Geography Teachers, Postsecondary
25-1064.00Median wage $97,590/yr3,330 employed (US)Rank #289 of 923 scored · top 31% by substitution
Teach courses in geography. Includes both teachers primarily engaged in teaching and those who do a combination of teaching and research.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution34
Exposure31
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
25 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
12%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%31
panel mean rating 2.2/5 → substitution pressure 31/100
Technical feasibility todayw 20%32
panel mean rating 2.3/5 → substitution pressure 32/100
Cost vs. human wagew 15%37
panel mean rating 2.5/5 → substitution pressure 37/100
Adoption barriersw 20%inverted — strong barriers lower the score42
panel mean rating 3.3/5 (barrier strength) → substitution pressure 42/100
Sector adoption velocityw 10%30
panel mean rating 2.2/5 → substitution pressure 30/100
Task breakdown (25 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Maintain student attendance records, grades, and other required records.
95CI 95–95 · exposure 100 · augmentation 75 · importance 4.2/5 · click for rater detail
Maintain student attendance records, grades, and other required records.
95| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Postsecondary institutions operate in the information/digital sector with high digitization; SIS and LMS adoption is near-universal among accredited colleges and universities, with deep, mature deployment patterns already established. |
| Sector adoption velocity | claude-sonnet-5 | 5/5 | Higher education has broadly and quickly adopted digital LMS and SIS platforms for attendance and grade management, a mature and near-universal practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | LMS dashboards and administrative dashboards augment faculty productivity by providing real-time views of attendance and grade distributions, enabling instructors to identify at-risk students and adjust teaching faster than manual record review would allow. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-enhanced gradebooks and LMS analytics substantially reduce administrative burden and error, letting instructors focus on teaching while automated systems handle record maintenance. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Maintaining attendance records, grades, and administrative records is highly structured data entry and management that modern learning management systems (Canvas, Blackboard, Google Classroom) and administrative software handle end-to-end with negligible human intervention, easily meeting the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 5/5 | Recording attendance and grades is a structured data-entry task easily handled by existing LMS/SIS automation and gradebook software with automated calculations and record-keeping. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While institutions have formal policies around record-keeping and some oversight requirements, there are no licensing or legal barriers preventing automated systems from handling these administrative tasks; systems are already in use across higher education. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Some institutional policy and grade-integrity oversight exists, but no licensing requirement mandates a human personally maintain these records; software-based recordkeeping is already standard practice. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | The marginal cost of software-managed records (already institutional infrastructure) is orders of magnitude lower than the human labor cost of manual record maintenance, grading entry, and administrative tracking. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Automated record-keeping software costs a small fraction of the faculty or administrative time it would take to maintain records manually. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Student information systems (SIS) and learning management platforms are mature, widely deployed products in virtually all postsecondary institutions that reliably automate attendance tracking, grade recording, and record-keeping at scale in production. |
| Technical feasibility today | claude-sonnet-5 | 5/5 | Learning management systems (Canvas, Blackboard, PowerSchool, etc.) already perform automated grade recording, attendance tracking, and reporting reliably at scale in production across universities. |
Prepare course materials, such as syllabi, homework assignments, and handouts.
76CI 76–76 · exposure 75 · augmentation 100 · importance 4.5/5 · click for rater detail
Prepare course materials, such as syllabi, homework assignments, and handouts.
76| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Higher education is in the pilot and early adoption phase for AI material generation; usage is growing but inconsistent across institutions. Many faculty use these tools informally, but systematic institutional deployment for course material prep remains uneven and measured, not yet mainstream practice. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Higher education has seen growing but uneven AI adoption for course prep; many instructors experiment with AI drafting tools, but institutional policies and faculty habits slow widespread standardized use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI substantially augments course design by drafting materials, generating alternatives, adapting content for accessibility, and freeing instructors to refine pedagogy rather than spend hours on formatting and boilerplate text. Instructors retain full control and judgment while their productivity on material preparation increases significantly. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI is highly effective at accelerating drafting of syllabi, assignments, and handouts, letting instructors focus on refining content, ensuring accuracy, and tailoring to student needs. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can generate syllabi, homework assignments, and handouts from course parameters and learning objectives with minimal human input. While quality control and pedagogical review remain necessary, AI can produce 50%+ of the work at acceptable quality, meeting the time-saving threshold for routine material generation. |
| Task automatability | claude-sonnet-5 | 4/5 | LLMs can draft syllabi, homework problems, and handouts from a course description or textbook outline with substantial time savings, requiring only instructor review and customization for accuracy and alignment with learning objectives. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers exist to using AI for syllabus and assignment generation; institutional policies vary but most allow it. The main friction is adoption velocity among faculty and institutional guidance on oversight, not hard legal mandates requiring human authorship. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement dictates who writes course materials, though academic norms and accreditation expectations mean faculty typically review and approve final content before use. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI inference costs for generating full course materials are negligible (pennies per course) compared to faculty hourly wages ($40–80/hour loaded) for preparing equivalent materials, yielding a cost advantage of 1–2 orders of magnitude. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Generating drafts of course materials via an LLM costs cents to a few dollars in compute versus hours of faculty time, making it dramatically cheaper even after review overhead. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (ChatGPT, Claude, Copilot) reliably generate course syllabi, assignment prompts, and handout content in production use by educators. Error rates on factual geography content are manageable with human review, and many institutions now use these tools operationally for material preparation. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | General-purpose chatbots and specialized ed-tech tools (e.g., syllabus generators, AI homework builders) are widely used by instructors today to produce first drafts of these materials reliably, though instructor editing is still standard practice. |
Compile bibliographies of specialized materials for outside reading assignments.
76CI 67–84 · exposure 70 · augmentation 100 · importance 2.9/5 · click for rater detail
Compile bibliographies of specialized materials for outside reading assignments.
76| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Higher education has begun rapidly integrating AI writing and research assistants; adoption of bibliography and citation aids is growing quickly in information-rich sectors like academia, with many institutions piloting or deploying these tools. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Higher education is adopting AI research tools steadily but unevenly, with mixed policies on AI use in course prep across institutions and disciplines. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI significantly augments faculty productivity by instantly generating candidate lists and formatted citations, allowing instructors to focus on evaluating relevance and refining reading lists rather than manual compilation work. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up literature discovery and citation formatting, letting instructors quickly assemble and refine specialized reading lists while retaining curatorial judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can readily search academic databases, identify relevant geography sources, and format bibliographies automatically using current tools. However, ensuring specialized relevance to course level and student needs may require some human review, preventing a perfect 5. |
| Task automatability | claude-sonnet-5 | 4/5 | AI can search literature, generate topically relevant bibliographies, and format citations quickly, covering most of this discrete research/compilation task with minimal human editing needed. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal barriers to adoption—no licensing requirement for the teacher, no legal mandate that a human must compile bibliographies, and institutional LMS integration of AI tools is straightforward. Academic integrity concerns are manageable through transparency. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, regulatory, or liability barriers prevent using AI to assist in compiling bibliographies; it's a low-stakes administrative/academic task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-powered bibliography compilation costs pennies per assignment via API access or free/low-cost tools, versus the faculty time (loaded hourly rate) required to manually search databases and format citations. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating a bibliography via AI tools costs a fraction of the faculty time required to manually search and curate specialized reading lists. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature AI systems (ChatGPT, research assistants, citation managers with AI integration) can generate formatted bibliographies with high reliability in production. Minor errors in citation accuracy or source verification may occur, warranting occasional review. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Tools like reference managers with AI search, Elicit, or LLM-based literature search exist and are used, but they still produce occasional inaccurate citations or miss specialized/niche sources requiring verification. |
Evaluate and grade students' class work, assignments, and papers.
69CI 51–87 · exposure 70 · augmentation 75 · importance 4.3/5 · click for rater detail
Evaluate and grade students' class work, assignments, and papers.
69| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Higher education and information-sector institutions are adopting AI grading tools rapidly, with many colleges piloting or rolling out automated grading in learning management systems. This matches professional services and information-sector adoption patterns. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adoption of AI grading tools is uneven and cautious, with many institutions still developing policies and relying on human graders/TAs for most substantive assessment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI grading tools significantly augment faculty productivity by generating detailed feedback, flagging outliers, and producing draft grades that educators refine and finalize. Teachers remain in the loop to validate and adjust, substantially raising their efficiency. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can efficiently pre-screen work, generate rubric-based feedback drafts, and flag issues, substantially speeding up an instructor's grading workflow while they retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | AI systems can perform end-to-end grading of assignments and papers with significant time savings today. Current LLMs and specialized grading tools can assess written work, compare it against rubrics, and provide detailed feedback—meeting the ≥50% time-saving threshold with quality parity to human grading on structured assignments. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft grades and feedback for structured assignments (essays, short answers) but grading nuanced geography analysis, maps, or original research still needs human judgment for full reliability at equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Adoption barriers are minimal: no licensing requirement mandates human grading, institutional oversight is light, and faculty have autonomy to delegate grading to AI tools. Some institutions prefer human contact for pedagogical reasons, but no legal or regulatory barrier blocks automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement blocks AI grading, but academic integrity policies, institutional grading authority, and appeals processes create moderate friction requiring instructor sign-off. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI grading (inference + integration) costs pennies per assignment, whereas faculty time at loaded university wages ($60–$100+ per hour) makes human grading far more expensive. AI is at least an order of magnitude cheaper per task equivalent. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-assisted grading tools are inexpensive per assignment compared to faculty or TA hourly wages, especially for high-enrollment courses, though oversight adds some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products (Turnitin, Canvas AI, specialized LLM grading tools) already perform this task in production at scale across higher education. Some limitations remain with nuanced judgment calls and rubric edge cases, but the systems are mature and widely used. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | AI writing feedback and grading assistants exist and are used by some instructors, but postsecondary grading of open-ended coursework typically still requires instructor review before grades are finalized. |
Compile, administer, and grade examinations, or assign this work to others.
57CI 51–64 · exposure 55 · augmentation 75 · importance 4.3/5 · click for rater detail
Compile, administer, and grade examinations, or assign this work to others.
57| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Universities and colleges show moderate adoption of AI-assisted exam tools and automated grading in LMS platforms (Blackboard, Canvas), particularly for large lecture courses. However, adoption varies widely by discipline and institution size; many instructors still hand-grade, indicating slower velocity than in corporate settings. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adoption of AI for exam creation and grading is still in early pilot stages, constrained by academic integrity concerns and institutional caution. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments instructor productivity by auto-generating question banks, automatically scoring objective items, flagging outliers in essay grading, and suggesting rubric adjustments. Instructors retain final grading authority while saving substantial time on routine assessment tasks. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools substantially help instructors draft question banks, create rubrics, and pre-screen or suggest grades, saving significant time while the instructor retains final oversight. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can compile exam questions (with templates and databases) and generate grading rubrics, and can fully automate grading of objective questions. However, human judgment remains necessary for essay grading, exam design validation, and ensuring alignment with learning objectives, preventing full end-to-end automation at consistent quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft exam questions and grade objective or short-answer responses well, but compiling exams aligned to specific course content and grading nuanced essays still requires human judgment for full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Educational institutions have gradually adopted automated grading tools, but academic autonomy norms, faculty resistance to algorithmic assessment, and accreditation expectations that instructors maintain direct assessment oversight create meaningful friction against full substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Postsecondary instructors have discretion over grading policy and academic integrity concerns exist, but there's no licensing requirement mandating a human perform grading, giving some but not strong barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-powered exam platforms reduce instructor labor significantly compared to manual compilation and hand-grading, especially at scale across multiple sections. The per-student cost of automated grading is far lower than human instructor time, though setup and oversight add some overhead. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-based question generation and objective grading tools are markedly cheaper than faculty or TA time per exam cycle, though essay-grading still needs oversight, tempering full savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (LMS systems with AI grading, question banks, automated scoring for multiple choice) reliably perform parts of this task in production. Full automation of subjective assessment remains limited, but objective exam administration and grading are mature and widely available in educational software. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like AI-assisted quiz generators and automated essay scoring exist and are used in some institutions, but reliability varies and higher education adoption for grading remains limited and often supplemented by instructor review. |
Perform spatial analysis and modeling using geographic information system techniques.
51CI 35–67 · exposure 45 · augmentation 88 · importance 3.3/5 · click for rater detail
Perform spatial analysis and modeling using geographic information system techniques.
51| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | GIS is well-established in higher education and geography departments are digitized, but adoption of AI-augmented spatial analysis in teaching remains uneven; pilots exist but production deployment in curricula is still emerging. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education and academic GIS research adopt AI tools slowly and selectively, mostly for coding assistance rather than full analytical pipelines. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI strongly augments GIS work by automating data preprocessing, suggesting analytical approaches, generating visualizations, and enabling rapid scenario testing, allowing instructors to focus on conceptual understanding and interpretation rather than manual processing. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly aids tasks like writing GIS code, suggesting spatial methods, interpreting outputs, and generating visualizations, meaningfully speeding up an instructor's workflow while they retain analytical oversight. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | GIS spatial analysis involves well-defined workflows (buffer analysis, overlay operations, raster/vector processing) that current AI systems and tools can execute with minimal human intervention, achieving significant time savings on data preparation, processing, and visualization. |
| Task automatability | claude-sonnet-5 | 2/5 | GIS spatial analysis requires selecting appropriate methods, interpreting spatial relationships, and validating outputs against domain knowledge, which current AI can assist but not fully replace end-to-end at production quality.assisted. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | GIS analysis in education has low regulatory barriers and no licensing requirement for the automation itself; institutional friction around tool adoption is modest in academic settings where technical infrastructure is increasingly standard. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for GIS analysis itself, but academic and research integrity norms mean faculty typically must validate and take responsibility for analytical methods and outputs. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Open-source GIS combined with AI processing (cloud services, automated analysis pipelines) is substantially cheaper than paying a skilled analyst or GIS specialist to perform equivalent analyses from scratch. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI can cut some scripting/data-prep time, the specialized software licenses, data curation, and expert oversight needed keep costs comparable to or only modestly below human-led analysis. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | GIS software (ArcGIS, QGIS) increasingly integrates AI-assisted analysis tools, and AI can perform many spatial modeling operations reliably, but interpretation of results and integration into teaching contexts still requires human judgment and customization. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI coding assistants can help write GIS scripts (e.g., Python/ArcGIS/QGIS) and some tools offer automated spatial analytics, but no deployed product autonomously conducts full research-grade spatial modeling reliably. |
Select and obtain materials and supplies, such as textbooks.
45CI 25–65 · exposure 45 · augmentation 75 · importance 3.0/5 · click for rater detail
Select and obtain materials and supplies, such as textbooks.
45| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Educational institutions adopt new procurement technologies slowly, with strong preference for faculty and librarian involvement in material selection. Most adoptions remain in the pilot phase rather than full production replacement of human procurement staff. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adopts AI tools unevenly and administrative/procurement tasks like this are not a focus of fast AI deployment in academia. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by searching databases, summarizing reviews, flagging price comparisons, and organizing vendor options, which speeds the human selection process. However, the augmentation is partial—faculty still drive final decisions based on pedagogical needs. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI is highly effective at helping instructors quickly find, compare, and evaluate textbook options and supplementary materials, significantly speeding up the selection process while the instructor retains final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with searching catalogs and comparing textbooks, the task requires human judgment about curriculum fit, budget constraints, and institutional approval. End-to-end automation with 50% time savings at equal quality is unlikely because final selection and procurement sign-off remain human responsibilities. |
| Task automatability | claude-sonnet-5 | 4/5 | Identifying, comparing, and selecting appropriate textbooks/materials is largely research and comparison work that AI can do quickly by summarizing options, syllabi alignment, and reviews, with a human making the final pick. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational procurement typically involves institutional policies, budget approvals, vendor relationships, and faculty input that create organizational friction. Many institutions require human authorization and departmental sign-off on material selections for pedagogical and financial reasons. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Some institutional purchasing/procurement policies and academic freedom over course content create mild friction, but no licensing or legal requirement mandates human-only material selection. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Implementing an AI-based procurement system requires setup, integration with institutional vendors, and ongoing oversight. This cost, combined with the modest labor saved on routine material selection, makes the cost ratio unfavorable compared to current human-driven processes. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Using an AI assistant to research and shortlist materials is far cheaper than hours of faculty time spent browsing catalogs and reviews. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product fully automates textbook selection and procurement for educational institutions. Some e-procurement platforms and library systems exist, but they require human curation and decision-making at multiple stages, falling short of reliable autonomous performance. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | AI tools can generate curated lists of textbooks and materials and compare them, but no deployed product fully manages procurement or institutional purchasing workflows reliably at scale. |
Write grant proposals to procure external research funding.
42CI 30–55 · exposure 38 · augmentation 63 · importance 3.2/5 · click for rater detail
Write grant proposals to procure external research funding.
42| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI for grant writing in academic institutions is still in the pilot phase; most departments rely on human expertise, institutional grant offices, or external consultants. Public adoption data shows minimal displacement. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Academic research settings show growing but uneven adoption of AI writing tools, with many faculty experimenting but few institutions with formalized workflows around grant AI use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating outlines, suggesting literature integration, and drafting preliminary sections, modestly raising faculty productivity. However, the core task of crafting a compelling, novel research narrative and meeting specific funder priorities still rests with the human researcher. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially speeds up drafting, editing, and formatting of proposal sections, improving productivity while the researcher retains control over strategy, methodology, and final content. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Grant proposals require contextual judgment, institutional knowledge, and persuasive argumentation tailored to specific funding bodies and research agendas—capabilities current AI systems struggle with at production quality. While AI can draft sections and suggest structures, significant human revision and originality are needed to meet funder expectations, falling well short of 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft substantial portions of grant proposals (background, literature synthesis, boilerplate sections) but requires significant human input on original research design, budget justification, and institutional specifics to meet quality bar at 50% time savings.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Universities often require human faculty ownership and institutional sign-off on grants; federal funding agencies (NSF, NIH) expect human authorship and accountability. These policies create friction, though they do not formally prohibit AI-assisted drafting. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but institutional and funder norms typically require the PI's certification and intellectual ownership of the proposal, creating some accountability friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tools (ChatGPT Plus, specialized writing assistants) cost $20–50/month, but academics still invest 40–60 hours per proposal; total AI-assisted cost remains comparable to or higher than hiring a professional grant writer or leveraging institutional grant support staff. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting assistance is cheap per query, but the overall task still requires substantial paid faculty time for review, strategy, and compliance, keeping total cost roughly comparable to unaided human effort minus modest savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No mature product reliably produces fundable grant proposals end-to-end; AI writing tools exist but consistently require extensive expert revision to meet funder standards. Universities and research institutions have not deployed autonomous AI grant-writing systems in production. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Tools like ChatGPT, Claude, and specialized grant-writing assistants are used in production by researchers today, but reliability varies and human revision is still essential for funder-specific requirements and accuracy. |
Keep abreast of developments in the field by reading current literature, talking with colleagues, and participating in professional conferences.
36CI 25–46 · exposure 30 · augmentation 75 · importance 4.3/5 · click for rater detail
Keep abreast of developments in the field by reading current literature, talking with colleagues, and participating in professional conferences.
36| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Higher education remains a relatively laggard sector for AI adoption in instructional tasks; while faculty may use AI tools for assistance, the norm of independent professional development remains strong, and institutions have not systematized AI-driven field monitoring. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Higher education is adopting AI research tools (e.g., literature summarizers, search assistants) at a moderate pace, though full integration into scholarly practice remains uneven. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist by rapidly summarizing new papers, flagging relevant research, and organizing conference proceedings, allowing educators to scan a broader literature base in less time while they retain agency over what to prioritize and integrate into their teaching. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools like semantic search, paper summarizers, and alert systems can meaningfully speed up literature review and knowledge synthesis for postsecondary teachers staying current in geography. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can summarize literature and identify recent developments, the task fundamentally requires human judgment about relevance, synthesis across multiple sources, and genuine professional networking with colleagues—activities that cannot be fully automated and require sustained engagement with the field. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help surface and summarize literature, but the underlying task of ongoing professional engagement, networking, and judgment-based synthesis requires sustained human involvement that isn't fully automatable end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Professional competence and currency in one's field is often a tacit expectation embedded in faculty employment and accreditation standards, creating organizational and reputational barriers to full substitution; colleagues' and students' expectations that instructors engage with their discipline also reinforce this. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No formal licensing barrier exists, but norms of academic engagement, tenure expectations, and professional community participation create moderate organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While literature summarization tools are cheap per use, the overhead of integration and human review (since an educator must ultimately decide what matters for their teaching) approaches the cost of a person spending time on this task themselves. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools for literature scanning are cheap relative to a professor's time, but the task also includes conference attendance and networking that AI cannot replace, so overall cost savings are only partial. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI systems exist to summarize academic papers and curate relevant content, and some tools help organize conference materials, but no deployed product reliably replaces the human judgment needed to stay meaningfully current or the interpersonal dimension of professional conversations. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI literature summarization and alerting tools exist and are used, but they don't reliably substitute for conference participation, colleague discussion, or nuanced field awareness in production settings. |
Maintain geographic information systems laboratories, performing duties such as updating software.
33CI 30–35 · exposure 25 · augmentation 50 · importance 3.3/5 · click for rater detail
Maintain geographic information systems laboratories, performing duties such as updating software.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Higher education has been slow to adopt autonomous AI-driven lab management; most institutions rely on traditional IT support models. GIS is a specialized domain with legacy systems, limiting adoption velocity compared to general enterprise software update scenarios. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education IT and academic departments are slow adopters of full automation for lab administration compared to fast-moving tech sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools can assist with monitoring system health, recommending updates, documenting changes, and flagging compatibility issues, meaningfully reducing the manual workload for human lab managers while they retain oversight of critical decisions and troubleshooting. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can assist with troubleshooting scripts, documentation, and update guidance, meaningfully speeding up parts of the maintenance work performed by IT staff or instructors. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Updating software can be partially automated (patch management, scheduled updates), but maintaining GIS labs involves monitoring diverse hardware/software ecosystems, troubleshooting environment-specific issues, and ensuring compatibility with specialized GIS applications—tasks requiring human judgment and domain expertise that current AI cannot handle end-to-end at 50% time savings. |
| Task automatability | claude-sonnet-5 | 2/5 | Software updates for GIS labs involve environment-specific configuration, licensing, hardware compatibility, and troubleshooting that current AI can assist with but not reliably execute end-to-end without human oversight.in |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Educational institutions typically require IT staff or authorized administrators to manage laboratory systems due to security, liability, and institutional policy requirements. However, these are organizational/procedural rather than hard legal barriers, creating moderate friction to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but institutional IT policies, security compliance, and coordination with faculty/students create moderate organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted management tools exist but still require substantial human oversight, domain expertise, and manual intervention for complex GIS environments. The all-in cost (tools, integration, ongoing monitoring) is comparable to or higher than employing IT staff with GIS domain knowledge. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can help write update scripts or diagnose errors, but a human still must execute, test, and verify lab-wide changes, so overall cost savings versus IT staff time are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While basic software update automation exists, reliable end-to-end GIS lab maintenance requires handling edge cases, version conflicts, and institutional-specific configurations. Few if any products demonstrate production-scale capability to autonomously manage full GIS laboratory environments with acceptable error rates. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously manages a university GIS lab's IT stack; IT admins use scripts and some AI-assisted tools but human sysadmins remain essential for physical labs and institutional systems. |
Plan, evaluate, and revise curricula, course content, course materials, and methods of instruction.
32CI 25–39 · exposure 30 · augmentation 75 · importance 4.0/5 · click for rater detail
Plan, evaluate, and revise curricula, course content, course materials, and methods of instruction.
32| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Higher education has been slow to integrate AI into core instructional design despite tool availability. Adoption remains largely experimental (drafting aids, content suggestions) rather than replacing faculty curriculum decisions in production settings. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adopts AI tools unevenly and cautiously for curriculum design, with pilots more common than institution-wide production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can effectively assist by generating content drafts, suggesting course structures, analyzing learning objectives, and providing variations on instructional approaches. Faculty remain in the loop to apply disciplinary judgment and institutional knowledge, substantially raising productivity on material preparation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI is genuinely useful for brainstorming course structures, generating materials, and suggesting revisions, substantially speeding up the drafting phase while the instructor retains final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate draft course materials and suggest content organization, curriculum planning requires pedagogical judgment, alignment with institutional standards, and iterative refinement based on student outcomes. AI cannot independently evaluate effectiveness or revise based on classroom feedback, limiting time savings to <50%. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can draft syllabi and suggest content, but genuine curriculum planning requires institutional context, accreditation alignment, and pedagogical judgment that current tools cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Curriculum decisions at postsecondary institutions typically require faculty governance, disciplinary expertise, and institutional/accreditation alignment. Faculty retain both legal and professional authority over curriculum, and academic norms strongly favor human expertise in course design. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement blocks AI use, but academic freedom, faculty governance, and accreditation standards create organizational friction against full automation of curriculum authority. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While AI tools have low inference costs, curriculum planning involves significant human oversight, context integration, and iterative refinement. The loaded cost of faculty time for review and revision likely remains comparable to or exceeds the cost of AI assistance. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting assistance is cheap per query, but the human review, subject-matter validation, and institutional approval processes keep overall cost comparable to faculty time rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI writing tools and content generation systems exist and are used to draft syllabi and learning materials, but no mature product reliably performs the full evaluation and revision cycle. Current systems produce material that requires substantial faculty oversight and contextual adaptation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like ChatGPT or course-design copilots are used informally by instructors but no deployed system autonomously plans and revises postsecondary curricula reliably in production. |
Advise students on academic and vocational curricula and on career issues.
32CI 30–34 · exposure 25 · augmentation 63 · importance 3.5/5 · click for rater detail
Advise students on academic and vocational curricula and on career issues.
32| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Higher-education adoption of AI advising remains limited to pilot chatbots and supplementary tools; most institutions still rely primarily on human advisors due to regulatory, accreditation, and reputational concerns. Adoption is slower than in business or technical sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education advising has seen slow, cautious AI adoption—mostly chatbots for logistics—rather than substantive advising automation, reflecting sector's typically slower AI uptake outside core research/admin functions. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by providing curriculum lookup, degree-requirement tracking, and labor-market data in real time, reducing research burden on advisors and freeing them for deeper conversation. However, the core advising conversation—matching student goals to opportunities—remains primarily human-driven. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist advisors by summarizing career data, drafting degree plans, and answering routine questions, freeing human time for higher-value counseling interactions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can provide factual information about curricula and career paths, meaningful academic and vocational advising requires understanding individual student contexts, aspirations, and constraints—nuanced judgment that AI cannot yet reliably replicate end-to-end. Some informational components (program descriptions, job outlook data) could be automated, but the counseling core demands human judgment. |
| Task automatability | claude-sonnet-5 | 2/5 | Advising blends factual guidance with personalized judgment, relationship-building, and institutional knowledge that current AI cannot fully replicate end-to-end; only partial sub-steps (course lookup, generic career info) are automatable. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Institutional custom, student preference for human advisors, and educational policy often require human sign-off on degree plans or formal advising records, creating moderate friction. Liability for poor career advice also discourages automation, though no hard legal requirement prevents AI assistance. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement, but institutional norms, accreditation expectations, and student preference for human mentorship create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Advising tools and chatbots are relatively inexpensive, but the human advisor salary for a full advising conversation is still lower than integrating, fine-tuning, and maintaining AI systems that capture sufficient student context to achieve parity in advice quality. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools for basic information retrieval are cheap, but the human advisor's judgment, credibility, and relationship value keep overall cost comparable once quality and trust are factored in. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs comprehensive academic/vocational advising at production scale; chatbots and career tools exist but lack the contextual reasoning and personalization needed to replace or significantly substitute for human advisors. Existing systems handle narrow FAQ tasks only. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Chatbots and advising software exist for basic FAQs and degree audits, but no deployed product reliably conducts full academic/career advising sessions at postsecondary level with nuanced judgment. |
Conduct research in a particular field of knowledge and publish findings in professional journals, books, or electronic media.
29CI 25–32 · exposure 25 · augmentation 75 · importance 4.3/5 · click for rater detail
Conduct research in a particular field of knowledge and publish findings in professional journals, books, or electronic media.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Academic sectors are adopting AI cautiously for research support (literature tools, writing assistance), but adoption of AI for actual research conduct and publication remains minimal due to credibility, ethics, and career-advancement concerns in postsecondary education. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Higher education and academic research are adopting AI writing and analysis tools at a moderate pace, with pilots and guidelines emerging but full research automation still rare. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments research productivity through literature discovery, manuscript drafting, statistical analysis, and revision support while the researcher maintains full oversight and intellectual control. These tools are already improving efficiency in academic workflows across postsecondary institutions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially aids literature review, data analysis, drafting, and editing, meaningfully increasing researcher productivity while the scholar retains responsibility for design and interpretation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Research design, literature review, and data analysis can be partially automated with AI tools, but conducting novel field research, forming original hypotheses, and interpreting findings in geographical contexts requires human expertise and judgment. The creative and empirical components critical to publishable research remain largely human-dependent. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can assist with literature review, drafting, and data analysis, but original geographic research requires field work, novel data collection, hypothesis generation, and scholarly judgment that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Academic publication requires human accountability: researchers must sign off on findings, institutions require faculty credentials, and peer review processes expect human expertise and integrity. Ethical standards in research and institutional attribution create legal and professional barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Academic publishing requires named human authorship, peer review, and institutional credit for tenure/promotion, creating structural barriers, though no formal licensing requirement exists specifically for this task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for research assistance (literature review, drafting) are inexpensive, but the human researcher cost dominates; a geography professor's time is too valuable to replace at lower cost. Integration and oversight of AI outputs add overhead without reducing the core human time requirement significantly. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools reduce some costs for literature synthesis and drafting, but the substantial human time in fieldwork, data collection, and expert analysis means overall cost savings versus a human researcher are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI can assist with literature searches, summarization, and drafting, but no deployed product performs end-to-end research generation or publication-ready manuscript production reliably. Existing systems lack the domain expertise and originality required for legitimate academic research in specialized geography. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like literature-review tools and writing assistants exist and are used by researchers, but no deployed system reliably conducts original research and produces publishable findings independently. |
Prepare and deliver lectures to undergraduate or graduate students on topics such as urbanization, environmental systems, and cultural geography.
28CI 25–30 · exposure 30 · augmentation 75 · importance 4.5/5 · click for rater detail
Prepare and deliver lectures to undergraduate or graduate students on topics such as urbanization, environmental systems, and cultural geography.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Higher education has been slow to adopt AI for core teaching tasks; pilots in lecture support exist, but production displacement of instruction delivery remains minimal. Resistance from faculty unions, institutional inertia, and pedagogical skepticism slow adoption. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adopts AI tools slowly for core teaching functions due to academic culture, accreditation inertia, and mixed evidence on effectiveness, though usage for prep work is growing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist instructors by drafting lecture outlines, generating examples, summarizing complex topics, creating visuals, and responding to common student questions—substantially raising preparation efficiency and content quality while the instructor retains full pedagogical control. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially helps professors draft lecture outlines, generate examples, create visual aids, and research current topics like urbanization trends, meaningfully boosting prep productivity. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate lecture content and outline slides, delivering a live lecture requires real-time student engagement, adaptive explanation, and presence that current systems cannot replicate end-to-end with 50% time savings at equal educational quality. AI can assist with preparation, but the human instructor remains essential for pedagogical effectiveness. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft lecture content and slides, but live delivery, adapting to student questions, and classroom presence still require a human instructor, so end-to-end automation with equal quality is not yet achievable. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Institutional accreditation, student expectations, legal and contractual obligations to provide qualified human instruction, and the human-contact requirement for mentorship and assessment create strong barriers to full automation of postsecondary teaching roles. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Accreditation standards, tenure-track faculty requirements, and academic norms around instructor qualifications create strong institutional and credentialing barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for content generation are inexpensive, but integrating them into a complete lecture delivery system with human oversight, customization, and student support remains cost-comparable to hiring an instructor, especially when quality expectations are high. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI content generation is cheap, actual delivery still requires faculty salary and institutional oversight, so the all-in cost of substituting a professor is not yet meaningfully lower. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed products (lecture-generation tools, content synthesis, slide creators) exist and can produce usable draft materials, but no product reliably handles the full teaching interaction—student questions, dynamic pacing, and assessment—at production quality in real classrooms. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like ChatGPT can generate lecture outlines and even AI avatars can present recorded content, but no mature product reliably delivers live, interactive postsecondary lectures at scale in real institutions. |
Participate in student recruitment, registration, and placement activities.
28CI 25–30 · exposure 25 · augmentation 50 · importance 3.0/5 · click for rater detail
Participate in student recruitment, registration, and placement activities.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Higher education has been slow to adopt AI for student-facing recruitment and placement; most institutions use traditional methods supplemented by CRM tools rather than AI-driven agents, reflecting risk-aversion and preference for human relationships in these sensitive processes. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education is a relatively slow-adopting sector for AI in core academic/administrative functions like recruitment counseling, though CRM and marketing automation tools are gradually being adopted. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by automating registration workflows, filtering applicant pools, suggesting placement matches based on student profiles, and managing scheduling—supporting human recruiters and advisors without replacing the interpersonal judgment central to these tasks. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI chatbots, CRM automation, and data analytics can meaningfully assist with lead generation, scheduling, and administrative aspects of recruitment, augmenting faculty efforts in this task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Student recruitment, registration, and placement require personalized communication, relationship-building, and judgment about individual fit—activities where AI can handle only narrow components (e.g., scheduling emails, filtering applications) but cannot autonomously conduct recruitment conversations or make placement decisions at scale with equivalent quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This task involves interpersonal recruiting, admissions counseling, and placement decisions that require relationship-building and institutional judgment beyond what current AI can fully replace end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational institutions face regulatory requirements around equal opportunity in recruitment and placement, fiduciary duty to students, and accreditation standards that often mandate human judgment and accountability; institutions also prefer human contact for student-facing recruitment and placement advising. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement, but institutional policies, accreditation standards, and the expectation of faculty involvement in admissions/placement decisions create moderate organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The infrastructure to automate recruitment, registration, and placement (CRM systems, chatbots, placement platforms) still requires significant human oversight and intervention; the total cost of AI systems plus required human supervision remains comparable to or higher than direct human effort. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can lower cost for initial outreach and scheduling, but the substantive faculty involvement in recruitment and placement retains human labor costs, keeping overall cost comparable to or only modestly cheaper than status quo. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While some AI tools assist with email outreach and registration automation exist, deployed systems lack the nuanced judgment, institutional knowledge, and interpersonal skill needed for genuine recruitment and placement activities; most institutions still rely on human judgment for these tasks. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some university systems use chatbots for initial inquiries and CRM automation for outreach, but actual recruitment conversations, registration advising, and placement decisions still rely heavily on human staff and faculty. |
Provide professional consulting services to government or industry.
25CI 20–30 · exposure 20 · augmentation 75 · importance 2.5/5 · click for rater detail
Provide professional consulting services to government or industry.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Government and industry consulting remain relationship-driven and risk-averse sectors. Adoption of AI for autonomous advisory is slow; firms use AI for support work (data prep, literature review) but retain human consultants for client-facing delivery and accountability. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Academic consulting is a niche, low-volume activity with limited public data on AI displacement; higher education and consulting sectors show slow, uneven AI integration for this kind of bespoke advisory work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially augment geographic consultants by rapidly synthesizing geographic data, generating scenario models, producing maps and visualizations, and drafting reports, freeing experts to focus on client interaction and strategic recommendations. This pattern is already visible in consulting firms using geospatial AI tools. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can significantly help geography professors research data, draft reports, and analyze spatial or policy information to support their consulting work, while the human remains the consultant of record. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Consulting requires nuanced judgment, stakeholder negotiation, and context-sensitive recommendations that demand human expertise. While AI can gather geographic data and produce preliminary analyses, end-to-end consulting delivery—including understanding client constraints, iterating on proposals, and taking accountability—remains heavily human-dependent. |
| Task automatability | claude-sonnet-5 | 2/5 | Consulting requires synthesizing expert judgment, contextual negotiation, and reputational trust that current AI cannot fully replicate end-to-end, though AI can support research and analysis components. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Consulting, especially to government and regulated industries, typically requires professional licensing (geography, urban planning credentials), client trust, legal liability for recommendations, and contractual sign-off by credentialed experts. Clients expect and often legally require a named professional responsible for advice. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing typically required, but reputational credibility, institutional affiliation, and client trust in a named expert create moderate barriers to AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI assistance (data analysis, drafting) can reduce some operational costs, but the high-value component—expert judgment and client relationship management—still commands senior human labor. All-in cost remains comparable to or above hiring experienced consultants. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply produce background research or drafts, but the consulting engagement itself still requires costly human expert time for judgment, credibility, and client relationships. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform consulting at scale; AI tools can draft reports or analyze datasets, but production consulting systems that independently client-facing advisory lack the accountability and adaptive judgment required in government/industry settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed AI products that independently serve as geography consultants to government or industry; this remains a human expert-driven service. |
Initiate, facilitate, and moderate classroom discussions.
18CI 5–30 · exposure 13 · augmentation 38 · importance 4.2/5 · click for rater detail
Initiate, facilitate, and moderate classroom discussions.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of AI for live classroom discussion facilitation in postsecondary education remains negligible; most institutions still view the instructor as irreplaceable for this core teaching function. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education has been slow and cautious in adopting AI for direct teaching/facilitation roles, with most use confined to administrative or content-generation tasks rather than live discussion leadership. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with minor tasks like suggesting follow-up questions or identifying themes in student comments, but current systems offer limited real-time pedagogical value during active discussion facilitation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help instructors generate discussion questions, summarize readings, or provide backchannel tools, meaningfully aiding preparation and follow-up even though it doesn't replace live facilitation. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Genuine classroom discussion facilitation requires real-time responsiveness to student comments, dynamic judgment about which tangents to pursue, and interpersonal skill to encourage participation—none of which current AI can perform reliably in a live classroom setting without constant human intervention. |
| Task automatability | claude-sonnet-5 | 2/5 | Facilitating live, adaptive classroom discussion requires real-time reading of student engagement, spontaneous questioning, and social dynamics management that current AI cannot reliably replicate end-to-end in a physical classroom. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong institutional and professional norms require a qualified human instructor to lead and take responsibility for classroom pedagogy; accreditation, student expectations, and educational law create significant friction against substituting AI for facilitation roles. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing barrier prevents AI assistance, but strong pedagogical norms, accreditation expectations, and student preference for human interaction create meaningful friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI infrastructure (streaming transcription, LLM inference, integration with classroom systems) plus human oversight to ensure quality and safety exceeds the hourly wage of a postsecondary instructor for this specific task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Even where AI could support discussion (e.g., online forums), a human instructor is still required for the core live facilitation, so cost savings versus full instructor wage are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably moderates live classroom discussions in production; tools exist for transcription and post-hoc analysis but not for real-time facilitation with equal pedagogical quality to a human instructor. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI chatbots can simulate discussion prompts or moderate text-based online forums to a degree, but no deployed product autonomously runs live in-person postsecondary classroom discussions at scale. |
Maintain regularly scheduled office hours to advise and assist students.
16CI 11–21 · exposure 5 · augmentation 50 · importance 3.9/5 · click for rater detail
Maintain regularly scheduled office hours to advise and assist students.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Higher education adoption of AI for core advising and office-hour functions remains limited; most institutions use AI only for peripheral tasks (scheduling, document processing) while maintaining human-led office hours due to accreditation norms and student outcome concerns. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education is a slow-adopting sector for replacing personal interaction tasks, though AI tutoring tools are being piloted alongside human office hours. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools can augment faculty office hours by pre-screening common questions, suggesting relevant resources, or generating draft referrals to support services, allowing faculty to focus on higher-level mentoring and decision-making during their limited time with students. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI chatbots and scheduling tools can help field common questions or prep materials, freeing some time, but the core advising interaction still requires the instructor. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Office hours require real-time, context-sensitive student interaction involving emotional intelligence, nuanced academic advice, and relationship-building that current AI systems cannot reliably replicate. While an AI chatbot might answer routine factual questions, it cannot substitute for the mentoring, career guidance, and personalized academic support that defines office hours. |
| Task automatability | claude-sonnet-5 | 1/5 | Office hours require real-time personal advising, mentorship, and relationship-building that current AI cannot substitute for in a way that meets equal-quality bar. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Institutions have strong organizational and professional norms requiring faculty to provide direct student support; many universities have explicit policies mandating office hours. Students and parents often expect human contact for academic advising, and institutions face reputational and enrollment risks if advising is fully delegated to AI. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement mandates a human specifically, but institutional norms, accreditation expectations, and student preference for personal interaction create meaningful friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | An AI system covering office-hour functions would require substantial setup, customization, and continuous human oversight to avoid poor advice, making the all-in cost competitive with but not significantly cheaper than employing a part-time advisor or using the faculty member's time directly. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Chat-based Q&A tools are cheap, but since they cannot fully replace the task, the effective cost of achieving equivalent human-quality advising via AI remains high due to oversight and limited capability. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some scheduling and administrative systems exist to automate calendar management and send reminders, but no deployed product reliably handles the core task—meaningful student advising with appropriate judgment and accountability. Chatbots can handle FAQ responses but lack the contextual understanding needed for genuine student support. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs the role of a professor's personal office-hour advising; chatbots exist for FAQ but not for substantive academic mentorship. |
Collaborate with colleagues to address teaching and research issues.
16CI 7–25 · exposure 13 · augmentation 50 · importance 3.4/5 · click for rater detail
Collaborate with colleagues to address teaching and research issues.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Higher education is a laggard in AI adoption for core academic functions. While institutions have adopted some AI tools for administrative tasks, genuine collaboration infrastructure remains grounded in human meetings, emails, and committees with slow change velocity. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adopts AI tools slowly for administrative and pedagogical support, but collaborative governance and committee work remain largely untouched by AI systems. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by synthesizing prior research, drafting discussion briefs, or organizing arguments to present to colleagues, thereby improving the efficiency and depth of collaborative problem-solving. However, the human remains central to negotiation and decision-making. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help prepare materials, summarize research, or draft communications that support these collaborative discussions, providing moderate productivity benefits. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in generating ideas, drafting summaries, or analyzing issues, genuine collaboration requires negotiation, consensus-building, and relationship judgment that current AI cannot perform end-to-end. The social and interpersonal dimensions of addressing teaching and research issues with human colleagues remain largely irreplaceable. |
| Task automatability | claude-sonnet-5 | 1/5 | Collegial collaboration on teaching and research issues requires interpersonal judgment, institutional context, and relationship-building that AI cannot replace end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Collaboration among academic colleagues is intrinsically a human-contact activity embedded in institutional culture and professional norms. Faculty governance, peer trust, and shared authority in addressing teaching and research matters create strong organizational and social barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Faculty governance, departmental norms, and academic collegiality structures strongly favor human participation, though no formal licensing law mandates it. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While AI drafting tools reduce some writing overhead, the core value of collaboration—stakeholder alignment and collective decision-making—cannot be substantially cost-reduced by current AI. The loaded cost of faculty time plus AI overhead is unlikely to be significantly cheaper than direct collaboration. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task, so no meaningful cost comparison favors AI; the human cost is the only real option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs multi-party academic collaboration at scale. AI can support individual contributors (drafting, analysis), but coordinating peers to resolve shared problems involves human judgment, buy-in, and context-awareness that existing systems handle only at a narrow task level. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs collegial collaboration autonomously; this remains a fundamentally human social and professional activity. |
Perform administrative duties, such as serving as department head.
8CI 0–16 · exposure 5 · augmentation 50 · importance 3.6/5 · click for rater detail
Perform administrative duties, such as serving as department head.
8| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Academic institutions operate on traditional hierarchies with slow digital transformation; while universities adopt some administrative tools, the core role of department head remains a human responsibility, and adoption of AI for this function is minimal and experimental. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Higher education administrative leadership roles show negligible AI displacement; adoption is limited to support tools like scheduling or document drafting. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist with scheduling, document preparation, data analysis for budgets, and communication drafting, raising a department head's productivity in routine administrative work while the human retains decision-making authority. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help draft reports, manage schedules, summarize meetings, or analyze budget data, providing useful support to a department head without altering the leadership function itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Administrative duties as a department head require complex interpersonal judgment, personnel management, budget oversight, and institutional decision-making that involve nuanced human relationships and accountability—areas where current AI systems cannot operate autonomously or reliably meet the 50% time-saving threshold end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | Serving as department head involves leadership, personnel decisions, budget authority, mentoring, and institutional politics that require in-person judgment and trust; AI cannot perform this role end-to-end.','no automation exists for holding an administrative leadership position.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Department head positions are formally designated roles with legal accountability for hiring, budgeting, and institutional decisions; organizational governance, accreditation bodies, and liability frameworks require a licensed human to hold and be responsible for these duties. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Department head is an institutionally designated leadership position typically requiring faculty appointment, tenure status, and formal governance approval, making substitution essentially impossible. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI can automate scattered administrative tasks (email filtering, meeting scheduling) but cannot replace the salary cost of a department head; the all-in cost of AI infrastructure plus required human oversight would not be materially cheaper than the human role itself. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for the human authority and accountability required in this leadership position, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with scheduling, document drafting, and basic data organization, no deployed system reliably performs the core functions of a department head (hiring decisions, performance reviews, conflict resolution, strategic planning) without substantial human oversight and judgment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs the role of department head; at best AI supports scheduling or drafting reports within the role, not the role itself. |
Supervise undergraduate or graduate teaching, internship, and research work.
4CI 0–7 · exposure 0 · augmentation 38 · importance 4.1/5 · click for rater detail
Supervise undergraduate or graduate teaching, internship, and research work.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Higher education adoption of AI for core supervision tasks remains negligible; institutions prioritize faculty autonomy and student-faculty relationships, and regulatory/accreditation resistance is strong. Pilots may exist, but production displacement is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education adopts AI tools unevenly and cautiously for core academic responsibilities like supervision, with pilots more common for administrative or content tasks than mentorship. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with administrative tracking (flagging late submissions, summarizing student performance data), but meaningful augmentation is limited because the core task—mentoring judgment, adaptive feedback, professional development of scholars—resists reliable AI support. Augmentation tools exist but do not substantially transform supervision productivity. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by suggesting feedback on drafts, tracking progress, or organizing supervisory meetings, but the core relational and evaluative supervision remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Supervising teaching, internship, and research work requires real-time human judgment about student progress, personalized feedback, mentoring relationships, and adaptive intervention—tasks that demand ongoing contextual understanding and emotional intelligence. Current AI systems cannot reliably manage the interpersonal and evaluative dimensions that define supervision at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | Supervision of students' teaching, internships, and research requires ongoing personal mentorship, judgment about individual development, and institutional accountability that current AI cannot replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Academic supervision is legally and ethically bound to faculty responsibilities; accreditation bodies, institutional policies, and professional norms require a licensed academic to sign off on student progress, grades, and research conduct. Substitution is not legally permissible. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Faculty supervision is often tied to institutional accreditation, degree-granting responsibilities, and mentorship relationships requiring a credentialed academic, creating strong structural barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A full-time faculty member's supervision cost is already built into their salary; AI-based oversight systems would require additional infrastructure, human review, and liability management, making the all-in cost comparable to or higher than the baseline human effort. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this supervisory role, so cost comparison favors the human who must perform it regardless of AI tool costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably supervises academic work in production. While AI can draft feedback or flag assignment submissions, genuine supervision—monitoring quality, providing corrective guidance, building professional judgment in students—remains a human-centric practice without mature automation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs supervisory mentorship of students' research or teaching practice; AI tools may support feedback on drafts but do not perform supervision itself. |
Act as advisers to student organizations.
4CI 0–7 · exposure 0 · augmentation 25 · importance 2.4/5 · click for rater detail
Act as advisers to student organizations.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Advising student organizations is fundamentally a human-contact responsibility embedded in educational culture, with no measurable trend toward AI replacement in postsecondary settings. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Higher education administrative/advising functions show slow AI adoption for interpersonal mentorship roles despite some use in scheduling or communications. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide minor assistance such as organizing meeting agendas or drafting policy templates, but the core advising function—mentorship, judgment, and institutional guidance—remains human-driven with limited augmentation potential. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help with scheduling, drafting communications, or budget tracking for the organization, but the core advisory relationship stays largely unaugmented. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires genuine human judgment, relationship-building, and mentorship to advise student organizations on strategy, governance, and leadership—core aspects that current AI cannot perform meaningfully without human presence and accountability. |
| Task automatability | claude-sonnet-5 | 1/5 | Advising student organizations requires ongoing personal mentorship, relationship-building, and situational judgment that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Educational institutions typically require human advisers with faculty or staff credentials and institutional accountability; liability and duty-of-care expectations create hard barriers to automating this advisory role. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Institutions typically require a designated faculty member to serve as official adviser for liability, signature authority, and institutional representation purposes. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | An AI system capable of advising student organizations would require significant integration, customization, and human oversight, making the all-in cost comparable to or higher than employing human advisers. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this role, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs the advisory function for student organizations; AI cannot substitute for the human adviser role that involves personal guidance, institutional knowledge, and decision accountability. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product serves as a student organization adviser; this remains a human relational and administrative role. |
Participate in campus and community events.
3CI 0–5 · exposure 0 · augmentation 25 · importance 2.9/5 · click for rater detail
Participate in campus and community events.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | There is no adoption pattern of AI for this task; it remains a core human responsibility in educational institutions with no technological substitution trajectory. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Higher education faculty community engagement is a low-digitization, relationship-driven activity with minimal AI adoption for this specific function. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist by scheduling events or preparing background materials, but the core task of human participation and engagement cannot be substantially augmented by current systems. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help with scheduling, event planning, or drafting talking points, but offers minimal assistance to the actual act of attending and engaging in person. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Participating in campus and community events is inherently a human social activity requiring physical presence, relationship-building, and contextual judgment. Current AI cannot meaningfully replace this interpersonal engagement. |
| Task automatability | claude-sonnet-5 | 1/5 | Attending and participating in physical campus/community events requires human presence, networking, and social judgment that AI cannot perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong barriers exist: institutional roles and community relationships require a human representative, and substitute systems lack legal standing or credibility in social/community contexts. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Institutional expectations, community relationship-building, and the inherently social/human nature of representing the department create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI has no applicable cost advantage here since the task fundamentally requires human presence and social interaction, making direct cost comparison irrelevant. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute delivering equivalent output, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can autonomously participate in social events, build relationships, or represent an institution in community settings. This remains outside the scope of current automation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product substitutes for a person's physical/social participation in events; this is fundamentally a human presence task. |
Supervise students' laboratory and field work.
0CI 0–0 · exposure 0 · augmentation 25 · importance 3.7/5 · click for rater detail
Supervise students' laboratory and field work.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task occurs in educational settings with strong norms favoring human instruction and oversight. No significant adoption of AI-driven student supervision systems exists, and regulatory and cultural barriers make rapid deployment highly unlikely. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Postsecondary field and lab supervision remains a physical, in-person activity with essentially no AI displacement occurring in this sector for this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with minor supplementary tasks like pre-lab safety checklists or post-field-work data review, but cannot augment the core supervisory function itself. The human instructor must remain fully present and responsible for safety and learning outcomes. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help prepare lab materials, safety checklists, or field guides in advance, but offers minimal real-time assistance during actual supervision. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time, in-person oversight of student safety, behavior, and learning in physical laboratory and field environments. Current AI cannot physically supervise students, ensure equipment safety, or make dynamic judgments about individual student progress and conduct in the moment. |
| Task automatability | claude-sonnet-5 | 1/5 | Supervising students during hands-on lab or field exercises requires physical presence, real-time safety oversight, and interpersonal judgment that current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Educational institutions have hard legal and fiduciary duties to provide qualified human supervision of students, especially in potentially hazardous laboratory and field work. Liability and regulatory requirements (safety codes, accreditation standards, institutional policy) mandate human presence and accountability. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Liability for student safety, institutional policy, and often legal/regulatory requirements for supervised fieldwork mandate a qualified human instructor be physically present. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | An instructor's loaded salary per supervision hour far exceeds any plausible cost of AI systems; however, AI cannot perform this task at all, making the comparison moot. Substitution is infeasible at any cost ratio. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute providing this supervisory function, so AI cost cannot be meaningfully compared as a replacement—human cost is the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs actual supervision of students in laboratory or field settings. AI systems lack embodied presence, cannot ensure safety protocols, and cannot replace the human authority and duty-of-care required by law and institutional policy. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product supervises students in physical field or lab settings; this remains firmly outside current product capabilities. |
Serve on academic or administrative committees that deal with institutional policies, departmental matters, and academic issues.
0CI 0–0 · exposure 0 · augmentation 38 · importance 3.5/5 · click for rater detail
Serve on academic or administrative committees that deal with institutional policies, departmental matters, and academic issues.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Committee participation is a core governance function governed by institutional law and tradition; no sectors are automating or replacing human committee service. This is fundamentally a human accountability structure that shows no meaningful displacement. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Higher education governance is a low-digitization, slow-moving institutional process with minimal AI penetration into committee participation itself. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist with meeting preparation (agenda synthesis, background research compilation) or post-meeting documentation, but the deliberative committee work itself requires human judgment and presence. Augmentation value is limited to clerical support around the core task. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help summarize policy documents, draft meeting notes, or prepare briefing materials, moderately aiding preparation without touching the deliberative core. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Committee service involves nuanced deliberation, consensus-building, and institutional judgment that require human presence, accountability, and contextual understanding of complex organizational dynamics. Current AI cannot meaningfully participate in the collaborative decision-making that defines committee work. |
| Task automatability | claude-sonnet-5 | 1/5 | Committee service requires human presence, deliberation, political negotiation, and institutional judgment that AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Institutional bylaws, accreditation standards, and governance frameworks legally require human faculty participation in committees. Faculty members must be present and accountable for decisions affecting policy, budget, and academic standards—these are hard, legally-mandated human requirements. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Faculty governance roles typically require formal appointment, institutional standing, and voting rights tied to employment status—hard organizational and often bylaw-based barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Committee service is typically performed by salaried faculty as part of institutional obligation rather than a billable task, making direct cost comparison inapplicable. AI systems cannot replace the human voice and voting responsibility inherent in governance work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI product replacing this role, so cost comparison favors the human by default since AI cannot deliver the output at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can reliably serve as a committee member or substitute for human participation in institutional governance. While AI can assist with document preparation or meeting summaries, it cannot engage in the deliberative, accountability-bearing role that committee service requires. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product substitutes for a faculty member serving on a governance committee; this remains entirely human-performed. |
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.