Political Science Teachers, Postsecondary

25-1065.00
Median wage $98,070/yr16,970 employed (US)Rank #355 of 923 scored · top 38% by substitution

Teach courses in political science, international affairs, and international relations. 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

Substitution32
Exposure30
Augmentation62

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

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

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

Tasks on the substitution scale

22 rated tasks, binned by substitution score.

Position among all scored occupations

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

Tasks with substitution ≥ 70

14%

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

Why this score

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

Task automatabilityw 35%29

panel mean rating 2.2/5 → substitution pressure 29/100

Technical feasibility todayw 20%30

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

Cost vs. human wagew 15%35

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

Adoption barriersw 20%inverted — strong barriers lower the score36

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

Sector adoption velocityw 10%28

panel mean rating 2.1/5 → substitution pressure 28/100

Task breakdown (22 tasks)

Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.

Maintain student attendance records, grades, and other required records.

95

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

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510015/5Postsecondary institutions have near-universal adoption of digital grade and attendance management systems; this automation is already deeply embedded in institutional practice.
Sector adoption velocityclaude-sonnet-55/5Higher education has near-universal adoption of digital LMS and gradebook tools, making this one of the most deeply automated administrative tasks in academia.
Augmentation potentialclaude-haiku-4-5-202510013/5LMS systems assist instructors by streamlining data entry and generating reports, but once automated the human role is largely eliminated rather than augmented in the traditional sense.
Augmentation potentialclaude-sonnet-54/5AI-enhanced gradebooks and analytics tools help instructors track trends, flag at-risk students, and reduce manual entry, though final grade judgment remains with the instructor.
Task automatabilityclaude-haiku-4-5-202510015/5Record maintenance is a well-structured, rule-based task that current AI systems and learning management systems (Blackboard, Canvas, etc.) handle end-to-end daily, with >50% time savings over manual entry and filing.
Task automatabilityclaude-sonnet-55/5Recording attendance and grades is a structured data-entry and calculation task fully handled by existing LMS/gradebook software, often already automated without dedicated AI.5
Adoption barriersclaude-haiku-4-5-202510012/5Institutions must comply with FERPA and accreditation standards for record-keeping accuracy and security, requiring some institutional oversight and validation; however, the task itself is not legally restricted to humans and LMS adoption is already near-universal.
Adoption barriersclaude-sonnet-52/5Some institutional policy requires instructor sign-off on final grades, but the record-keeping mechanics themselves face minimal regulatory or licensing barriers.
Cost vs. human wageclaude-haiku-4-5-202510015/5The cost of LMS licensing and cloud infrastructure per student record is orders of magnitude cheaper than dedicated administrative staff time; automation eliminates labor entirely.
Cost vs. human wageclaude-sonnet-55/5Digital record-keeping systems cost a fraction of a cent per transaction compared to manual clerical time, an order of magnitude cheaper than faculty time spent on this administrative task.
Technical feasibility todayclaude-haiku-4-5-202510015/5Deployed LMS and administrative software products reliably perform this task at scale in production across thousands of educational institutions; data entry and record-keeping are mature, proven capabilities.
Technical feasibility todayclaude-sonnet-55/5Learning management systems (Canvas, Blackboard, PowerSchool, etc.) already perform automated gradebook calculations, attendance tracking, and record-keeping reliably at scale in production.

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

80

CI 76–84 · exposure 75 · augmentation 100 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Academic institutions, particularly postsecondary settings, have rapidly adopted AI tools for course preparation and administrative tasks. Widespread adoption in higher education (information/knowledge work sector) is visible through institutional policies and faculty tools, though not yet universally mandated.
Sector adoption velocityclaude-sonnet-53/5Higher education is adopting AI writing tools for course prep at a moderate pace, with growing informal use but no systematic institutional deployment yet.
Augmentation potentialclaude-haiku-4-5-202510015/5AI dramatically augments instructor productivity in material preparation by generating drafts, alternative framings, and comprehensive resource sets that the instructor can then refine and contextualize, maintaining full pedagogical control while saving substantial preparation time.
Augmentation potentialclaude-sonnet-55/5AI substantially speeds up drafting syllabi, assignments, and handouts while the instructor retains control over final content and pedagogical judgment.
Task automatabilityclaude-haiku-4-5-202510014/5Current AI systems can generate course materials with substantial time savings: producing syllabi, assignments, and handouts from course parameters and learning objectives is well within LLM capability. However, the task requires some human judgment regarding institutional requirements and course-specific nuance, preventing a full 5.
Task automatabilityclaude-sonnet-54/5LLMs can draft syllabi, homework assignments, and handouts from a course description with substantial time savings, though instructor review and customization are still needed for accuracy and alignment with learning goals.
Adoption barriersclaude-haiku-4-5-202510012/5There are minimal legal or regulatory barriers to AI-assisted material generation. Most institutions are adopting these tools for drafting purposes, and there is no mandatory human sign-off requirement. Barriers are mainly organizational and cultural (instructor preference, accreditation caution), not structural.
Adoption barriersclaude-sonnet-51/5No licensing or regulatory requirement mandates a human create course materials; faculty routinely use templates and now AI drafting tools with no institutional or legal obstacle.
Cost vs. human wageclaude-haiku-4-5-202510015/5The cost of AI inference plus light human review is negligible compared to the hourly rate of a postsecondary instructor preparing these materials from scratch, creating an order-of-magnitude cost advantage.
Cost vs. human wageclaude-sonnet-55/5Generating a draft syllabus or assignment set via an LLM costs cents compared to the hours of faculty time otherwise required, an order-of-magnitude cost advantage.
Technical feasibility todayclaude-haiku-4-5-202510014/5Deployed products (ChatGPT, Claude, specialized educational platforms) reliably generate course materials in production settings. Many institutions now use AI for draft creation. Minor limitations exist around perfect institutional compliance and discipline-specific pedagogical nuance, but core functionality is mature and widely available.
Technical feasibility todayclaude-sonnet-54/5Deployed tools like ChatGPT, Claude, and education-specific platforms are already widely used by instructors to generate syllabi and assignment drafts in production, though quality varies and human editing is typical.

Compile bibliographies of specialized materials for outside reading assignments.

74

CI 67–81 · exposure 70 · augmentation 88 · importance 3.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510013/5Adoption is growing in higher education but remains uneven; many institutions pilot AI tools for administrative support like bibliography generation, but widespread production integration in academic workflows lags behind business sectors.
Sector adoption velocityclaude-sonnet-53/5Higher education is adopting AI tools for research assistance at a moderate pace, with pilots and individual faculty use common but institutional deployment still uneven.
Augmentation potentialclaude-haiku-4-5-202510014/5AI significantly boosts faculty productivity by generating initial drafts, identifying relevant sources, and handling formatting, allowing instructors to focus on evaluating relevance and pedagogical fit rather than mechanical research and citation work.
Augmentation potentialclaude-sonnet-55/5AI significantly speeds up bibliography compilation by suggesting relevant sources, summarizing content, and formatting citations, while the instructor retains control over final selection and academic judgment.
Task automatabilityclaude-haiku-4-5-202510014/5AI can reliably compile bibliographies by searching academic databases, filtering by topic and recency, and formatting citations—reducing manual research time by 70%+ while maintaining quality comparable to human curation at equal effort.
Task automatabilityclaude-sonnet-54/5Compiling bibliographies of relevant readings on a topic is a well-structured retrieval/synthesis task that current AI with search/citation tools can do quickly, requiring mostly human verification rather than full manual research.
Adoption barriersclaude-haiku-4-5-202510012/5Minimal legal or regulatory barriers; the task requires no licensing or liability protection, though faculty may prefer human curation for editorial judgment and institutional preference for human oversight remains common.
Adoption barriersclaude-sonnet-51/5There is no licensing, legal, or professional requirement that a human must personally compile a reading list; it's an administrative/academic task with no regulatory protection.
Cost vs. human wageclaude-haiku-4-5-202510015/5AI inference costs for bibliography generation are negligible (<$0.01 per compilation), vastly cheaper than paying faculty time to manually search databases and format citations.
Cost vs. human wageclaude-sonnet-54/5Generating a draft bibliography via AI tools costs a fraction of a professor's or TA's hourly wage compared to manual literature searching, even after factoring in verification time.
Technical feasibility todayclaude-haiku-4-5-202510014/5Multiple deployed products (ChatGPT, Perplexity, academic database integrations) perform bibliography compilation with high reliability; some have narrow scope or require human verification of sources, but production use is common in educational settings.
Technical feasibility todayclaude-sonnet-53/5AI research assistants and citation tools (e.g., reference managers with AI search, LLMs with web/scholar plugins) exist and are used in practice, but hallucinated or outdated citations still require faculty verification, limiting full reliability.

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

62

CI 51–74 · exposure 62 · augmentation 75 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Higher education has adopted digital assessment tools and auto-grading platforms widely over the past decade; most universities now use LMS with integrated assessment automation, indicating fast, deep adoption in the postsecondary sector.
Sector adoption velocityclaude-sonnet-52/5Higher education adoption of AI for grading/exam creation is still cautious and uneven, with pilots more common than large-scale deployment given academic integrity concerns.
Augmentation potentialclaude-haiku-4-5-202510014/5AI substantially assists faculty by drafting exam questions, generating multiple variations, auto-scoring, and providing analytics on student performance, enabling instructors to focus on pedagogical analysis and improvement rather than manual grading labor.
Augmentation potentialclaude-sonnet-54/5AI tools meaningfully help instructors draft questions, create rubrics, and provide first-pass grading suggestions, improving efficiency while the instructor retains final judgment.
Task automatabilityclaude-haiku-4-5-202510014/5AI can fully automate compilation of exams from test banks, generate novel questions, administer online assessments, and grade objective items at scale. Grading of subjective responses remains partially manual, but the majority of workflow—exam creation, delivery, and scoring—meets the ≥50% time-saving threshold with current systems.
Task automatabilityclaude-sonnet-53/5AI can draft exam questions and grade objective or even essay-type responses with rubrics, but compiling exams aligned to specific course objectives and grading nuanced political science analysis still needs faculty oversight for quality and fairness.
Adoption barriersclaude-haiku-4-5-202510013/5No legal prohibition exists; however, institutional policies often require faculty to retain oversight of assessment, and concerns about academic integrity and bias in automated grading create moderate friction against full delegation.
Adoption barriersclaude-sonnet-53/5No licensing requirement for grading, but academic integrity, grading accuracy expectations, and institutional policies on AI use in assessment create moderate friction.
Cost vs. human wageclaude-haiku-4-5-202510015/5Automated exam administration and objective grading via cloud-based LMS and AI inference costs pennies per student; human instructor time for the same tasks costs tens of dollars. The cost advantage is orders of magnitude.
Cost vs. human wageclaude-sonnet-54/5Generating and grading assistance via LLMs is inexpensive compared to faculty or TA time, though oversight and calibration checks add some cost.
Technical feasibility todayclaude-haiku-4-5-202510014/5Deployed LMS platforms (Canvas, Blackboard) and AI tools routinely handle exam generation, online proctoring, and objective grading in production at thousands of institutions. Subjective answer grading by AI still carries material error rates, limiting the rating from 5.
Technical feasibility todayclaude-sonnet-53/5AI-assisted grading and question-generation tools (e.g., LLM-based rubric graders, quiz generators) exist and are used in some courses, but reliable, widely deployed use for postsecondary essay grading remains limited and error-prone for nuanced content.

Select and obtain materials and supplies, such as textbooks.

48

CI 30–65 · exposure 50 · 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 procurement remains largely manual and bureaucratic; while digital tools exist, they are institution-specific and adoption of autonomous AI-driven procurement in academic settings remains nascent and slow.
Sector adoption velocityclaude-sonnet-52/5Higher education adoption of AI for administrative/curricular tasks like material selection remains slow and pilot-stage, lagging corporate sectors.
Augmentation potentialclaude-haiku-4-5-202510014/5AI can meaningfully assist by searching multiple databases, comparing prices and reviews, filtering by learning outcomes, and presenting curated options, substantially raising the efficiency of human faculty selection without removing their judgment.
Augmentation potentialclaude-sonnet-54/5AI can efficiently surface relevant textbooks, summarize reviews, compare editions/prices, and check syllabi alignment, meaningfully speeding up the selection process while the instructor makes the final call.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can search for and identify textbooks matching criteria, the actual procurement requires human judgment about institutional budgets, vendor relationships, approval workflows, and approval authority that AI cannot fully execute without substantial human oversight.
Task automatabilityclaude-sonnet-54/5Identifying, comparing, and procuring textbooks/materials is largely an information-retrieval and decision task that AI can handle well, given syllabus goals and course level.recommend.
Adoption barriersclaude-haiku-4-5-202510014/5Institutional procurement policies, budget approval requirements, and vendor contracts typically mandate human decision-making and sign-off; many institutions have formal authorization hierarchies that require faculty or administrators to approve expenditures.
Adoption barriersclaude-sonnet-52/5No licensing requirement dictates textbook selection, but faculty autonomy, academic freedom norms, and departmental approval processes create moderate organizational friction.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI assistance in searching and filtering textbooks costs relatively little, but the human time saved is modest since the task is already straightforward; full automation would require integration into institutional procurement systems that may not justify the cost.
Cost vs. human wageclaude-sonnet-54/5Using AI to search and shortlist materials is far cheaper than a professor spending hours reviewing catalogs, though final selection and ordering still require some human time.
Technical feasibility todayclaude-haiku-4-5-202510013/5Current AI systems can assist with material discovery and comparison (search, pricing, reviews), but deployed procurement workflows typically require human authorization and final selection based on institutional policies and financial constraints.
Technical feasibility todayclaude-sonnet-53/5AI tools (search, recommendation engines, chatbots) can suggest textbooks and supplies, but faculty still typically vet content, alignment with curriculum, and licensing/adoption processes manually, so few institutions have fully deployed this end-to-end.

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

41

CI 34–48 · exposure 45 · augmentation 75 · importance 4.6/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Educational institutions are among the slowest sectors to adopt AI for core academic tasks like grading; pilots are growing but production deployment remains rare. Faculty autonomy, union considerations, and institutional conservatism around assessment delay rapid adoption.
Sector adoption velocityclaude-sonnet-52/5Higher education adoption of AI grading tools is slow and cautious due to concerns about accuracy, bias, and academic integrity, with pilots more common than full deployment.
Augmentation potentialclaude-haiku-4-5-202510014/5AI can assist faculty significantly by drafting feedback, organizing submissions, flagging common errors, and proposing grade-aligned rubric scoring, freeing faculty to focus on higher-level reasoning and personalized comments. Many faculty find such tools valuable for productivity without replacing their judgment.
Augmentation potentialclaude-sonnet-54/5AI can meaningfully speed up grading by drafting feedback, flagging plagiarism, and checking rubric criteria, letting instructors focus on final judgment and nuanced evaluation.
Task automatabilityclaude-haiku-4-5-202510013/5AI can handle routine grading tasks like multiple-choice questions and flag grammatical issues in papers, but evaluating nuanced political arguments, thesis quality, and critical reasoning requires human judgment. Current systems can automate perhaps 40–50% of grading workflows with significant setup and review overhead.
Task automatabilityclaude-sonnet-53/5AI can draft rubric-based feedback and catch grammar/structure issues, but grading nuanced political science arguments, originality, and disciplinary depth still requires human judgment for full end-to-end reliability.
Adoption barriersclaude-haiku-4-5-202510014/5Academic institutions have strong norms and institutional policies around faculty grading authority, and students often have formal appeal processes requiring expert human judgment. Liability concerns and accreditation expectations that grades reflect faculty expertise create meaningful friction against full automation.
Adoption barriersclaude-sonnet-53/5No formal licensing requires a human to grade, but academic integrity concerns, institutional policy, and accreditation expectations create moderate friction against full AI grading.
Cost vs. human wageclaude-haiku-4-5-202510012/5Current AI systems require significant human review and fine-tuning per course/rubric, and the labor to verify and adjust AI-generated grades often approaches the cost of human grading. At scale in large institutions, the cost ratio may improve, but for typical postsecondary contexts it remains unfavorable.
Cost vs. human wageclaude-sonnet-53/5AI tools can cheaply pre-screen or draft feedback, but instructor review and final grading still consume significant time, keeping costs roughly comparable rather than dramatically cheaper.
Technical feasibility todayclaude-haiku-4-5-202510013/5Tools like plagiarism detectors and basic writing-assessment systems are deployed in education, and LLMs can generate rubric-aligned feedback, but they produce frequent errors in assessing argument validity and disciplinary reasoning. Adoption remains inconsistent and typically requires substantial human oversight.
Technical feasibility todayclaude-sonnet-52/5Some AI grading assistants exist for structured assignments (essays, short answer), but they are not widely deployed for nuanced postsecondary political science grading and require heavy instructor oversight.

Prepare and deliver lectures to undergraduate or graduate students on topics such as classical political thought, international relations, and democracy and citizenship.

29

CI 25–34 · exposure 25 · augmentation 75 · importance 4.6/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Postsecondary education remains labor-intensive and conservative in adopting AI for core instruction. Most AI adoption in higher education focuses on grading, tutoring, and administrative tasks rather than replacing lecture delivery. Production displacement of instructors for core teaching is minimal despite experimental interest.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI slowly for core teaching tasks; pilots (e.g., AI tutors, content generators) exist but classroom lecture delivery by AI remains rare and experimental.
Augmentation potentialclaude-haiku-4-5-202510014/5AI significantly augments lecture preparation: generating drafts, summarizing research, creating visual aids, and adapting content for accessibility. An instructor using AI tools can prepare richer, more diverse lectures faster and field follow-up questions with instant evidence synthesis, substantially raising productivity while the expert remains in the loop.
Augmentation potentialclaude-sonnet-54/5AI substantially helps with drafting lecture outlines, summarizing readings, generating discussion questions, and creating supplementary materials, meaningfully boosting preparation efficiency.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can generate lecture content and outlines, delivering effective lectures requires real-time interaction, responsiveness to student confusion, and the credibility of a subject-matter expert. Current systems cannot replicate the live pedagogical judgment, charisma, and adaptive teaching that characterize quality university instruction, leaving only content preparation as potentially automatable—less than 50% of the full task.
Task automatabilityclaude-sonnet-52/5AI can help draft lecture content and slides, but live delivery, classroom interaction, adapting to student questions, and personal pedagogical presence remain largely human tasks not replaceable end-to-end today.
Adoption barriersclaude-haiku-4-5-202510014/5Significant barriers exist: universities employ instructors under contracts; accreditation and institutional governance require faculty to teach; students expect expert instruction; and legal/credential requirements tie course delivery to faculty credentials. Institutions are unlikely to replace instructors with AI-only delivery without regulatory and normative shifts.
Adoption barriersclaude-sonnet-53/5No licensing mandate requires a human specifically, but institutional accreditation, tenure structures, and student expectations of live faculty create moderate friction against full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5The loaded cost of a postsecondary instructor includes salary, benefits, and institutional overhead typically $60–120k annually. AI content generation might save 5–10 hours per week of prep, but the instructor remains the essential cost. Substituting the instructor entirely would require overcoming barriers (below) that make the cost comparison moot; partial augmentation does not move the needle significantly.
Cost vs. human wageclaude-sonnet-53/5AI-assisted content drafting is cheap, but full lecture delivery still requires a paid instructor, so overall cost savings are modest rather than transformative.
Technical feasibility todayclaude-haiku-4-5-202510012/5AI can draft lecture notes and generate slides, and some institutions experiment with AI-assisted content; however, no deployed product reliably delivers a complete, coherent lecture on specialized political science topics with instructor presence and student engagement. Content generation exists but lacks the integration, credibility, and interactive feedback loop needed for production classroom use.
Technical feasibility todayclaude-sonnet-52/5No deployed product autonomously delivers full postsecondary lectures in production; AI tools mainly assist with content generation and study aids, not live instruction at scale.

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

29

CI 25–34 · exposure 25 · augmentation 75 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Higher education has adopted AI slowly for advising; most institutions use humans for substantive academic guidance and reserve AI for information distribution. Adoption remains in the pilot and supplementary phase rather than production replacement.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI advising tools cautiously and unevenly; while some chatbots assist with FAQs, deep integration into academic/career advising remains limited and slow-moving.
Augmentation potentialclaude-haiku-4-5-202510014/5AI can significantly assist advisors by summarizing student records, flagging prerequisite conflicts, retrieving program information, and suggesting career pathways, freeing advisors to focus on counseling and judgment. This augmentation raises advisor productivity while keeping the human in the central role.
Augmentation potentialclaude-sonnet-54/5AI can meaningfully assist faculty by drafting career resources, summarizing degree requirements, and generating talking points, freeing time for the more personalized aspects of advising.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can provide generic information about curricula and career pathways, advising requires understanding individual student circumstances, aspirations, and constraints. Current systems lack the nuanced judgment and personalization needed to replace substantive academic advising at equal quality.
Task automatabilityclaude-sonnet-52/5Advising requires personalized judgment about a specific student's history, goals, and institutional context, which current AI cannot fully replicate end-to-end despite being able to answer general curriculum questions.riz.the interpersonal mentoring component resists full automation.gets 2.the task blends information retrieval (automatable) with relationship-based counseling (not automatable).sets 2 overall.thus 2.only partial time savings possible.rated 2.
Adoption barriersclaude-haiku-4-5-202510014/5Educational advising often falls under institutional policies requiring credentialed staff oversight; some jurisdictions regulate career counseling. Institutional liability concerns and student expectations for human mentorship create strong organizational friction against full automation.
Adoption barriersclaude-sonnet-53/5No formal licensure requires a human advisor, but institutional norms, FERPA-related privacy concerns, and expectations of faculty mentorship create meaningful friction against full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5Integration costs, oversight requirements, and the need for human review of AI recommendations mean the all-in cost per advising interaction remains comparable to or higher than a staff advisor providing the same depth of counsel.
Cost vs. human wageclaude-sonnet-53/5AI tools can cheaply handle FAQ-style curriculum questions, but the professor's time spent on advising is only partially reducible, so overall cost savings are moderate rather than dramatic.
Technical feasibility todayclaude-haiku-4-5-202510012/5Chatbots and career-matching tools exist and can handle routine queries, but they struggle with complex advising scenarios and typically require human oversight. No production system reliably replaces human academic advisors in postsecondary contexts.
Technical feasibility todayclaude-sonnet-52/5Chatbots and advising software exist for scheduling and basic degree-requirement lookups, but no deployed product reliably conducts substantive career/curriculum advising for postsecondary students at scale without human oversight.

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

28

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

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Higher education remains a laggard sector for operational automation. While some institutions pilot AI content tools, actual curriculum revision and evaluation remain human-centered processes with slow institutional adoption of AI-driven changes.
Sector adoption velocityclaude-sonnet-52/5Higher education is generally slower to adopt AI for core academic functions like curriculum design compared to fast-moving sectors like finance or tech.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can meaningfully assist by drafting syllabus sections, suggesting learning objectives aligned with standards, or analyzing student feedback patterns, allowing faculty to focus on higher-level pedagogical decisions and refinement of instructional strategy.
Augmentation potentialclaude-sonnet-54/5AI tools can meaningfully assist in brainstorming course content, generating materials, summarizing readings, and suggesting instructional methods, significantly boosting instructor productivity while they retain final judgment.
Task automatabilityclaude-haiku-4-5-202510012/5Curricula planning and evaluation require domain expertise, pedagogical judgment, and understanding of student outcomes that AI cannot reliably replicate end-to-end. While AI can generate draft syllabi or suggest content, the core task of evaluating educational effectiveness and revising methods demands human expertise.
Task automatabilityclaude-sonnet-52/5AI can help draft or suggest curriculum elements, but the holistic planning, alignment with institutional goals, and pedagogical judgment required cannot be fully automated at equal quality today.dominated by human judgment.
Adoption barriersclaude-haiku-4-5-202510014/5Curriculum design is typically a faculty prerogative governed by institutional accreditation standards, departmental governance, and academic freedom expectations. University policies and professional norms create strong barriers against outsourcing core instructional design decisions to automated systems.
Adoption barriersclaude-sonnet-53/5No licensing requirement mandates a human to design curricula, but accreditation standards, departmental review processes, and academic freedom norms create moderate institutional friction.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI content generation and drafting tools have modest marginal costs, but the oversight required to ensure pedagogically sound curricula, combined with a faculty member's specialized knowledge, means the human remains the primary cost driver for this task.
Cost vs. human wageclaude-sonnet-52/5While AI can cheaply generate drafts, the human oversight, subject-matter expertise, and institutional review needed keep the all-in cost closer to comparable rather than dramatically cheaper.
Technical feasibility todayclaude-haiku-4-5-202510012/5AI tools like GPT can draft course content or suggest organizational structures, but no deployed product reliably evaluates curricula quality, assesses student learning outcomes, or makes principled revisions to instruction methods at production scale in universities.
Technical feasibility todayclaude-sonnet-52/5Some AI tools (e.g., course-design assistants) exist but are not widely deployed as reliable, production-grade systems for full curriculum planning and revision in postsecondary settings.

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

28

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

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Academia is a slow-digitizing sector with strong norms around human scholarship; while some researchers experiment with AI writing assistants, production-scale adoption of AI as research agent is minimal and actively resisted in many institutions.
Sector adoption velocityclaude-sonnet-52/5Higher education and academic research are relatively slow to adopt AI for core scholarly production, though AI writing/research assistants are increasingly used informally.
Augmentation potentialclaude-haiku-4-5-202510013/5AI tools meaningfully assist researchers with literature review synthesis, writing drafts, and editing, but the human scholar remains the decision-maker on research direction, novelty, and interpretation—a useful productivity boost on peripheral tasks.
Augmentation potentialclaude-sonnet-54/5AI significantly aids literature reviews, drafting, data analysis, and editing, meaningfully boosting researcher productivity while the scholar retains intellectual ownership and judgment.
Task automatabilityclaude-haiku-4-5-202510012/5Research ideation and publication writing can be partially aided by AI tools (literature synthesis, drafting), but the core creative work—formulating original research questions, designing novel studies, and producing publishable insights—requires sustained human judgment and domain expertise that current systems cannot reliably automate end-to-end.
Task automatabilityclaude-sonnet-52/5AI can assist with literature review, drafting, and data analysis, but original research design, novel theoretical contribution, and scholarly judgment remain human-led, so full end-to-end automation at equal quality is not achievable today.
Adoption barriersclaude-haiku-4-5-202510014/5Academic publication requires human authorship, peer verification, and institutional accountability; journals and funding bodies expect human researchers to be responsible for originality and accuracy, creating legal and professional barriers to full automation.
Adoption barriersclaude-sonnet-53/5There's no licensing requirement, but academic norms, peer review, authorship accountability, and institutional expectations of original scholarship create meaningful friction against full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI assistance (GPT subscriptions, writing tools) costs relatively little, but the human researcher remains the primary cost driver; the system does not replace the researcher's labor significantly enough to achieve cost parity, let alone order-of-magnitude savings.
Cost vs. human wageclaude-sonnet-52/5AI can cheaply handle some sub-tasks like summarizing sources, but the overall research process still requires substantial expert time for design, analysis, and validation, keeping costs comparable to or only modestly below human-only effort.
Technical feasibility todayclaude-haiku-4-5-202510012/5While AI can assist with literature review, writing, and formatting, no deployed product reliably conducts original empirical or theoretical research autonomously in political science at publication standard; tools exist for narrow sub-tasks but not the full workflow.
Technical feasibility todayclaude-sonnet-52/5Tools exist for literature search, summarization, and drafting assistance, but no deployed product reliably conducts original political science research and produces publishable findings independently.

Participate in student recruitment, registration, and placement activities.

28

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

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Higher education is a laggard sector in automation adoption; while some universities use CRM and registration software, meaningful AI-driven recruitment and placement remain pilots or absent. Most departments still rely on faculty and staff doing this work directly.
Sector adoption velocityclaude-sonnet-52/5Higher education administrative processes are adopting AI slowly for outreach and registration support, but faculty-level recruitment participation remains largely traditional and in-person.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist faculty and recruitment staff by flagging candidate records, summarizing student profiles, or suggesting matching opportunities with programs or employers, raising coordinator productivity in managing large candidate pools without eliminating the human judgment and relationship-building core of the task.
Augmentation potentialclaude-sonnet-53/5AI tools can help draft recruitment materials, manage registration data, and analyze placement outcomes, moderately aiding the faculty member's efficiency.
Task automatabilityclaude-haiku-4-5-202510012/5Student recruitment requires relationship-building, persuasion, and contextual judgment about fit; registration is partly automatable (form processing, data entry) but placement involves career counseling and judgment. Current AI cannot reliably handle the full end-to-end task of recruitment and meaningful placement at 50% time savings.
Task automatabilityclaude-sonnet-52/5This involves interpersonal advising, event participation, and institutional relationship-building that AI cannot execute end-to-end; only sub-components like drafting communications can be offloaded.
Adoption barriersclaude-haiku-4-5-202510014/5Academic institutions have strong cultural and organizational preferences for human recruiters and advisors to build student relationships and trust; there are also reputational and liability concerns around algorithmic placement that may disadvantage students. Faculty leadership in recruitment also creates institutional inertia.
Adoption barriersclaude-sonnet-53/5No strict licensing barrier, but institutional norms, personal rapport with prospective students, and accreditation/administrative expectations favor human faculty involvement.
Cost vs. human wageclaude-haiku-4-5-202510012/5Existing recruitment and placement software requires human oversight, administrative setup, and integration costs that approach or match the cost of a human coordinator or recruiter for mid-volume work. The human value in relationship-building and outcome quality justifies their wage relative to AI cost.
Cost vs. human wageclaude-sonnet-52/5AI can cheaply handle scheduling or FAQ-style outreach, but the faculty member's personal involvement (interviews, advising, relationship building) still requires human time, limiting cost savings.
Technical feasibility todayclaude-haiku-4-5-202510012/5Basic registration portals exist and CRM systems handle some recruitment workflows, but no deployed AI reliably performs recruitment (which requires nuanced judgment and interpersonal credibility) or placement (which requires understanding individual strengths, career goals, and opportunities). These remain largely human-driven in practice.
Technical feasibility todayclaude-sonnet-52/5Some CRM and chatbot tools assist with recruitment outreach and registration logistics, but no product autonomously performs faculty-level recruitment, registration, and placement participation.

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

27

CI 16–38 · exposure 17 · augmentation 75 · importance 4.6/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Academic institutions adopt tools for literature discovery slowly; tenure norms and peer review reward direct engagement with conferences and colleagues, limiting pressure for full automation of this task.
Sector adoption velocityclaude-sonnet-53/5Academia has moderate AI tool adoption for literature review and summarization, though conference attendance and networking remain human-centric and slow to change.
Augmentation potentialclaude-haiku-4-5-202510014/5AI significantly augments this task by filtering and summarizing literature feeds, surfacing relevant papers, and enabling faster scanning of conference programs—substantially raising the scholar's productivity while they remain the decision-maker on engagement.
Augmentation potentialclaude-sonnet-54/5AI literature summarization, alerting services, and search tools substantially speed up how a professor tracks new developments, even though human engagement remains central.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires sustained professional judgment, filtering relevant developments, and building domain expertise through active engagement. Current AI cannot autonomously decide what is important to a scholar's research agenda or participate meaningfully in conference interactions.
Task automatabilityclaude-sonnet-52/5AI can help surface and summarize literature, but the task fundamentally involves ongoing personal engagement, judgment about relevance, and relationship-building at conferences that isn't reducible to an automatable output.'
Adoption barriersclaude-haiku-4-5-202510014/5Strong barriers exist: professional development is often institutionally mandated, tenure and career advancement explicitly require demonstrated engagement with the field, and the social/networking dimensions are deeply embedded in academic culture and career expectations.
Adoption barriersclaude-sonnet-52/5No licensing barrier, but norms of scholarly engagement, tenure/promotion expectations, and professional networking requirements create some friction against full substitution.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI tools for literature monitoring (arxiv alerts, semantic search) have low direct costs but require human oversight and do not eliminate the time and travel costs of conferences or the value of peer discussion, making substitution economically incomplete.
Cost vs. human wageclaude-sonnet-52/5AI tools for literature scanning are cheap, but since the task cannot be fully replaced, the relevant comparison is minimal cost savings against the human's ongoing professional development time.
Technical feasibility todayclaude-haiku-4-5-202510012/5While AI can summarize academic papers and flag recent publications, no deployed system reliably curates development streams tailored to an individual scholar's evolving interests or substitutes for the judgment and networking inherent in colleague conversations and conference attendance.
Technical feasibility todayclaude-sonnet-52/5AI research assistants and summarization tools exist and are used to track literature, but no deployed product substitutes for the full scope of staying current including colleague discussion and conference participation.

Write grant proposals to procure external research funding.

26

CI 23–30 · exposure 25 · augmentation 63 · importance 2.9/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Academic institutions remain highly conservative on automation; faculty retain direct control over grant writing as a core professional function. Adoption of AI for full proposal generation is minimal; most use cases remain limited to brainstorming or minor drafting support.
Sector adoption velocityclaude-sonnet-52/5Academia adopts AI writing tools unevenly and cautiously, especially for high-stakes grant applications where originality and integrity concerns slow uptake compared to industries like finance or tech.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can meaningfully assist by generating literature review summaries, outlining proposal structure, suggesting budgetary frameworks, or drafting sections that faculty then revise. This augmentation improves efficiency for experienced grant writers but does not transform the core intellectual work of positioning research impact.
Augmentation potentialclaude-sonnet-54/5AI is genuinely useful for brainstorming aims, drafting boilerplate sections, editing prose, and summarizing literature, meaningfully speeding up the proposal-writing process while the researcher retains control of strategy and content.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can draft sections of grant proposals (background, methodology scaffolds), the task requires deep domain expertise, original research positioning, institutional knowledge, and strategic judgment about funding landscapes that current systems cannot reliably generate end-to-end. Significant expert oversight and rewriting remain necessary.
Task automatabilityclaude-sonnet-52/5AI can draft sections and boilerplate but grant proposals require original research framing, strategic positioning, and deep subject expertise that current AI cannot reliably generate end-to-end at equal quality.
Adoption barriersclaude-haiku-4-5-202510014/5Strong barriers exist: grant agencies often require human accountability and PI signatures; institutional review processes and faculty responsibility for accuracy create liability exposure; and funding bodies may explicitly prohibit or penalize AI-generated content in proposals, creating regulatory friction.
Adoption barriersclaude-sonnet-53/5No licensing requirement, but funding agencies expect PI authorship, institutional sign-off, and personal accountability for proposal content, creating moderate organizational and reputational barriers to full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5An academic researcher's loaded cost for grant writing includes salary time; AI inference is cheap but integration, prompt engineering, and expert review/revision labor substantially offset the savings, making total cost-per-successful-proposal comparable to or higher than human-only workflows.
Cost vs. human wageclaude-sonnet-52/5AI can cut some drafting time cheaply, but the human oversight, subject-matter expertise, and revision cycles needed still dominate cost, so overall savings versus faculty time are modest.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed product reliably writes competitive grant proposals independently; AI tools like ChatGPT can assist with drafting but produce generic outputs requiring extensive expert revision. Institutions have not adopted AI as a primary grant-writing system due to quality and liability concerns.
Technical feasibility todayclaude-sonnet-52/5AI writing tools are used by some academics to draft proposal sections, but no product reliably produces submission-ready, fundable grant proposals without extensive human rewriting and expert review.

Provide professional consulting services to government or industry.

23

CI 16–30 · exposure 17 · augmentation 63 · importance 2.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Government and regulated industries adopt AI slowly for strategic advisory roles due to liability and governance concerns; AI is used for research support in consulting firms but not as the primary consultant delivering client advice.
Sector adoption velocityclaude-sonnet-52/5Academic consulting is a small, relationship-driven niche within a sector (higher education) that adopts AI more slowly than finance or tech, with pilots for research support but little displacement of the consulting role itself.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can meaningfully assist human consultants by synthesizing research, drafting policy briefs, and analyzing data, improving the speed and breadth of analysis, but the consultant retains decision-making authority and client relationships.
Augmentation potentialclaude-sonnet-54/5AI can significantly speed up literature review, policy analysis drafting, data summarization, and scenario modeling that feed into the consulting product, meaningfully boosting the professor-consultant's productivity.
Task automatabilityclaude-haiku-4-5-202510011/5Consulting on political science issues requires deep contextual judgment, stakeholder relationship management, and persuasion—tasks that demand human expertise, credibility, and accountability. Current AI systems cannot end-to-end replace a consultant's role in advising government or industry decision-makers.
Task automatabilityclaude-sonnet-52/5Consulting requires synthesizing nuanced political context, stakeholder relationships, and judgment calls that current AI cannot reliably perform end-to-end, though it can assist with research and drafting components.
Adoption barriersclaude-haiku-4-5-202510014/5Consulting to government and industry typically requires professional licensing, security clearances, legal liability for advice, and contractual accountability that only a named human expert can satisfy. Organizations and regulators expect human consultants to sign off on advice.
Adoption barriersclaude-sonnet-53/5No formal licensing typically required, but government and industry clients expect accountable human experts with reputational standing and the ability to be questioned, deposed, or held liable for advice given.
Cost vs. human wageclaude-haiku-4-5-202510012/5Consulting commands high professional fees ($200–$500+ per hour) due to expert judgment and accountability; AI analysis tools cost far less per unit but cannot replace the full consulting engagement, making direct cost comparison difficult and AI potentially more expensive when integration and human oversight are included.
Cost vs. human wageclaude-sonnet-52/5While AI research assistance is cheap, the actual valuable output—trusted advice, relationship management, and accountability—still requires expensive human expert time, keeping overall cost comparable to or only modestly below human-only delivery.
Technical feasibility todayclaude-haiku-4-5-202510012/5While AI can assist with research synthesis and policy analysis, no deployed product reliably performs end-to-end consulting services where the AI is the primary advisor to government or industry clients. Products exist for analytical support but not for independent consulting delivery.
Technical feasibility todayclaude-sonnet-52/5No deployed product independently delivers professional political consulting services; AI tools are used as research aids by human consultants rather than replacing the consulting relationship itself.

Initiate, facilitate, and moderate classroom discussions.

13

CI 0–25 · exposure 13 · augmentation 50 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Higher education adoption of AI in classroom teaching remains experimental and deeply contested; departments have not begun systematically replacing instructor-led discussion with automated moderation, and faculty governance structures actively resist such changes.
Sector adoption velocityclaude-sonnet-52/5Higher education has been slow to adopt AI for live instructional delivery, with most current use concentrated in administrative support, grading assistance, and content creation rather than replacing in-class facilitation.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist instructors by suggesting discussion prompts, analyzing student writing for patterns, or generating pre-class summaries of readings—genuinely useful preparation and support tools—but the live facilitation and real-time judgment remain firmly with the human instructor.
Augmentation potentialclaude-sonnet-53/5AI can help instructors prepare discussion prompts, background materials, and even suggest follow-up questions or summarize discussion themes afterward, offering moderate support without taking over the live moderation role.
Task automatabilityclaude-haiku-4-5-202510011/5Classroom discussion facilitation requires real-time responsiveness to student contributions, dynamic judgment about when to probe deeper or redirect, and handling of unpredictable human interactions—capacities that current AI cannot reliably execute in a live classroom setting. While AI can generate discussion prompts or summarize arguments, orchestrating genuine pedagogical dialogue with authentic student engagement remains beyond current automation.
Task automatabilityclaude-sonnet-52/5AI chatbots can pose discussion questions or summarize responses, but live facilitation of a dynamic in-person classroom discussion requires real-time social judgment, reading the room, and adaptive follow-up that current systems cannot reliably perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Postsecondary institutions have accreditation standards, learning outcomes tied to instructor-led engagement, and student expectations (often contractually backed) that a qualified faculty member will teach and facilitate. Liability for educational outcomes and institutional reputation create strong legal and organizational barriers to substituting AI for human instruction.
Adoption barriersclaude-sonnet-54/5Teaching at accredited postsecondary institutions typically requires a credentialed instructor of record to lead and be accountable for classroom instruction, and students expect human interaction, creating strong institutional and accreditation-based barriers.
Cost vs. human wageclaude-haiku-4-5-202510011/5Even if AI could moderate discussions, the overhead of setup, oversight, and liability review would likely exceed the cost of a trained instructor, whose salary is already sunk for other teaching duties.
Cost vs. human wageclaude-sonnet-52/5Since AI cannot yet substitute for the live facilitation itself, the relevant comparison is minimal—any AI tool would need to be paired with a human instructor, so cost savings on this specific task are negligible.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed product reliably moderates live classroom discussions; experimental AI chatbots can simulate discussion but do not authentically facilitate human-to-human peer learning. Some systems can generate discussion questions or summarize written exchanges, but these fall short of the real-time facilitation and social judgment required.
Technical feasibility todayclaude-sonnet-51/5There is no deployed product that autonomously moderates live postsecondary classroom discussions; existing AI education tools support content generation or asynchronous forums, not real-time in-person facilitation.

Maintain regularly scheduled office hours to advise and assist students.

10

CI 4–16 · exposure 5 · augmentation 50 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Despite decades of educational technology development, universities have not adopted AI to replace faculty office hours; advising remains a human-delivered service with strong cultural and contractual entrenchment in academia.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools slowly for direct student advising relationships, though some institutions pilot chatbots for basic administrative questions.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist faculty by drafting advising notes, retrieving degree requirements, or suggesting intervention strategies, but the core interaction remains human-led; modest productivity gains are possible without transforming the task.
Augmentation potentialclaude-sonnet-53/5AI can help prepare materials, draft answers to common questions, or schedule/organize office hours, but the core advising interaction remains human-led.
Task automatabilityclaude-haiku-4-5-202510011/5Advising and assisting students requires nuanced understanding of individual circumstances, personalized guidance, and relational trust that current AI systems cannot reliably replicate in real-time conversation at scale. While AI can draft responses or provide information, it cannot replace the human judgment and institutional authority needed for meaningful academic advising.
Task automatabilityclaude-sonnet-51/5This task requires synchronous, in-person or live human presence for personalized mentorship and relationship-building, which cannot be end-to-end automated by current AI systems.
Adoption barriersclaude-haiku-4-5-202510015/5Academic advising is legally and institutionally a faculty responsibility; accreditation and student-support regulations require human contact and sign-off by licensed faculty. Faculty contracts explicitly mandate office hours, and institutional governance makes unilateral automation infeasible.
Adoption barriersclaude-sonnet-54/5Institutional norms, accreditation expectations, and student advising relationships create strong organizational and professional expectations that a human faculty member personally hold office hours.
Cost vs. human wageclaude-haiku-4-5-202510012/5Deploying an AI advising system with adequate oversight, integration into student information systems, and human review would cost more than the modest salary-equivalent of scheduled office hours, especially when accounting for liability and error recovery.
Cost vs. human wageclaude-sonnet-52/5While AI chat tools are cheap, the task specifically requires the human's scheduled availability, so cost comparison is largely moot since the human presence itself is the deliverable.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed product reliably performs the full advising role; AI chatbots exist but are narrow, lack context about individual student records and institutional policies, and fail to build the trust necessary for genuine student advising relationships. Institutions would not substitute AI for required office hours.
Technical feasibility todayclaude-sonnet-51/5No deployed product substitutes for a professor's live office hours as an institutional and relational requirement; chatbots exist for FAQs but not for this specific accountable role.

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

8

CI 0–16 · exposure 8 · augmentation 50 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Postsecondary education remains highly conservative in delegating supervisor roles; institutional structures, accreditation bodies, and faculty governance create strong resistance to automation of supervisory authority.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools slowly for interpersonal mentoring and supervisory roles, though it's faster for administrative and grading tasks.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist faculty supervisors by automating administrative tracking, summarizing student progress, flagging outliers, and drafting feedback, thereby raising supervisor productivity; however, the human must remain the decision-maker and final evaluator.
Augmentation potentialclaude-sonnet-53/5AI can help draft feedback, track student progress, suggest research resources, or generate rubrics, aiding but not replacing the supervisory relationship.
Task automatabilityclaude-haiku-4-5-202510012/5AI cannot meaningfully supervise in the pedagogical sense—monitoring progress, providing feedback, mentoring judgment calls, and ensuring accountability require human judgment and relationship continuity. While AI could assist with scheduling and basic tracking, actual supervision requires presence and discretionary correction.
Task automatabilityclaude-sonnet-51/5Supervision requires ongoing relational judgment, mentoring, feedback on student growth, and institutional accountability that AI cannot perform end-to-end today.
Adoption barriersclaude-haiku-4-5-202510015/5Academic supervision involves legal liability for student outcomes, accreditation requirements, fiduciary duty to students, and institutional governance standards that typically mandate faculty oversight. Regulatory frameworks and institutional policy explicitly require human faculty sign-off.
Adoption barriersclaude-sonnet-54/5Institutional policy, accreditation, and mentorship/supervisory roles typically require a qualified faculty member to be formally responsible, creating strong organizational and quasi-regulatory barriers.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of deploying an AI system to supervise at acceptable quality, including human oversight to verify AI feedback and decisions, would likely exceed the cost of a faculty member performing the task directly.
Cost vs. human wageclaude-sonnet-51/5There is no AI substitute product with a comparable cost basis since the task isn't performed by AI at all.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs academic supervision end-to-end. Products exist for administrative tasks (scheduling, grade tracking) but not for the relational, evaluative oversight that defines supervision of teaching, internship, and research work.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs supervisory mentoring of students' teaching, internships, or research; this remains a human relational responsibility.

Collaborate with colleagues to address teaching and research issues.

6

CI 0–13 · exposure 5 · augmentation 50 · importance 3.7/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Higher education, especially faculty research collaboration, remains relatively low in digitization and AI adoption for core academic processes. Institutions are laggards in automating collegial deliberation due to cultural values around academic independence and shared governance.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools for research support and content generation, but collaborative governance and peer discussion processes remain slow to change and largely human-driven.
Augmentation potentialclaude-haiku-4-5-202510013/5AI tools can assist by synthesizing literature on teaching practices, drafting proposals, or organizing discussion materials, but the core collaborative work—deliberating, persuading, and reaching faculty consensus—remains primarily human-driven and benefits only partially from AI support.
Augmentation potentialclaude-sonnet-53/5AI can help by summarizing research, drafting shared materials, or organizing discussion points, providing moderate support to the collaborative process without replacing the interpersonal exchange.
Task automatabilityclaude-haiku-4-5-202510011/5Collaboration to address teaching and research issues requires nuanced interpersonal negotiation, creative problem-solving in context-specific academic environments, and building consensus among faculty peers. Current AI cannot meaningfully replace this inherently human coordination activity.
Task automatabilityclaude-sonnet-51/5This task is fundamentally interpersonal collaboration among faculty peers involving relationship-building, negotiation, and shared decision-making that AI cannot perform end-to-end.4
Adoption barriersclaude-haiku-4-5-202510015/5Academic governance and collegial decision-making carry strong institutional and cultural expectations that human judgment and presence are essential; faculty autonomy and shared governance norms create hard barriers to substituting AI for human collaboration.
Adoption barriersclaude-sonnet-54/5Academic collegiality, governance structures, and tenure/peer-review norms require human faculty to directly engage with one another, creating strong organizational and cultural barriers to substitution.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of setting up, integrating, and overseeing an AI system to manage academic collaboration would far exceed the marginal expense of colleague time spent in actual collaborative meetings, making automation economically unfeasible.
Cost vs. human wageclaude-sonnet-51/5There is no AI product performing this task, so no meaningful cost comparison to a human collaborator exists; the human relationship itself is the deliverable.
Technical feasibility todayclaude-haiku-4-5-202510012/5While AI can draft meeting agendas or summarize discussion points, no deployed product reliably performs the end-to-end collaborative deliberation and consensus-building that defines this task. Existing tools are at best assistive rather than autonomous performers.
Technical feasibility todayclaude-sonnet-51/5No deployed product substitutes for collegial collaboration on teaching/research issues; at best AI tools support drafting or scheduling around such interactions.

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

4

CI 0–7 · exposure 5 · augmentation 38 · importance 3.3/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Academic institutions are among the slowest sectors to adopt automation of administrative processes, particularly those involving shared governance and collective decision-making, which are core to faculty culture and union agreements.
Sector adoption velocityclaude-sonnet-52/5Higher education is a moderate-to-slow adopter of AI for governance functions; committee work remains almost entirely human-driven with minimal AI penetration.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could modestly assist with preparing background materials or analyzing policy documents, but committee work is inherently relational and deliberative, limiting the scope for meaningful augmentation of the core human function.
Augmentation potentialclaude-sonnet-53/5AI can help summarize documents, draft policy language, or prepare briefing materials for committee members, providing moderate assistance to the surrounding administrative work.
Task automatabilityclaude-haiku-4-5-202510011/5Committee work fundamentally requires human judgment on institutional policies, stakeholder negotiation, and contextual understanding of departmental dynamics. No AI system can currently replace the deliberative and decision-making role that committee members play end-to-end.
Task automatabilityclaude-sonnet-51/5This task requires human judgment, institutional politics, relationship navigation, and consensus-building among colleagues that AI cannot perform end-to-end today.'
Adoption barriersclaude-haiku-4-5-202510015/5Academic committees are legally and institutionally mandated to include human faculty participation; governance structures require direct human deliberation and sign-off. Institutional policy explicitly requires human accountability and cannot delegate these responsibilities to automated systems.
Adoption barriersclaude-sonnet-55/5Committee service is an institutional governance function requiring a human faculty member's presence, voting rights, and accountability, often specified by university bylaws or accreditation requirements.
Cost vs. human wageclaude-haiku-4-5-202510011/5Committee service is a high-judgment, relatively low-volume task performed by salaried academics as part of their role. The cost of oversight and integration to automate even partial components would exceed the marginal savings from reducing human participation.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute for committee membership itself, so cost comparison favors the human by default; AI cannot produce the deliverable.
Technical feasibility todayclaude-haiku-4-5-202510012/5AI can assist with administrative tasks like agenda preparation or document summarization, but no deployed product reliably performs the core committee functions of policy deliberation, voting, and institutional decision-making. Current systems lack the autonomy and accountability structures required.
Technical feasibility todayclaude-sonnet-51/5No deployed product substitutes for a human serving on and participating in committee deliberations and governance decisions.

Act as advisers to student organizations.

4

CI 0–7 · exposure 0 · augmentation 25 · importance 3.1/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Higher education is among the slowest sectors to adopt automation for roles involving student interaction and pastoral care. There is no measurable adoption of AI systems replacing or augmenting faculty advisers in student organizations in production environments.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools for teaching aids and admin tasks, but advisory/mentorship roles for organizations see minimal AI integration currently.
Augmentation potentialclaude-haiku-4-5-202510012/5AI might assist with routine administrative tasks (scheduling, tracking meeting notes, drafting communications), but the core advisory function—mentoring, conflict resolution, strategy guidance—sees minimal augmentation value from current systems. The human remains essential and irreplaceable.
Augmentation potentialclaude-sonnet-52/5AI can help with logistics like scheduling, drafting communications, or budgeting suggestions, but offers limited support for the interpersonal mentoring core of the task.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires sustained mentorship, judgment about individual student needs and organizational dynamics, and the ability to navigate sensitive interpersonal and institutional issues—capabilities that current AI cannot perform end-to-end. There is no meaningful part of advising student organizations that AI can automate to achieve 50% time savings at equal quality.
Task automatabilityclaude-sonnet-51/5Advising student organizations requires ongoing personal mentorship, relationship-building, and situational judgment that AI cannot perform end-to-end today.
Adoption barriersclaude-haiku-4-5-202510015/5Student advising is fundamentally a human-contact requirement embedded in academic governance and institutional mission; many institutions legally or contractually mandate faculty oversight of student organizations. The role carries reputational and liability implications that require human judgment and accountability.
Adoption barriersclaude-sonnet-54/5Institutional policy typically requires a designated faculty/staff advisor for liability, oversight, and accreditation purposes, creating a strong organizational barrier to substitution.
Cost vs. human wageclaude-haiku-4-5-202510011/5AI cannot substitute for faculty advisor time at present; any oversight and integration of an AI system would add cost without meaningful task displacement. The cost would exceed the loaded wage of the faculty member already performing the role.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI alternative performing this role, so cost comparison favors the human doing the actual advising and institutional presence.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs the advisory role to student organizations; the task demands contextual understanding of student development, institutional culture, and one-on-one relationship building that exceeds current AI capabilities. This remains firmly in the research/concept stage.
Technical feasibility todayclaude-sonnet-51/5No deployed product substitutes for a faculty advisor role in student organizations; this remains an inherently human, relational function.

Participate in campus and community events.

3

CI 0–5 · exposure 0 · augmentation 25 · importance 2.8/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5There is no meaningful adoption of AI for this task because the task is intrinsically tied to human presence and community engagement, which are core institutional values in academic settings.
Sector adoption velocityclaude-sonnet-51/5Higher education is generally slow to adopt AI for interpersonal and community-facing responsibilities like this.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could offer minor assistance in planning or scheduling events (calendar management, reminder systems), but it cannot participate in or augment the actual participation itself, which is the core of the task.
Augmentation potentialclaude-sonnet-52/5AI might help with scheduling, event summaries, or follow-up communications, but offers minimal help with the core act of participating in person.
Task automatabilityclaude-haiku-4-5-202510011/5Participating in campus and community events requires physical presence, real-time social interaction, relationship-building, and contextual judgment. Current AI systems cannot attend events or meaningfully engage with communities in person.
Task automatabilityclaude-sonnet-51/5Physical attendance, networking, and representing the institution at events requires genuine human presence and social engagement that AI cannot perform.'
Adoption barriersclaude-haiku-4-5-202510015/5Strong institutional and social barriers exist: faculty participation in campus and community events is often a tenure/evaluation criterion, requires human judgment about representation, and is inherently tied to institutional and community relationships that demand human presence.
Adoption barriersclaude-sonnet-54/5This is an inherently human, relational activity tied to institutional representation and community presence, making automation essentially infeasible regardless of technology.
Cost vs. human wageclaude-haiku-4-5-202510011/5Since AI cannot perform this task, the cost comparison is not meaningful. Any partial assistance (e.g., scheduling help) would be negligible relative to the human labor required for actual participation.
Cost vs. human wageclaude-sonnet-51/5There is no AI service replicating in-person participation, so cost comparison is not applicable; AI cannot deliver this output at all.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product can physically attend or participate in campus and community events. The task is fundamentally embodied and social in ways that fall outside current AI capabilities.
Technical feasibility todayclaude-sonnet-51/5No product exists that can substitute for a person's physical and social participation in campus/community events.

Perform administrative duties, such as serving as department head.

1

CI 0–3 · exposure 0 · augmentation 38 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Academic institutions remain highly traditional in governance structures and have shown minimal adoption of AI for administrative leadership roles, relying on established hierarchies and human accountability.
Sector adoption velocityclaude-sonnet-52/5Higher education administration adopts AI slowly for governance and leadership roles, though it may use AI for peripheral administrative tasks like scheduling or reporting.
Augmentation potentialclaude-haiku-4-5-202510012/5Current AI tools can assist with document drafting, meeting scheduling, and data analysis to support a department head, but the scope is narrow compared to the full scope of leadership duties requiring human judgment and authority.
Augmentation potentialclaude-sonnet-53/5AI can help with drafting reports, scheduling, summarizing meeting notes, and organizing budgets, meaningfully aiding a department head's administrative workload.
Task automatabilityclaude-haiku-4-5-202510011/5Administrative duties as a department head involve discretionary decision-making, personnel management, budget oversight, and institutional relationship-building that require human judgment, accountability, and authority that AI cannot exercise today.
Task automatabilityclaude-sonnet-51/5Serving as department head involves leadership, personnel decisions, budget negotiation, conflict resolution, and institutional politics that require human judgment and authority AI cannot replicate or hold end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Legal and regulatory frameworks require a human faculty member with appropriate credentials and institutional authority to serve as department head; universities cannot delegate hiring, firing, budget approval, or policy decisions to non-human agents.
Adoption barriersclaude-sonnet-55/5Department head roles require formal institutional appointment, faculty governance approval, and accountability structures that legally and organizationally require a qualified human.
Cost vs. human wageclaude-haiku-4-5-202510011/5Department head duties require a salaried faculty member with domain expertise and institutional authority; any AI assistance would be purely supplementary and could not substitute for the human cost of the position.
Cost vs. human wageclaude-sonnet-51/5There is no AI substitute performing this leadership function, so no meaningful cost comparison exists—human cost is the only viable option.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI system can autonomously serve as a department head or perform the full range of administrative oversight, resource allocation, and personnel-related decisions required of this role in a real academic institution.
Technical feasibility todayclaude-sonnet-51/5No deployed AI product performs the role of a department head; at most AI tools assist with scheduling or document drafting within such a 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.