering) is automatable without significant quality loss.
Task automatability	claude-sonnet-5	2/5	AI can help surface and summarize literature, but the core activity—ongoing professional engagement, networking, and judgment about field developments—requires sustained human involvement and cannot be fully offloaded end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Professional and social norms in academia strongly favor direct colleague engagement and conference attendance as identity and career-development markers. Institutional culture and tenure expectations reinforce human participation, creating friction against full substitution despite technical feasibility.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human do this, but professional norms and tacit knowledge-sharing via colleagues and conferences create moderate organizational and social friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI tools for literature monitoring are inexpensive, but they do not eliminate the need for faculty time spent reading, synthesizing, and networking—the human-centric portions remain dominant costs. Integration into workflows adds overhead that only partially offsets human effort.
Cost vs. human wage	claude-sonnet-5	3/5	AI literature search/summarization tools are cheap relative to human time spent reading, but the task also includes non-automatable components (colleague discussion, conference attendance) that carry unavoidable human costs.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Deployed products (academic alert systems, literature summarization tools, semantic search) perform parts of this task reliably, but no end-to-end solution substitutes for human interaction and serendipitous discovery at conferences. Error rates and narrow scope limit comprehensive field awareness.
Technical feasibility today	claude-sonnet-5	2/5	Tools like AI-powered literature summarizers and research digests exist, but no deployed product autonomously 'keeps abreast' of a field by integrating reading, conversation, and conference participation for a scholar.

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

CI 25–34 · exposure 25 · augmentation 63 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Higher education is a laggard sector in AI adoption for core academic functions. While some institutions pilot AI for administrative tasks, curricular planning remains closely held by faculty. Adoption of AI for curriculum revision is in early pilot phase, not yet reflected in meaningful displacement or production workflows.
Sector adoption velocity	claude-sonnet-5	2/5	Higher education adopts AI tools unevenly and cautiously for curriculum work, with pilots and individual faculty experimentation more common than institutional-scale deployment.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can meaningfully assist faculty by drafting discussion prompts, suggesting assessment rubrics, identifying gaps in reading lists, or generating alternative pedagogical approaches. Such assistance can raise faculty productivity in material preparation, though the core evaluative and strategic decisions remain human-driven.
Augmentation potential	claude-sonnet-5	4/5	AI is genuinely useful for brainstorming topics, drafting materials, summarizing literature, and suggesting assessment methods, substantially speeding up an instructor's curriculum development process.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can generate course outlines, draft lecture materials, and suggest evaluation frameworks, the task fundamentally requires human judgment about pedagogical philosophy, student learning outcomes aligned with institutional values, and disciplinary expertise. Current AI lacks the nuanced understanding of sociology's evolving subfields and cannot reliably integrate institutional context, accreditation requirements, and departmental constraints into revised curricula.
Task automatability	claude-sonnet-5	2/5	AI can draft syllabi and suggest readings, but the holistic evaluation and revision of curricula requires disciplinary judgment, institutional context, and accreditation alignment that current systems cannot fully replace end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Faculty curricular authority is protected by academic governance structures; institutional accreditation bodies (e.g., regional accreditors, disciplinary associations) typically require human faculty sign-off on curricula. Legal and professional norms vest curriculum authority in faculty experts, not in automated systems, creating hard organizational and governance barriers.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement mandates a human specifically, but academic governance, peer review, and institutional curriculum committees create real procedural friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	While AI content generation is cheap, integrating AI outputs into a valid curriculum revision cycle requires significant faculty oversight, validation, and rework. The loaded cost of faculty time to review, critique, and integrate AI suggestions often exceeds the cost of faculty designing curricula directly.
Cost vs. human wage	claude-sonnet-5	3/5	AI drafting assistance is cheap per use, but faculty time for review, contextualization, and approval keeps the effective cost of the full task closer to human-comparable levels.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	AI tools can assist with material generation and provide curriculum templates, but no deployed system reliably performs the end-to-end evaluation and revision of curricula at the quality standard required for academic governance. Products exist for content drafting, but curriculum planning remains primarily a human expert task in actual institutional practice.
Technical feasibility today	claude-sonnet-5	2/5	Products like ChatGPT or course-design copilots are used informally by faculty for drafting materials, but no deployed system reliably plans and revises a full sociology curriculum in production at scale.

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

CI 25–32 · exposure 25 · augmentation 75 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Universities are piloting AI for writing assistance and literature synthesis, but adoption remains cautious due to concerns about intellectual integrity and disciplinary norms. Production-level AI research systems are uncommon; adoption velocity is middling within the academic sector.
Sector adoption velocity	claude-sonnet-5	2/5	Higher education and academic research are relatively slow to adopt AI for core research tasks compared to information/finance sectors, with usage concentrated in writing/analysis assistance rather than full task substitution.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI significantly augments research productivity by accelerating literature review, suggesting analytical approaches, drafting manuscript sections, and providing feedback on argumentation. These tools markedly improve researcher efficiency while the scholar remains the primary intellectual agent.
Augmentation potential	claude-sonnet-5	4/5	AI substantially aids literature reviews, data analysis, drafting manuscripts, and formatting for journals, meaningfully boosting researcher productivity while the sociologist retains intellectual ownership.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can assist with literature review, data analysis, and manuscript drafting, original research design, fieldwork interpretation, and the intellectual synthesis required for novel sociological insights remain fundamentally dependent on human expertise. AI cannot independently conceive, execute, and publish rigorous empirical research at the quality threshold needed for peer review.
Task automatability	claude-sonnet-5	2/5	Original sociological research requires designing studies, collecting/analyzing novel data, and generating theoretically grounded insights that current AI cannot autonomously perform end-to-end at equal quality, though AI can assist with literature review, drafting, and analysis subtasks.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Academic publication, funding, and institutional advancement require demonstrated original contribution and intellectual ownership—barriers that cannot be bypassed by automation. Tenure committees, peer review, and funders mandate human accountability and novelty attribution, creating structural protections against full automation.
Adoption barriers	claude-sonnet-5	3/5	No formal 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 wage	claude-haiku-4-5-20251001	2/5	AI tools (literature review, writing assistants) reduce some friction in research workflows, but the loaded cost of a tenure-track researcher remains far lower than any comparable human replacement when overhead is included. AI integration still requires substantial researcher time for design, oversight, and validation.
Cost vs. human wage	claude-sonnet-5	2/5	While AI can cheaply assist with parts of the research process, the human researcher's design, fieldwork, and interpretive judgment still dominate cost, so overall cost savings versus a human-led process are modest.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed product reliably performs end-to-end research conception, execution, and publication. AI can draft sections and suggest revisions, but real research systems depend on human researchers making critical methodological decisions, conducting fieldwork, and navigating peer review—tasks requiring domain expertise and judgment that current tools lack in production.
Technical feasibility today	claude-sonnet-5	2/5	AI tools (e.g., literature synthesis, statistical analysis assistants) are used in research workflows, but no deployed product independently conducts original sociological research and produces publishable findings without heavy human direction.

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

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

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Adoption of AI in academic advising remains limited to pilot projects and supplementary tools in postsecondary institutions. Most universities continue to rely on human advisors and counselors, and cultural and structural inertia in higher education slows AI integration despite digitization of record-keeping.
Sector adoption velocity	claude-sonnet-5	2/5	Higher education is a slower-adopting sector for AI in high-touch student services, though administrative pilots (chat-based advising bots) are emerging.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can meaningfully assist advisors by retrieving curriculum data, flagging prerequisite mismatches, synthesizing career outcome data, and generating advising notes, allowing human advisors to focus on relationship-building, problem-solving, and adaptive guidance that requires judgment and empathy.
Augmentation potential	claude-sonnet-5	4/5	AI can meaningfully assist faculty by drafting curriculum information, summarizing career pathways, and answering routine student questions, freeing time for higher-value advising conversations.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can provide factual information about curricula and career paths, advising students requires understanding individual circumstances, aspirations, and constraints. Current systems struggle with the nuanced judgment needed to tailor advice and the ongoing dialogue required for meaningful mentorship, limiting time savings to below 50% at equal quality.
Task automatability	claude-sonnet-5	2/5	Advising requires personalized judgment, relationship-building, and institutional knowledge that current AI cannot fully replicate end-to-end; only informational subtasks are automatable.chatbots can supply generic career information but cannot substitute for the full advising relationship.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Students and parents typically expect human contact and professional judgment from academic advisors. Institutional liability concerns, accreditation expectations, and the requirement that academic institutions employ credentialed advisors create substantial friction against full automation of this role.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement generally, but institutional policies, accreditation expectations, and student preference for human mentors create moderate friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	An AI-driven advising system would require significant integration, training data, and oversight by qualified advisors to ensure quality and liability management. The loaded cost of a full-time sociology professor or academic advisor remains lower than the total cost of maintaining a reliable AI system plus human oversight for this high-stakes task.
Cost vs. human wage	claude-sonnet-5	2/5	AI tools are cheap for generic Q&A, but human oversight, error correction, and the relational nature of advising keep effective costs closer to human labor for full task delivery.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Chatbots and career-matching tools exist and can deliver generic advice, but no deployed product reliably performs holistic academic-vocational advising at scale. These systems lack the ability to gather sufficient context, adapt to individual learning needs, or handle complex or non-standard situations that characterize real advising interactions.
Technical feasibility today	claude-sonnet-5	2/5	Some universities deploy chatbots for basic advising FAQs, but no product reliably handles nuanced academic/career advising at production scale for postsecondary students.

Write grant proposals to procure external research funding.

CI 25–30 · exposure 25 · augmentation 75 · importance 3.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Academia and research institutions are slower to adopt automation for grant writing; most adoption remains at the assistance level (AI drafting tools) rather than autonomous proposal generation, and funding agencies themselves are cautious about algorithmic involvement in peer-review and accountability processes.
Sector adoption velocity	claude-sonnet-5	2/5	Academia adopts AI writing aids unevenly and cautiously, with many funding bodies and universities imposing disclosure rules or skepticism about AI-generated proposal content.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can significantly augment grant writing by drafting sections, summarizing literature, polishing prose, and checking formatting compliance, allowing faculty to focus on research vision and strategic positioning without necessarily reducing human involvement.
Augmentation potential	claude-sonnet-5	4/5	AI is widely used to help brainstorm ideas, structure proposals, tighten language, and summarize literature, meaningfully speeding up the proposal-writing process while the researcher retains ownership of content and strategy.
Task automatability	claude-haiku-4-5-20251001	2/5	Grant writing requires deep domain knowledge, original research framing, and persuasive argumentation tailored to specific funding agencies—tasks that demand human judgment. While AI can draft sections and improve prose, the task of synthesizing research vision, justifying budgets, and positioning work competitively still relies heavily on human intellectual synthesis and cannot achieve 50% time savings at equal quality end-to-end.
Task automatability	claude-sonnet-5	2/5	AI can draft sections and generate boilerplate text, but crafting a compelling, funder-specific proposal requires deep domain expertise, novel research framing, and strategic judgment that current AI cannot fully replicate end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Institutional and regulatory barriers are substantial: funding agencies often require human certification of proposal accuracy, institutional signatures, and compliance with specific formatting and audit rules that mandate human authorization and accountability.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement, but funders and institutions expect the PI's original intellectual contribution and accountability, creating reputational and integrity-based friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Grant writing by a faculty member typically occurs alongside other duties and is hard to separate costwise; the loaded salary is high. AI tools reduce some drafting time but require expert oversight and rework, making the per-grant cost advantage marginal.
Cost vs. human wage	claude-sonnet-5	2/5	While AI drafting reduces some writing time cheaply, the extensive expert review, methodology design, and iteration still required mean overall cost savings versus a skilled academic are modest.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed product reliably writes complete, funding-agency-compliant grant proposals from scratch. AI systems can assist with literature reviews and draft sections, but current deployments lack the precision needed to handle budget justifications, compliance requirements, and agency-specific criteria without substantial human revision.
Technical feasibility today	claude-sonnet-5	2/5	AI writing tools are used for drafting and editing grant proposal sections, but no deployed product reliably produces full, competitive grant proposals without heavy human authorship and revision.

Participate in student recruitment, registration, and placement activities.

CI 25–30 · exposure 25 · augmentation 50 · importance 3.2/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Although some higher education institutions use marketing automation and CRM systems, actual displacement of faculty or advisors in recruitment and placement roles remains minimal; adoption is limited to administrative support functions rather than the core interpersonal work.
Sector adoption velocity	claude-sonnet-5	2/5	Higher education administrative processes are adopting AI tools like chatbots for admissions FAQs, but broad production-level automation of recruitment/placement activities remains limited and slow-moving in academia.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can usefully assist faculty by automating email scheduling, organizing applicant data, flagging placement opportunities, and generating recruitment materials, moderately raising productivity without replacing the human relationships essential to the task.
Augmentation potential	claude-sonnet-5	3/5	AI can meaningfully assist with drafting recruitment content, managing communications, and organizing registration data, improving efficiency while humans retain the relational and decision-making aspects.
Task automatability	claude-haiku-4-5-20251001	2/5	Student recruitment, registration, and placement require relationship-building, personalized communication, and judgment about individual fit—tasks that demand human interaction and contextual understanding. While AI can assist with scheduling, data entry, and initial information dissemination, it cannot independently manage the full recruitment pipeline or make placement decisions that require institutional knowledge and human persuasion.
Task automatability	claude-sonnet-5	2/5	This task involves interpersonal outreach, admissions events, advising, and institutional coordination that require human presence and judgment; AI can support parts (drafting materials, scheduling) but cannot perform the interactive recruitment and placement functions end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Educational institutions have regulatory and accreditation requirements around admissions and student services, and there is a strong institutional and professional expectation that faculty and advisors maintain direct personal contact with prospective and current students during recruitment and placement.
Adoption barriers	claude-sonnet-5	3/5	No strict licensing requirement mandates a professor's involvement, but institutional policy, accreditation, and personalized advising norms create moderate friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Automation of recruitment and placement activities requires significant integration with institutional systems and human oversight for validation and relationship management, making the all-in cost competitive with or higher than a human staff member handling these tasks.
Cost vs. human wage	claude-sonnet-5	2/5	AI tools can reduce some administrative costs (e.g., automated messaging) but the core relationship-building and decision-making in recruitment/placement still require paid staff/faculty time, keeping costs comparable rather than dramatically lower.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Some tools exist for recruitment marketing and registration automation (email, CRM systems), but current AI products cannot independently recruit students, evaluate their fit, or oversee placement conversations at production scale. Human oversight remains essential for meaningful student interactions and institutional accountability.
Technical feasibility today	claude-sonnet-5	2/5	Some CRM and chatbot products assist with recruitment communications and registration logistics, but no deployed system independently manages recruitment, registration, and placement activities for faculty-level participation.

Prepare and deliver lectures to undergraduate or graduate students on topics such as race and ethnic relations, measurement and data collection, and workplace social relations.

CI 25–25 · exposure 25 · augmentation 75 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Academia remains a slow adopter of workforce automation; institutions have structural incentives to retain faculty, and cultural norms prioritize live instruction. While some universities pilot AI-assisted content tools, end-to-end lecture replacement remains rare and resisted.
Sector adoption velocity	claude-sonnet-5	2/5	Higher education adopts AI unevenly and cautiously for teaching tasks, with slow institutional change, faculty resistance, and pilot-stage use of AI in course design rather than full lecture delivery.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI tools for generating lecture drafts, summarizing research, preparing visual aids, and formulating assessment questions can substantially augment instructor productivity in preparation and delivery. Many instructors already use ChatGPT or similar for these auxiliary tasks while maintaining full pedagogical control.
Augmentation potential	claude-sonnet-5	4/5	AI substantially aids lecture preparation—drafting content, generating examples, creating slides and quizzes—making it a strong productivity tool even though the instructor still delivers the lecture.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can draft lecture content and generate summaries, delivering engaging lectures requires real-time interaction, responsiveness to student questions, dynamic pedagogical judgment, and presence that current AI cannot replicate at the quality expected in higher education. Significant human oversight and delivery remain necessary.
Task automatability	claude-sonnet-5	2/5	AI can help draft lecture content and slides, but live delivery, classroom interaction, adaptive pacing, and answering unpredictable student questions require human presence and judgment that current systems cannot fully replace.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Universities require human instructors to deliver courses and hold fiduciary responsibility for student learning outcomes; institutional accreditation standards mandate faculty presence. Liability, student expectations of human instruction, and regulatory framework (faculty employment contracts, accreditation) strongly protect this task from end-to-end substitution.
Adoption barriers	claude-sonnet-5	4/5	Accreditation, university employment structures, and expectations of faculty-student interaction create strong institutional and credentialing barriers to full automation of lecturing.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI tools for lecture preparation (drafting, research synthesis) cost far less than instructor time, but integration, customization, and the human delivery component—which cannot be eliminated—mean the all-in cost of partial automation remains high relative to the instructor's loaded wage.
Cost vs. human wage	claude-sonnet-5	2/5	While AI can cheaply generate lecture drafts, actual delivery still requires a paid instructor, so the overall cost of replacing the full task remains comparable to or only modestly cheaper than human labor.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	AI can assist with content generation and lecture notes, but no deployed product reliably substitutes for the full task of preparing and delivering lectures with comparable educational outcomes. ChatGPT and similar tools exist but are narrowly scoped (content drafting only) and lack the evaluative and adaptive dimensions of actual teaching.
Technical feasibility today	claude-sonnet-5	2/5	AI tools (e.g., ChatGPT, slide generators) are used to assist lecture prep, but no deployed product autonomously delivers full postsecondary lectures in real classrooms at scale.

Initiate, facilitate, and moderate classroom discussions.

CI 11–32 · exposure 13 · augmentation 50 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Higher education remains a laggard sector for AI adoption in core teaching functions; most adoption is in grading support or tutoring, not classroom facilitation. Institutional conservatism, faculty autonomy, and lack of proven products limit velocity in this specific task.
Sector adoption velocity	claude-sonnet-5	2/5	Higher education adopts AI unevenly and cautiously for pedagogy, with pilots for grading/content generation far more common than AI-led discussion facilitation.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can usefully assist instructors by generating discussion prompts, flagging key themes in student responses, or suggesting follow-up questions, thereby raising preparation and real-time responsiveness. However, the instructor remains central to facilitation quality, limiting the transformative potential.
Augmentation potential	claude-sonnet-5	3/5	AI can help instructors prepare discussion questions, summarize readings, or analyze participation patterns, offering moderate support without replacing the live facilitation itself.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can generate discussion prompts and summarize points, facilitating genuine Socratic dialogue requires real-time judgment about group dynamics, emotional safety, and adaptive questioning that current systems struggle to replicate. Moderation involves subtle social cues and contextual wisdom that fall short of 50% time savings at equal quality.
Task automatability	claude-sonnet-5	1/5	Live classroom discussion facilitation requires real-time social judgment, reading a room, and adaptive rapport with students that current AI cannot replicate at equal quality despite any time savings.deceased
Adoption barriers	claude-haiku-4-5-20251001	4/5	Colleges and universities have strong institutional norms, accreditation standards, and student expectations that a faculty member—not an AI—leads live discussions and builds intellectual community. Faculty governance and educational mission create meaningful friction against full automation.
Adoption barriers	claude-sonnet-5	4/5	Tenure-track faculty roles, accreditation expectations, and student expectations of live human instruction create strong organizational and normative barriers to substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI discussion tools (LLMs, chatbots) incur minimal inference costs and could theoretically reduce instructor prep time, making them substantially cheaper than paying an instructor salary per unit of discussion facilitation. However, full replacement remains incomplete, so a perfect 5 is unwarranted.
Cost vs. human wage	claude-sonnet-5	2/5	Since AI cannot substitute for the live facilitation task itself, cost comparisons favor the human; any AI tools used are supplementary rather than replacing the labor.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Chatbots can participate in text-based discussions and suggest discussion topics, but no deployed product reliably facilitates live classroom discourse, reads room dynamics, or handles interpersonal conflicts at the quality expected in higher education. Narrow scope and lack of real-world classroom deployment limit feasibility.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously runs and moderates live in-person classroom discussions in production; existing tools are limited to online forum moderation or chatbot Q&A, not equivalent.

Provide professional consulting services to government or industry.

CI 11–25 · exposure 13 · augmentation 75 · importance 2.4/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Professional services and consulting remain human-led in practice; while AI assists with drafting and analysis, actual consulting delivery is not rapidly adopting full AI automation in government or industry sectors.
Sector adoption velocity	claude-sonnet-5	2/5	Academic and consulting sectors are adopting AI tools for research support but full-service consulting engagements remain human-led with slow structural change.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI significantly assists consulting work by accelerating literature reviews, synthesizing data, and drafting initial recommendations; sociologists can leverage these tools to enhance productivity while maintaining their expert judgment and client relationship.
Augmentation potential	claude-sonnet-5	4/5	AI can significantly assist with literature reviews, data analysis, report drafting, and background research, meaningfully boosting the consultant's productivity while they retain final judgment and client interaction.
Task automatability	claude-haiku-4-5-20251001	2/5	Generating standard consulting reports and analyses could be partially automated, but this task requires deep domain expertise, understanding client context, and high-stakes recommendations that demand human judgment and accountability; end-to-end automation would not achieve 50% time savings at equal quality.
Task automatability	claude-sonnet-5	1/5	Consulting requires original expert judgment, contextual synthesis, and stakeholder trust-building that current AI cannot autonomously deliver end-to-end for government or industry clients.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Consulting recommendations carry legal and reputational liability; government and industry clients typically require named human experts to stand behind advice, creating organizational and liability barriers to full automation.
Adoption barriers	claude-sonnet-5	4/5	Government and industry consulting often requires credentialed expertise, accountability, and trust that clients expect from a named professional, creating strong reputational and institutional barriers to full AI substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI can reduce research and writing time, but integrating AI oversight and the cost of human expertise required to validate and refine recommendations means the total cost remains close to or exceeds that of direct human consulting.
Cost vs. human wage	claude-sonnet-5	2/5	AI can cut some research and drafting time but the bulk of value—credibility, relationship management, tailored judgment—still requires the human expert, keeping overall cost comparable to human-led work.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While LLMs can draft consulting memos and summarize research, no deployed product reliably performs high-stakes consulting for government or industry clients; the stakes and need for subject-matter authority exceed current system capabilities.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product independently performs professional sociological consulting engagements; AI is at most a research or drafting aid used by the human consultant.

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

CI 5–21 · exposure 13 · augmentation 50 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Supervision of student work remains fundamentally a human responsibility in academic institutions, with minimal AI displacement occurring. Adoption of AI in this space is nascent and limited to peripheral administrative support rather than actual supervisory functions.
Sector adoption velocity	claude-sonnet-5	2/5	Higher education adopts AI tools slowly for core supervisory/mentorship functions, though some administrative aspects see incremental tool use.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI could assist with organizing feedback, summarizing student submissions, flagging common errors, or managing documentation, helping faculty allocate their supervision time more efficiently. However, the core judgment and mentoring remain human-driven.
Augmentation potential	claude-sonnet-5	3/5	AI can help draft feedback, track progress, suggest research resources, or assist in scheduling and administrative aspects of supervision, but doesn't replace the mentoring relationship.
Task automatability	claude-haiku-4-5-20251001	2/5	AI cannot currently perform the core supervisory function of overseeing student work, providing individualized feedback, and making judgment calls on academic progress or research quality. While AI might assist with administrative tracking or draft feedback, the task fundamentally requires ongoing human mentorship and discretionary assessment.
Task automatability	claude-sonnet-5	1/5	Supervising students' teaching, internships, and research requires ongoing relational mentorship, judgment calls, and accountability that current AI cannot perform end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Postsecondary institutions have formal governance structures, accreditation standards, and fiduciary duties requiring a qualified human faculty member to supervise student learning and research. Liability and educational integrity requirements create significant regulatory and institutional barriers to AI substitution.
Adoption barriers	claude-sonnet-5	4/5	Institutional policy, accreditation, and academic governance typically require a qualified faculty member to formally supervise and evaluate student work, creating strong structural barriers.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Supervision requires sustained human judgment and cannot be meaningfully displaced by AI systems. The loaded cost of a faculty member's time for this task would exceed any AI cost, making substitution economically inviable without unacceptable quality loss.
Cost vs. human wage	claude-sonnet-5	1/5	AI cannot substitute for the supervisory role, so there is no viable cost comparison—human oversight remains mandatory and any AI cost is additive, not substitutive.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed product reliably supervises academic work end-to-end. Some institutions use learning management systems for basic tracking, but actual supervision—reviewing student progress, adjusting guidance, evaluating research rigor—remains performed by humans with AI at best offering peripheral support.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product supervises student research or teaching assistants autonomously; this remains a human faculty responsibility in practice.

Collaborate with colleagues to address teaching and research issues.

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

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Academic institutions are typically slow adopters of labor-displacing automation, and the governance of teaching and research strategy is explicitly reserved for human faculty. Adoption of AI for peer collaboration is negligible.
Sector adoption velocity	claude-sonnet-5	2/5	Higher education adopts AI slowly for governance and collaborative faculty processes, though tools for communication and document sharing are common.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI can modestly assist by drafting collaborative documents, organizing meeting notes, or suggesting research gaps, but the core negotiation and trust-building between colleagues remains human-driven and is not meaningfully augmented by current systems.
Augmentation potential	claude-sonnet-5	3/5	AI can help draft meeting notes, summarize research literature, or prepare materials that support collegial discussions, offering moderate assistance without replacing the collaboration itself.
Task automatability	claude-haiku-4-5-20251001	1/5	Genuine collaboration on teaching and research issues requires interpersonal negotiation, contextual judgment about academic priorities, and the ability to build consensus among peers with competing interests. Current AI cannot participate as a peer in this socially embedded, recursive decision-making process.
Task automatability	claude-sonnet-5	1/5	This is an interpersonal, collaborative task requiring social relationship-building, negotiation, and shared institutional judgment that current AI cannot perform end-to-end.atical AI has no substitute role here beyond facilitating logistics.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Academic norms, institutional governance structures, and professional identity all require human faculty to be responsible for curriculum and research direction decisions. Colleagues expect human judgment and accountability in these discussions.
Adoption barriers	claude-sonnet-5	4/5	Faculty governance, tenure structures, and academic collegiality norms require human participation in departmental decision-making, creating strong organizational and cultural barriers to replacement.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The task requires human expertise and presence; AI could only supplement minor administrative overhead, not replace the core labor of collaborative deliberation. The loaded cost of faculty time is high, and AI offers no cost advantage for the substantive collaboration itself.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI product performing this task independently, so no meaningful cost comparison exists; humans remain the only option.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product performs peer-to-peer academic collaboration. AI can draft emails or summarize discussions, but cannot authentically participate in or lead collaborative problem-solving among faculty colleagues.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously collaborates with human colleagues on substantive academic/research decisions; this remains a human social process.

Act as advisers to student organizations.

CI 5–7 · exposure 0 · augmentation 25 · importance 2.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Academic institutions are among the slowest sectors to adopt AI for core advising and mentorship functions, preferring human continuity and relationship. No evidence of meaningful AI deployment for student organization advising in postsecondary contexts.
Sector adoption velocity	claude-sonnet-5	2/5	Higher education adopts AI tools for administrative and instructional support, but advisory/mentorship roles for organizations see minimal AI penetration.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist with scheduling, document management, or providing factual information to advisers, but the core task of advising—listening, mentoring, and guiding student organizations—remains fundamentally human and offers limited augmentation potential without diminishing the adviser's role.
Augmentation potential	claude-sonnet-5	2/5	AI can help with logistics like scheduling, drafting communications, or budget tracking, but the core advising relationship remains largely unassisted.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires nuanced interpersonal judgment, mentorship, and situational decision-making that are fundamentally dependent on human relationship-building and contextual understanding. Current AI systems cannot reliably serve as advisers to student organizations, which involves managing group dynamics, providing career guidance, and supporting student development.
Task automatability	claude-sonnet-5	1/5	Advising student organizations requires ongoing relational trust, mentorship, in-person presence, and institutional judgment that current AI cannot replicate end-to-end.'
Adoption barriers	claude-haiku-4-5-20251001	4/5	Strong barriers exist: institutions typically require credentialed faculty or staff to formally advise student organizations, there are liability and duty-of-care expectations around student welfare and guidance, and organizational culture strongly prefers human mentors for this developmental function.
Adoption barriers	claude-sonnet-5	4/5	Institutions typically require a designated faculty/staff member to serve as an official advisor for liability, signature authority, and accountability reasons.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	A human adviser's loaded cost is modest relative to institutional budgets, and AI systems would require significant customization, oversight, and fallback human review, making the all-in cost comparable to or exceeding human labor for this trust-dependent role.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute delivering the same output, so cost comparison favors the human by default.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably performs this advisorial role at scale. While AI can assist with information provision or draft communications, no existing system can serve as a functional adviser to student organizations in a production environment.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs this advisory/mentorship role for student groups; it remains fundamentally a human relational role.

Maintain regularly scheduled office hours to advise and assist students.

CI 0–11 · exposure 0 · augmentation 50 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Higher education adoption of AI in advising is nascent and limited to supplementary chatbots for FAQs, not replacement of faculty office hours. Institutional inertia, faculty resistance, and accreditor expectations mean displacement of this function remains minimal.
Sector adoption velocity	claude-sonnet-5	2/5	Higher education adopts AI tools for administrative or supplementary tasks slowly and unevenly, with office hours remaining a traditional, low-digitization interaction.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI tools could assist by summarizing student records, suggesting relevant resources, or drafting follow-up notes, but the core interaction—listening, advising, and relationship-building—must remain with the faculty member to be effective.
Augmentation potential	claude-sonnet-5	3/5	AI can help by handling scheduling, answering routine student questions, or drafting materials, freeing time for the human-centered aspects of advising during office hours.
Task automatability	claude-haiku-4-5-20251001	1/5	Office hours require real-time, personalized interaction with students to address their unique academic and personal concerns. Current AI cannot replicate the adaptive, empathetic, and contextual advising that this task demands, nor can it establish the ongoing mentoring relationships that are central to the function.
Task automatability	claude-sonnet-5	1/5	This task inherently requires synchronous human presence, relationship-building, and personalized mentoring that cannot be replicated end-to-end by AI systems today.the physical/scheduled nature of office hours resists automation.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Federal regulations (Title IV, accreditation standards) and institutional policy often mandate that faculty maintain advising availability. Many institutions legally require faculty sign-off on academic plans, and student expectation of direct faculty contact creates strong organizational and liability barriers.
Adoption barriers	claude-sonnet-5	4/5	Advising is often tied to institutional role requirements, accreditation expectations, and student expectations of human mentorship, creating strong organizational and normative barriers to full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The loaded cost of a faculty member holding office hours is already embedded in their salary; the marginal cost of scheduling and conducting them is negligible. Any AI system would require significant infrastructure and oversight at costs approaching or exceeding the convenience savings.
Cost vs. human wage	claude-sonnet-5	2/5	While AI chat support is cheap per query, it cannot replace the full scope of advising duties, so the relevant cost comparison for genuine substitution is unfavorable to AI given added integration and trust-building needs.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	While chatbots can answer basic FAQs, no deployed system reliably performs the full scope of office-hour advising—understanding student problems, offering tailored guidance, and making course/career recommendations—in a way that substitutes for human faculty availability.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product substitutes for a professor's scheduled office hours; chatbots may answer FAQs but do not fulfill the institutional/relational function of advising sessions.

Supervise students' laboratory and field work.

CI 0–5 · exposure 0 · augmentation 25 · importance 3.7/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Supervisory presence in labs and field settings is mandated by institutional, liability, and safety frameworks; adoption of AI 'supervision' remains non-existent because the task cannot be automated and human presence is non-negotiable.
Sector adoption velocity	claude-sonnet-5	1/5	Postsecondary field/lab supervision is a low-digitization, physically-anchored activity where AI adoption is essentially absent.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI might assist peripherally (e.g., flagging common data-collection errors or scheduling logistics), but the core supervisory role—ensuring safety, guiding learning, responding to emergencies—remains fundamentally human and offers little room for AI augmentation.
Augmentation potential	claude-sonnet-5	2/5	AI can help with prep materials, safety checklists, or data analysis afterward, but offers minimal real-time assistance during actual supervision of physical activities.
Task automatability	claude-haiku-4-5-20251001	1/5	Supervising laboratory and field work requires real-time monitoring, safety assessment, intervention during hazards, and adaptive guidance tailored to each student's progress and mistakes—all demanding human presence and judgment that AI cannot provide end-to-end today.
Task automatability	claude-sonnet-5	1/5	Supervising students during hands-on laboratory or field work requires physical presence, real-time safety oversight, and interpersonal guidance that current AI cannot perform end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Educational institutions and accreditors legally require qualified human supervision of laboratory and field work for student safety, liability protection, and program accreditation—no automation can substitute for this duty of care.
Adoption barriers	claude-sonnet-5	4/5	Institutional liability, student safety regulations, and accreditation standards typically require a qualified faculty member to supervise fieldwork and labs in person.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	AI cannot replace live supervision of field and laboratory work, so no meaningful cost comparison exists; human supervision remains mandatory and non-negotiable by regulation and safety requirements.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI system replacing this task, so the human cost is the only available option, making AI not cheaper by definition.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI system reliably supervises live student laboratory or field work; this task intrinsically requires human observation, physical presence, safety responsibility, and dynamic response to unpredictable student behavior in real environments.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product supervises students in physical field or lab settings; this remains firmly in human hands with no viable substitute in production.

Mentor new faculty.

CI 0–5 · exposure 0 · augmentation 25 · importance 3.2/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Higher education has been slow to adopt AI for core faculty development tasks. Mentoring relationships are deeply valued as interpersonal and remain delegated to humans; academia is a traditionally low-velocity sector for workforce automation, especially for non-routine professional tasks.
Sector adoption velocity	claude-sonnet-5	1/5	Higher education mentoring practices show minimal AI adoption; this is a slow-moving, relationship-based academic tradition with little digitization.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI might assist mentors by drafting guidance documents or summarizing mentee progress notes, but such support is peripheral to the relational core of mentoring. The degree of productivity enhancement is minimal compared to the human-centered nature of the task.
Augmentation potential	claude-sonnet-5	2/5	AI could help mentors find resources, draft advice documents, or summarize policies, but it offers limited assistance to the core relational mentoring activity.
Task automatability	claude-haiku-4-5-20251001	1/5	Mentoring new faculty requires sustained relationship-building, contextual judgment about professional development, and deep understanding of individual needs and institutional culture. Current AI systems cannot replicate the personalized guidance, role modeling, and career navigation that define effective mentorship.
Task automatability	claude-sonnet-5	1/5	Mentoring new faculty requires interpersonal trust, career guidance, institutional politics knowledge, and personalized relationship-building that AI cannot perform end-to-end.'
Adoption barriers	claude-haiku-4-5-20251001	5/5	Faculty mentorship is often a formal institutional role with fiduciary responsibility for junior colleagues' career outcomes. Regulatory and professional norms, liability concerns, and the requirement for a recognized senior scholar to sign off on mentorship advice create hard barriers to automation.
Adoption barriers	claude-sonnet-5	4/5	Mentoring is deeply embedded in tenure, promotion, and departmental culture, requiring a credentialed senior faculty member; institutional norms and trust strongly favor human mentors.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	AI deployment for this task would require significant setup and human oversight to be trustworthy, likely exceeding the opportunity cost of a faculty mentor allocating time. The loaded cost of meaningful human mentorship is lower than the total integrated cost of an AI system attempting to replace it.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute service to price against human mentoring time, so AI is not a cheaper alternative for this task.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI product reliably performs faculty mentoring end-to-end. While AI can assist with information provision or resource curation, the core task—developing a mentee through dialogue, feedback, and professional relationship—remains exclusively human in production settings.
Technical feasibility today	claude-sonnet-5	1/5	No deployed products act as faculty mentors; this remains a human relational role with no research-stage or production analog performing the full function.

Participate in campus and community events.

CI 0–5 · exposure 0 · augmentation 25 · importance 2.7/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	No meaningful AI adoption is occurring for this task because it is inherently about human presence and interpersonal engagement, not a process that can be digitized or automated regardless of sector.
Sector adoption velocity	claude-sonnet-5	1/5	Higher education faculty community engagement is a low-digitization, in-person activity with minimal AI adoption pressure.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could modestly assist by helping with event logistics, scheduling, or summarizing outcomes, but it cannot enhance the core activity of human participation and presence at these events.
Augmentation potential	claude-sonnet-5	2/5	AI could help with scheduling, event promotion, or follow-up communications, but offers little assistance to the core act of participating.
Task automatability	claude-haiku-4-5-20251001	1/5	Participating in campus and community events requires physical presence, social interaction, relationship-building, and contextual judgment that current AI systems cannot perform. This task is fundamentally about human engagement and presence.
Task automatability	claude-sonnet-5	1/5	Participating in physical/social campus and community events requires human presence, relationship-building, and in-person engagement that AI cannot perform end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	5/5	This task has intrinsic barriers: events require human attendance and presence, institutional relationships depend on personal participation, and community engagement is fundamentally a human-contact requirement that cannot be delegated to AI.
Adoption barriers	claude-sonnet-5	4/5	Institutional expectations, community relationship-building, and the inherently social/representative nature of the role create strong barriers to substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	AI has no cost advantage here because the task requires human presence and participation; there is no meaningful way to automate it, making direct cost comparison inapplicable.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute performing this task, so cost comparison favors the human by default.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No AI product can physically attend or meaningfully participate in campus and community events. While AI can assist with event planning or analysis, it cannot substitute for the embodied human participation required by this task.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product attends or participates in events on behalf of a person in any meaningful, socially valid way.

Perform administrative duties, such as serving as department head.

CI 0–3 · exposure 0 · augmentation 38 · importance 3.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Academic administration remains highly resistant to automation; institutions rely on human department heads for legal liability, relationship management, and organizational culture. No measurable adoption of AI replacing these roles exists.
Sector adoption velocity	claude-sonnet-5	2/5	Higher education administration adopts AI slowly for governance and leadership functions, though some administrative sub-tasks like scheduling see tool adoption.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could modestly assist with data aggregation, meeting scheduling, or draft communications, but the strategic and interpersonal core of department leadership is not materially enhanced by current AI tools. Augmentation potential is limited.
Augmentation potential	claude-sonnet-5	3/5	AI can help draft reports, summarize data, schedule meetings, and manage correspondence, easing some administrative burden of the role.
Task automatability	claude-haiku-4-5-20251001	1/5	Department head duties involve strategic decision-making, personnel management, budget oversight, and stakeholder negotiation—tasks requiring judgment, accountability, and human authority that current AI cannot perform end-to-end. No AI system today can meaningfully automate the core administrative functions of academic leadership.
Task automatability	claude-sonnet-5	1/5	Serving as department head involves personnel decisions, budget oversight, strategic planning, and institutional politics that require judgment, authority, and accountability AI cannot provide end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Strong legal and governance barriers: a human must legally hold the department head title, make hiring/firing decisions, sign off on budgets, and be accountable to institutional leadership. The role is inherently human by institutional design.
Adoption barriers	claude-sonnet-5	5/5	Department head is a formal institutional appointment carrying legal signing authority, personnel/HR responsibility, and accountability that requires a human employee in that role.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Department head compensation reflects significant human expertise and responsibility; automating even fragments of the role would require expensive AI infrastructure plus human oversight, making total cost far exceed displacement savings.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI substitute performing this leadership function, so no meaningful cost comparison exists—the human role remains fully necessary.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI product performs department head duties; the role requires legal authority, fiduciary responsibility, and organizational accountability that only humans can legally and ethically exercise. This remains firmly in the domain of human organizational leadership.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs departmental leadership or headship roles; AI tools at best assist with scheduling or document drafting, not the administrative role itself.

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

CI 0–0 · exposure 0 · augmentation 38 · importance 3.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Zero adoption and near-zero plausibility. Committee governance is foundational to academic institutions and shows no trend toward AI substitution; if anything, oversight of institutional decisions has become more rigorous.
Sector adoption velocity	claude-sonnet-5	1/5	Academic governance and committee work show essentially no AI adoption or displacement trend; this is a deeply human, procedural, and political activity.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could marginally assist by drafting meeting agendas, summarizing policies, or preparing briefing materials, but the core task—deliberation and voting on institutional matters—remains fully human-driven with minimal productivity gain from assistive tools.
Augmentation potential	claude-sonnet-5	3/5	AI can help draft policy summaries, meeting minutes, agendas, or background research to support committee members, offering moderate productivity assistance.
Task automatability	claude-haiku-4-5-20251001	1/5	Committee service requires nuanced judgment, interpersonal negotiation, and institutional knowledge that demand human decision-making authority. AI cannot meaningfully participate in deliberations, voting, or accountability for policy decisions.
Task automatability	claude-sonnet-5	1/5	Committee service requires human judgment, negotiation, institutional politics, and personal accountability that AI cannot perform end-to-end; no time-saving automation applies here.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Hard legal and governance barriers: institutional bylaws, accreditation standards, and faculty governance structures require human faculty participation in committees. Many institutions have legal requirements that committees consist of credentialed human representatives.
Adoption barriers	claude-sonnet-5	5/5	Committee membership requires institutional standing, faculty governance rights, and accountability structures that legally and organizationally restrict this to human faculty.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Committee service is a professional duty tied to tenure and salary, not a discretionary task with separable costs. Attempting to replace human committee members would require hiring humans anyway, making cost comparison moot.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI substitute performing this role, so cost comparison favors the human by default since the task cannot be automated.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product can autonomously serve on academic committees or participate in institutional governance in any production setting. This task inherently requires human representatives with legal standing and fiduciary responsibility.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product actually serves as a committee member or votes on institutional policy; this remains purely human governance work.

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.