Forestry and Conservation Science Teachers, Postsecondary

25-1043.00
Median wage $101,420/yr1,520 employed (US)Rank #365 of 923 scored · top 40% by substitution

Teach courses in forestry and conservation science. 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

Substitution31
Exposure29
Augmentation61

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

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

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

Tasks on the substitution scale

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%29

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

Technical feasibility todayw 20%28

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

Cost vs. human wagew 15%34

panel mean rating 2.4/5 → substitution pressure 34/100

Adoption barriersw 20%inverted — strong barriers lower the score36

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

Sector adoption velocityw 10%29

panel mean rating 2.1/5 → substitution pressure 29/100

Task breakdown (25 tasks)

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

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

95

CI 95–95 · exposure 100 · augmentation 63 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510015/5Higher education has near-universal adoption of LMS and student information systems (SIS) for attendance and grade tracking, with deep, routine production use across virtually all postsecondary institutions.
Sector adoption velocityclaude-sonnet-55/5Higher education has near-universal adoption of digital gradebooks and attendance systems, representing mature, widespread digitization.
Augmentation potentialclaude-haiku-4-5-202510013/5LMS platforms assist instructors by automating routine entry and providing real-time dashboards for attendance monitoring, though the task is already largely automated rather than augmented once deployed.
Augmentation potentialclaude-sonnet-54/5AI-integrated LMS tools substantially reduce faculty administrative burden by auto-calculating grades, flagging attendance issues, and generating reports, though instructors still review and finalize records.
Task automatabilityclaude-haiku-4-5-202510015/5Record maintenance—tracking attendance, grades, and administrative data—is highly structured, rule-based work that current systems handle end-to-end via learning management systems (Canvas, Blackboard, etc.) with >50% time savings and equal accuracy compared to manual entry.
Task automatabilityclaude-sonnet-55/5Recording attendance and grades into a system is a structured, repetitive data-management task easily handled by existing LMS/SIS software and automation scripts, meeting the 50% time-saving threshold trivially.
Adoption barriersclaude-haiku-4-5-202510012/5While institutional policy and data governance practices create moderate friction, there are no legal or regulatory barriers preventing automated record-keeping; institutions can and do delegate this entirely to systems.
Adoption barriersclaude-sonnet-52/5Some institutional policy requires instructor sign-off on final grades, but the record-keeping itself has few legal or licensing barriers to automation.
Cost vs. human wageclaude-haiku-4-5-202510015/5LMS software typically costs institutions $10–50 per student annually for record management, vastly cheaper than the $50k+ loaded cost of administrative staff labor for the equivalent manual work.
Cost vs. human wageclaude-sonnet-55/5Automated record-keeping software costs a small fraction of the faculty or administrative time it replaces, especially at scale across an institution.
Technical feasibility todayclaude-haiku-4-5-202510015/5Mature, widely deployed LMS products reliably perform attendance tracking, grade entry, and record-keeping at scale across educational institutions globally, with robust features and institutional integrations.
Technical feasibility todayclaude-sonnet-55/5Learning management systems (Canvas, Blackboard, PowerSchool, etc.) already automate gradebook calculations, attendance tracking, and record-keeping in production at virtually all universities.

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

77

CI 76–79 · exposure 75 · augmentation 100 · importance 4.7/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Higher education, especially well-resourced institutions, shows rapid adoption of AI tools for administrative and instructional tasks. Many faculty and teaching centers actively promote AI use for syllabus and assignment drafting, with adoption in pilots and production common.
Sector adoption velocityclaude-sonnet-53/5Higher education is adopting generative AI for course prep at a moderate pace, with growing pilot programs and individual faculty use but no institution-wide mandated deployment yet.
Augmentation potentialclaude-haiku-4-5-202510015/5AI dramatically augments this task by offering instant first drafts, alternative framings, and adaptation to different student levels, freeing the instructor to focus on pedagogical refinement and alignment with learning objectives rather than starting from a blank page.
Augmentation potentialclaude-sonnet-55/5AI substantially speeds up drafting of syllabi, assignments, and handouts, letting instructors focus on customizing content to specific course goals and fieldwork elements unique to forestry/conservation science.
Task automatabilityclaude-haiku-4-5-202510014/5Current AI systems can generate syllabi, homework assignments, and handouts with high quality and speed, achieving well over 50% time savings. A teacher can prompt an LLM with course objectives and structure, receiving near-complete draft materials that require only light customization and review.
Task automatabilityclaude-sonnet-54/5Generating syllabi, homework assignments, and handouts from a course description or outline is a well-structured drafting task that current LLMs handle well, requiring mainly instructor review and customization rather than full recreation.
Adoption barriersclaude-haiku-4-5-202510012/5Course materials must ultimately reflect the instructor's voice and institutional requirements, but there are no legal or licensing barriers to using AI drafts. Organizational friction is low; adoption hinges mainly on instructor comfort and institutional policy, not hard constraints.
Adoption barriersclaude-sonnet-52/5No licensing or legal requirement mandates that only the instructor draft materials, though academic norms and institutional expectations for instructor-authored content create some mild friction.
Cost vs. human wageclaude-haiku-4-5-202510015/5AI inference costs for generating course materials are negligible ($0.01–0.10 per full syllabus), orders of magnitude cheaper than the 5–10 hours of faculty time traditionally spent on manual drafting at typical academic wages.
Cost vs. human wageclaude-sonnet-55/5Drafting these materials via AI costs a fraction of a cent to a few dollars in compute versus hours of faculty time, making it dramatically cheaper even after review.
Technical feasibility todayclaude-haiku-4-5-202510014/5Deployed LLM products (ChatGPT, Claude, etc.) reliably generate course materials in production use by educators. While some instructors report needing edits for tone or alignment with institutional requirements, the core task of material generation is demonstrably functional at scale.
Technical feasibility todayclaude-sonnet-54/5AI writing tools and LMS-integrated assistants (e.g., ChatGPT, Copilot, course-design AI tools) are already used by many instructors to draft syllabi and assignments, though final materials still need domain-specific adaptation for specialized forestry/conservation content.

Compile bibliographies of specialized materials for outside reading assignments.

76

CI 67–84 · exposure 70 · augmentation 100 · importance 2.7/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Postsecondary institutions are information-rich, digitally mature sectors with high AI adoption rates. Many instructors have already begun using ChatGPT and similar tools for syllabus support, and LMS integration is accelerating adoption in this segment.
Sector adoption velocityclaude-sonnet-53/5Higher education has moderate AI tool adoption for research and teaching support, with growing but uneven use of AI literature search tools among faculty.
Augmentation potentialclaude-haiku-4-5-202510015/5AI dramatically augments instructor productivity by instantly generating candidate bibliographies, allowing humans to filter, refine, and verify selections rather than starting from scratch. This transforms the workflow while keeping the instructor in full control of pedagogical judgment.
Augmentation potentialclaude-sonnet-55/5AI-powered search and citation tools substantially speed up finding and organizing relevant specialized readings, a task well-suited to augmentation while faculty retain final judgment on selections.
Task automatabilityclaude-haiku-4-5-202510014/5AI can reliably search academic databases, identify relevant papers and books on specialized forestry topics, and generate formatted bibliographies with minimal human intervention. This meets the ≥50% time-saving threshold for the core mechanical task, though verification of source quality and assignment fit may still require human review.
Task automatabilityclaude-sonnet-54/5AI systems with literature search and citation tools can compile topical bibliographies quickly, though verifying accuracy and relevance for a specific course still requires some human review.
Adoption barriersclaude-haiku-4-5-202510012/5No licensing or legal barriers exist; instructors can use these tools freely. Organizational friction is minimal and adoption is already occurring. The only modest barrier is preference for human curation of reading assignments, which is organizational rather than regulatory.
Adoption barriersclaude-sonnet-51/5No licensing, regulatory, or liability barriers prevent using AI tools to compile reading lists; it's a low-stakes administrative/academic task.
Cost vs. human wageclaude-haiku-4-5-202510015/5AI-assisted bibliography compilation costs pennies per assignment via API calls or subscriptions, versus the $30–50+ loaded cost of 30–60 minutes of instructor time. The cost difference is at least an order of magnitude in favor of AI.
Cost vs. human wageclaude-sonnet-54/5AI-assisted bibliography compilation is very fast compared to manual literature search, making it far cheaper than a professor's or TA's time for this narrow task.
Technical feasibility todayclaude-haiku-4-5-202510014/5Mature products (ChatGPT, Claude, Google Scholar API integrations, Zotero with AI plugins) demonstrably generate bibliographies and can search academic literature. However, domain-specific accuracy and citation completeness occasionally require human correction, preventing a full 5.
Technical feasibility todayclaude-sonnet-53/5Tools like reference managers, AI research assistants, and citation databases are deployed and used, but hallucinated or outdated citations remain a known issue requiring instructor verification.

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

65

CI 43–87 · exposure 66 · augmentation 88 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Higher education is rapidly adopting AI-assisted and automated grading tools; major learning management systems have integrated AI scoring, and many institutions are piloting or deploying these systems in production.
Sector adoption velocityclaude-sonnet-52/5Higher education, especially in applied science fields like forestry, has been slower and more cautious in adopting AI grading tools compared to sectors like finance or general professional services.
Augmentation potentialclaude-haiku-4-5-202510015/5AI provides transformative productivity gains by drafting detailed rubric-based feedback, flagging plagiarism, and identifying common errors, allowing instructors to focus on holistic assessment and student mentoring rather than mechanical marking.
Augmentation potentialclaude-sonnet-54/5AI can meaningfully speed up initial feedback drafting, plagiarism checks, and rubric-based scoring assistance, letting instructors focus on nuanced technical evaluation.
Task automatabilityclaude-haiku-4-5-202510015/5AI systems can automatically grade objective and short-answer assignments at scale with high reliability, and can provide detailed feedback on written papers through plagiarism detection, style analysis, and rubric-based scoring—exceeding 50% time savings compared to manual grading.
Task automatabilityclaude-sonnet-53/5AI can draft feedback and score structured assignments, but grading specialized forestry/conservation coursework (field reports, technical analyses) still requires domain expertise and judgment that current systems only partially replicate.
Adoption barriersclaude-haiku-4-5-202510012/5Minimal regulatory or licensing barriers exist; institutions can legally deploy AI grading systems. Primary friction is institutional policy, faculty skepticism, and student concerns about fairness, but these are organizational rather than legal obstacles.
Adoption barriersclaude-sonnet-53/5No licensing requirement blocks AI grading, but academic integrity norms, accreditation standards, and faculty responsibility for final grades create institutional friction.
Cost vs. human wageclaude-haiku-4-5-202510015/5AI grading costs (API calls, licensing, integration) are typically $0.01–$0.10 per submission, while a professor's loaded wage for grading an assignment is $5–$20+ per item; AI is orders of magnitude cheaper.
Cost vs. human wageclaude-sonnet-53/5AI-assisted grading tools are cheap per assignment, but integration, calibration to rubrics, and required faculty review narrow the cost advantage for specialized technical coursework.
Technical feasibility todayclaude-haiku-4-5-202510014/5Mature products (Turnitin, Canvas, ChatGPT-based grading assistants) are deployed in production at scale for assignment evaluation, though performance on nuanced essay grading and discipline-specific work remains materially below human consistency.
Technical feasibility todayclaude-sonnet-52/5General-purpose LLM grading tools exist and are used for essays in some contexts, but no mature, widely deployed product reliably grades niche postsecondary forestry/conservation science assignments at scale.

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

59

CI 48–70 · exposure 55 · augmentation 75 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Higher education has rapidly adopted learning management systems and auto-grading tools over the past decade, with widespread deployment in universities and community colleges, particularly for objective assessment and first-pass grading of essays.
Sector adoption velocityclaude-sonnet-52/5Higher education, especially specialized STEM fields, adopts AI grading/exam tools slowly and unevenly compared to fast-moving sectors like finance or general software.
Augmentation potentialclaude-haiku-4-5-202510014/5AI meaningfully assists instructors by auto-generating question banks, providing first-pass grading summaries, and flagging outlier grades for human review, substantially reducing the labor of exam creation and grading while leaving final assessment decisions and feedback to faculty.
Augmentation potentialclaude-sonnet-54/5AI meaningfully assists in drafting question banks, generating rubrics, and pre-grading objective content, saving significant instructor time while they retain final oversight.
Task automatabilityclaude-haiku-4-5-202510013/5AI can compile and grade multiple-choice and short-answer exams reliably, and can generate test questions from course materials, achieving significant time savings for routine assessment. However, designing exams that properly assess learning outcomes and creating fair rubrics for open-ended answers typically require human pedagogical judgment, limiting end-to-end automation to roughly half the task.
Task automatabilityclaude-sonnet-53/5AI can draft exam questions and grade objective or even short-answer responses with instructor review, but compiling exams aligned to specific course content and grading nuanced technical/scientific answers still needs human oversight for full quality equivalence.
Adoption barriersclaude-haiku-4-5-202510012/5Educational institutions face moderate friction: accreditation bodies may require human oversight of high-stakes grading, and some instructors prefer human judgment on subjective work. However, no legal licensing requirement or hard regulatory mandate prevents automated exam administration and grading, and delegation to others is already standard practice.
Adoption barriersclaude-sonnet-53/5No licensing requirement blocks AI-assisted grading, but academic integrity, accreditation standards, and institutional policy typically require instructor accountability and sign-off on grades.
Cost vs. human wageclaude-haiku-4-5-202510015/5AI-driven exam systems (infrastructure, API inference, basic grading) cost substantially less than paying graduate teaching assistants or faculty to compile, administer, and grade exams manually, achieving clear order-of-magnitude savings on routine assessment tasks.
Cost vs. human wageclaude-sonnet-53/5AI tools can cut time on question generation and rote grading substantially, but integration, review, and handling of complex scientific reasoning still require paid faculty/TA time, keeping costs roughly comparable when quality is maintained.
Technical feasibility todayclaude-haiku-4-5-202510014/5Multiple deployed products (Canvas, Blackboard, Gradescope, ChatGPT-integrated tools) already perform exam administration, auto-grading of objective items, and partial grading of essays at scale in educational institutions. These systems are production-ready, though subjective assessment grading remains imperfect and requires human review.
Technical feasibility todayclaude-sonnet-53/5Products like AI-assisted quiz generators and automated grading tools (e.g., Gradescope, LMS AI features) are deployed in higher ed, but reliability for specialized forestry/conservation science content is narrower than for general subjects.

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

36

CI 25–46 · exposure 30 · augmentation 63 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Postsecondary institutions remain conservative on automating scholarly engagement; adoption of AI literature tools is emerging but slow, with most faculty still preferring manual reading and in-person networking.
Sector adoption velocityclaude-sonnet-53/5Higher education has moderate AI adoption for research assistance tools, with growing use of AI-powered literature review tools, but conference attendance and networking remain unchanged.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can usefully assist by suggesting relevant papers, summarizing key findings, and highlighting trends in literature, but the faculty member remains the driver; this augmentation is meaningful but does not transform the core task.
Augmentation potentialclaude-sonnet-54/5AI tools like semantic search, paper summarizers, and alert systems significantly speed up literature review and discovery of new developments, meaningfully boosting productivity for this task.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can retrieve and summarize literature efficiently, the task requires synthesizing insights across disparate sources, engaging in meaningful professional dialogue, and networking—activities that demand human judgment and social presence. Partial automation (literature scanning) exists, but end-to-end execution at 50% time savings is not achievable today.
Task automatabilityclaude-sonnet-52/5AI can help summarize literature and surface relevant papers, but the actual task of staying current involves ongoing human judgment, networking, and synthesis that isn't fully replaceable by automation.
Adoption barriersclaude-haiku-4-5-202510014/5Strong organizational and professional norms require faculty to personally engage with scholarly literature and peer networks; institutions and professional bodies expect direct participation in conferences and collegial exchange. A faculty member cannot credibly delegate this to automation.
Adoption barriersclaude-sonnet-52/5No formal licensing barrier prevents AI assistance here, though professional norms around scholarly engagement and networking create some organizational friction.
Cost vs. human wageclaude-haiku-4-5-202510012/5Current AI services for literature monitoring and summarization are available but require significant human oversight and interpretation. The all-in cost (subscription + human time to validate and integrate) remains comparable to or higher than the faculty member's marginal effort.
Cost vs. human wageclaude-sonnet-53/5AI literature tools are cheap for the reading/summarization portion, but they don't replace the full task including conferences and colleague interaction, so overall cost comparison is mixed.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed product reliably handles the full scope of staying abreast in an academic field; literature aggregators and summarization tools exist but are narrow in scope and do not substitute for human reading, collegial discussion, and conference participation at production scale.
Technical feasibility todayclaude-sonnet-53/5Products like AI research assistants and literature summarization tools exist and are used by academics, but conference networking and colleague discussions are not addressed by any deployed AI product.

Write grant proposals to procure external research funding.

31

CI 29–34 · exposure 25 · augmentation 75 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Adoption of AI for grant writing in academic institutions remains slow and largely limited to pilot use of writing assistants for polish and ideation. Most forestry and conservation programs still rely on human expertise (faculty, grant writers) due to compliance requirements, risk aversion, and the strategic importance of funding. No evidence of rapid, production-level AI displacement in this domain.
Sector adoption velocityclaude-sonnet-52/5Academic research administration adopts AI tools slowly and unevenly; grant writing assistance is emerging but not yet a widespread production practice in higher education.
Augmentation potentialclaude-haiku-4-5-202510014/5AI writing tools demonstrably assist academics in this task: they help brainstorm, outline proposals, revise for clarity, generate background literature summaries, and proofread. Faculty remain in control and leverage AI to raise productivity on drafting and editing, transforming turnaround time without removing human judgment or accountability.
Augmentation potentialclaude-sonnet-54/5AI is genuinely useful for drafting narrative sections, editing for clarity, formatting, and summarizing prior work, meaningfully speeding up the proposal-writing process while the researcher retains intellectual control.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can draft sections of grant proposals (background, methods summaries) and improve text clarity, the task requires substantial human input: identifying funding opportunities, articulating novel research contributions, and crafting institution-specific justifications that depend on the researcher's unique expertise and institutional context. Current AI cannot reliably achieve 50% time savings end-to-end on this cognitively and strategically demanding task.
Task automatabilityclaude-sonnet-52/5AI can draft sections and boilerplate but cannot independently formulate novel research contributions, budgets, or institutional context that meet funder scrutiny, so full end-to-end substitution with equal quality is not yet reached.
Adoption barriersclaude-haiku-4-5-202510014/5Strong barriers exist: funding agencies often require the PI or authorized institutional representative to sign proposals, certifying accuracy and originality; institutions impose compliance reviews; and reputational/liability risk (false claims, plagiarism detection) mean humans must legally validate and take responsibility for final submissions. Autonomous AI substitution is not permitted.
Adoption barriersclaude-sonnet-53/5No licensing requirement bars AI use, but funders often require PI certification, institutional sign-off, and originality/integrity standards that create moderate friction against full automation.
Cost vs. human wageclaude-haiku-4-5-202510013/5A university grant-writing service or AI tool subscription (hundreds to thousands per year) is roughly comparable in cost to a few hours of a postdoctoral researcher's or grant writer's time per proposal. For bulk assistance on multiple proposals, AI tooling may lean slightly cheaper, but integration and human oversight maintain moderate cost balance.
Cost vs. human wageclaude-sonnet-53/5AI drafting reduces some drafting time cheaply, but the overall cost is comparable once expert review, revision, and domain-specific input from the researcher are factored in.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed product reliably writes complete, competitive grant proposals. AI writing assistants (e.g., ChatGPT, Grammarly) exist and can help with drafting and editing, but they lack the domain knowledge, strategic judgment, and institutional/funder context needed to produce proposals that win funding. Organizations still require humans to do the core work.
Technical feasibility todayclaude-sonnet-52/5AI writing tools are used to draft proposal text but no deployed product reliably produces fundable, discipline-specific grant proposals without heavy faculty rewriting and review.

Review papers for colleagues and scientific journals.

31

CI 20–43 · exposure 38 · augmentation 75 · importance 3.1/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Academic publishing and peer review adoption of AI automation remains slow; most journals still require human reviewers and discourage or prohibit AI assistance in reviews. Adoption is confined to pilots and is not widespread in production workflows despite growing interest.
Sector adoption velocityclaude-sonnet-52/5Academic publishing and higher education are historically slow to adopt AI for judgment-intensive peer review tasks, with policies often restricting AI use in the review process.
Augmentation potentialclaude-haiku-4-5-202510014/5AI tools already assist reviewers by flagging structural issues, summarizing manuscripts, checking references, and highlighting statistical concerns, meaningfully reducing review time and thoroughness. A human reviewer leveraging these assistants can conduct deeper, faster reviews while maintaining full evaluative responsibility.
Augmentation potentialclaude-sonnet-54/5AI can meaningfully assist reviewers by summarizing papers, checking references, detecting plagiarism, and highlighting inconsistencies, saving time while the human retains final judgment.
Task automatabilityclaude-haiku-4-5-202510013/5AI can assist with structural review, spotting missing citations, and identifying statistical errors, achieving partial automation of the review workload. However, evaluating novelty, significance, methodological soundness, and recommending acceptance/rejection still require domain expertise and human judgment that current systems cannot reliably provide end-to-end.
Task automatabilityclaude-sonnet-52/5AI can summarize and flag issues in papers but cannot yet reliably evaluate novel scientific contributions, methodology rigor, or field-specific significance for forestry/conservation science at a level equal to an expert reviewer.
Adoption barriersclaude-haiku-4-5-202510015/5Peer review traditionally requires a named, credentialed expert to stake professional reputation on their judgment; journals and colleagues expect human accountability for recommendations. Legal and professional norms make it difficult to substitute anonymous AI for human peer review without explicit consent and regulatory change.
Adoption barriersclaude-sonnet-54/5Peer review is an institutional and ethical responsibility tied to expert judgment, confidentiality, and journal policies that generally require named, credentialed human reviewers, creating strong professional and normative barriers.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI inference costs for reviewing a manuscript are low, but integration, prompt engineering, and human oversight to validate recommendations are non-trivial. For a specialist faculty member reviewing a few papers per year, the all-in AI cost per review may still exceed the opportunity cost of their time, especially given liability concerns.
Cost vs. human wageclaude-sonnet-53/5AI tools are cheap to run for surface-level checks, but since they cannot fully replace substantive review, the effective cost per equivalent-quality review remains comparable to human effort when factoring necessary oversight.
Technical feasibility todayclaude-haiku-4-5-202510013/5Tools exist (e.g., plagiarism detectors, automated citation checkers, some ML-based manuscript analysis) that perform narrow aspects of peer review. However, no deployed product reliably conducts a full peer review at the quality expected by journals or colleagues—error rates and scope limitations are material.
Technical feasibility todayclaude-sonnet-52/5Some AI writing/review assistant tools exist for grammar, structure, and basic consistency checks, but no deployed product performs substantive scholarly peer review reliably in production.

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

30

CI 25–35 · exposure 25 · augmentation 50 · importance 3.3/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Higher education procurement remains largely manual and decentralized; while some universities have digitized requisition systems, adoption of AI-driven procurement agents is minimal. Institutional inertia and regulatory caution slow adoption.
Sector adoption velocityclaude-sonnet-52/5Higher education administrative and procurement processes adopt AI slowly; academic departments rarely use AI agents for purchasing decisions.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can helpfully assist by drafting material lists, comparing vendors and prices, or summarizing equipment specifications, improving the human's speed and thoroughness in procurement planning without removing human judgment.
Augmentation potentialclaude-sonnet-53/5AI can assist by researching textbook options, comparing prices, and drafting supply lists, meaningfully speeding up part of the selection process.
Task automatabilityclaude-haiku-4-5-202510012/5While AI could assist in identifying suitable materials and generating purchase lists, the task requires navigating institutional procurement systems, vendor selection, checking budgets, and handling requisition approvals—most of which still require human authorization and institutional integration. End-to-end automation with 50% time saving is not achievable today.
Task automatabilityclaude-sonnet-52/5AI can help research and suggest textbooks or equipment options, but the actual selection requires curriculum judgment and procurement/ordering involves administrative and physical steps AI cannot fully execute.
Adoption barriersclaude-haiku-4-5-202510014/5Institutional procurement is heavily regulated by university policies, approved vendor lists, budget controls, and often requires human sign-off and formal requisition authority. Legal and compliance requirements create material friction to full automation.
Adoption barriersclaude-sonnet-52/5No licensing barrier, but institutional purchasing procedures, budget approval, and faculty discretion over curriculum materials create moderate organizational friction.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI assistance (e.g., generating vendor lists or comparing prices via web search) is inexpensive, but the task still requires significant human effort for verification, approval, and institutional integration. The all-in cost savings are marginal relative to a librarian or procurement coordinator's time.
Cost vs. human wageclaude-sonnet-52/5AI assistance is cheap for research but the task still requires human decision-making, vendor negotiation, and physical procurement, limiting overall cost savings.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed product reliably performs institutional procurement autonomously. LLMs can draft lists and summarize vendor comparisons, but they cannot access institutional catalogs, budget systems, or execute purchase orders without human intervention and oversight.
Technical feasibility todayclaude-sonnet-52/5Recommendation tools and procurement software exist but no deployed AI system autonomously selects and obtains lab equipment/textbooks for a specific course reliably.

Provide information to the public by leading workshops and training programs and by developing educational materials.

30

CI 25–35 · exposure 25 · augmentation 75 · importance 3.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Educational institutions have adopted AI for content draft and course design support, but live instruction by AI in formal postsecondary settings remains rare; adoption is limited to materials development, not task completion.
Sector adoption velocityclaude-sonnet-52/5Higher education and academic extension services adopt AI slowly, mostly for content drafting rather than replacing live instructional or outreach roles.
Augmentation potentialclaude-haiku-4-5-202510014/5AI can substantially assist educators by drafting workshop curricula, generating handouts, creating interactive materials, and analyzing participant feedback—all while the instructor remains in the loop and responsible for delivery and adaptation.
Augmentation potentialclaude-sonnet-54/5AI substantially speeds up creation of workshop slides, handouts, quizzes, and instructional materials, letting instructors focus more time on live delivery and interaction.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can help draft educational materials and organize workshop content, the core task of *leading* workshops and training programs requires real-time human interaction, responsiveness to audience questions, and adaptive teaching—capabilities current AI systems cannot perform end-to-end at equal quality without substantial human oversight.
Task automatabilityclaude-sonnet-52/5AI can help draft educational materials but leading in-person workshops and training programs requires live human facilitation, presence, and adaptive interaction that current AI cannot replicate end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5Postsecondary teaching roles typically involve institutional credentialing, accreditation requirements, and expectations that a qualified human must deliver instruction; liability and institutional governance create meaningful friction against full automation.
Adoption barriersclaude-sonnet-52/5No strict licensing requirement for delivering public workshops, but audiences expect a credible human expert and institutional accreditation processes create some friction.
Cost vs. human wageclaude-haiku-4-5-202510012/5Generating educational materials via AI is cheap, but the bottleneck is the live-teaching component, which still requires a human instructor; total cost savings do not exceed the loaded wage of a postsecondary educator for the full task.
Cost vs. human wageclaude-sonnet-52/5AI can cheaply produce draft materials, but the labor-intensive live workshop delivery still requires paid human time, keeping overall cost comparable to or only modestly below a human instructor's.
Technical feasibility todayclaude-haiku-4-5-202510012/5AI can generate draft educational materials and lesson plans, but no deployed product reliably leads live workshops or training programs independently; such systems exist only in early-stage prototypes and require heavy human facilitation.
Technical feasibility todayclaude-sonnet-52/5Products like ChatGPT can generate slides, handouts, or curricula, but no deployed system reliably conducts live workshops or public training sessions in forestry/conservation contexts today.

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

29

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

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Higher education has been slow to adopt AI for core instructional design and curriculum governance, with most adoption limited to supplementary content generation. Faculty autonomy and institutional conservatism on pedagogical decisions remain strong, limiting rapid displacement.
Sector adoption velocityclaude-sonnet-52/5Higher education adoption of AI for curriculum design is still in early pilot stages, with slow institutional change cycles typical of academia.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist by generating content ideas, flagging curriculum gaps, suggesting assessment frameworks, and automating administrative documentation, allowing faculty to focus on synthesis and institutional fit. However, the assistance is partial and requires careful human validation.
Augmentation potentialclaude-sonnet-54/5AI is highly useful for brainstorming course content, generating materials, summarizing new research, and suggesting revisions, meaningfully boosting instructor productivity while they retain final judgment.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can draft course outlines, generate example materials, and suggest pedagogical structures, comprehensive curriculum planning requires understanding institutional constraints, student populations, accreditation standards, and long-term educational goals that demand human judgment. AI cannot reliably evaluate and revise curricula end-to-end with quality parity to experienced educators.
Task automatabilityclaude-sonnet-52/5AI can draft syllabi and suggest materials, but faculty-level curriculum planning requires accreditation alignment, disciplinary judgment, and institutional context that AI cannot fully replace end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5Curriculum decisions are typically governed by faculty governance structures, accreditation bodies, and institutional academic committees. Postsecondary institutions have strong organizational and regulatory barriers to outsourcing core educational strategy to AI systems without faculty authorization and review.
Adoption barriersclaude-sonnet-53/5No licensing requirement mandates a human, but tenure/faculty governance structures, academic freedom norms, and institutional accreditation processes create meaningful friction against full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI can reduce time on content generation, but curriculum design at a postsecondary level still requires faculty salary investment for oversight, validation, and synthesis. The total cost of AI-assisted curriculum revision remains comparable to or higher than hiring experienced educators.
Cost vs. human wageclaude-sonnet-53/5AI tools are cheap for drafting content, but the human oversight, subject-matter expertise, and institutional approval processes still dominate the overall cost of curriculum work.
Technical feasibility todayclaude-haiku-4-5-202510012/5Generative AI tools exist for creating course content drafts and suggesting instructional methods, but no deployed product reliably handles the full cycle of planning, evaluating, and revising curricula at scale. Implementation depends on instructor oversight and institutional validation.
Technical feasibility todayclaude-sonnet-52/5Products like ChatGPT and course-design assistants are used informally by instructors, but no deployed system reliably performs full curriculum evaluation and revision autonomously in production.

Prepare and deliver lectures to undergraduate or graduate students on topics, such as forest resource policy, forest pathology, and mapping.

28

CI 25–30 · exposure 25 · augmentation 75 · importance 4.6/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Academic institutions are slow adopters of automation in core teaching functions. While some institutions pilot AI-assisted content generation and tutoring, actual displacement of faculty lecture preparation and delivery remains minimal. Higher education shows laggard digitization compared to corporate sectors.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools slowly for core teaching, especially in niche STEM/applied fields like forestry, with pilots more common than production use for full lecture delivery.
Augmentation potentialclaude-haiku-4-5-202510014/5AI tools meaningfully assist instructors by drafting lecture outlines, generating example problems, summarizing research, and creating visualizations on topics like forest mapping and policy. These assistive tools can raise preparation efficiency and content quality while the instructor retains full pedagogical control and student interaction.
Augmentation potentialclaude-sonnet-54/5AI substantially aids lecture preparation—drafting outlines, generating explanations, creating visuals, and summarizing research—while the instructor still delivers and refines the material.
Task automatabilityclaude-haiku-4-5-202510012/5Lecture preparation (slides, notes) could be partially automated with AI drafting, but live delivery requires human interaction, adaptability to student questions, and pedagogical judgment that AI cannot reliably replicate. Content generation aids exist, but end-to-end replacement with 50% time savings while maintaining quality is not achievable today.
Task automatabilityclaude-sonnet-52/5AI can help draft lecture content and slides, but live delivery, adapting to student questions, and pedagogical judgment for a specialized field require human presence and expertise that current tools cannot fully replace.
Adoption barriersclaude-haiku-4-5-202510014/5Strong barriers exist: universities require credentialed faculty to design and deliver courses, accreditation bodies mandate human instruction, institutional governance restricts who can assign grades and certify learning, and students expect human mentorship and accountability. Legal and contractual requirements protect instructor roles.
Adoption barriersclaude-sonnet-53/5No licensing requirement for lecturing itself, but institutional norms, accreditation expectations, and student preference for expert human instructors create moderate friction to substitution.
Cost vs. human wageclaude-haiku-4-5-202510012/5A faculty member's loaded cost (salary, benefits, office) is substantially higher per lecture than AI infrastructure, but AI cannot yet substitute for the full role. The cost of oversight, course management, and human instruction still dominates, making the ratio unfavorable for replacement.
Cost vs. human wageclaude-sonnet-52/5AI can cheaply assist with content drafting, but the overall task still requires a paid faculty member for delivery, oversight, and subject-matter authority, keeping all-in cost comparable to human labor.
Technical feasibility todayclaude-haiku-4-5-202510012/5AI systems can generate lecture outlines and draft materials, but no deployed product reliably handles the full cycle of preparation, delivery, and adaptive teaching. Products lack ability to handle real-time student interaction, assessment, and dynamic course adjustment at production scale in academic settings.
Technical feasibility todayclaude-sonnet-52/5Products like ChatGPT and lecture-generation tools exist for content prep, but no deployed system reliably delivers full lectures in specialized postsecondary forestry/conservation courses at scale.

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

28

CI 25–30 · exposure 25 · augmentation 63 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5While higher education has piloted AI chatbots for basic advising queries, production adoption of AI-driven advising at scale remains limited; institutions continue to rely on human advisors, and regulatory/accreditation structures reinforce that pattern.
Sector adoption velocityclaude-sonnet-52/5Higher education is a slow-adopting sector for AI-driven personal advising; pilots exist for general student services but postsecondary faculty advising remains largely human-led.
Augmentation potentialclaude-haiku-4-5-202510014/5AI can meaningfully assist human advisors by surfacing relevant program requirements, degree pathways, labor-market data, and flagging at-risk students, substantially raising advisor productivity and the quality of guidance delivered to students.
Augmentation potentialclaude-sonnet-53/5AI can help faculty advisors by summarizing degree requirements, career pathways, and job market data, improving efficiency, but the core advising interaction still depends on human judgment.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can provide generic career information and curriculum recommendations at scale, advising students on academic paths and career issues requires personalized assessment of individual aptitudes, constraints, and goals—judgment that current systems cannot reliably deliver end-to-end without substantial human oversight. The task involves nuanced understanding of student circumstances that AI struggles to capture accurately.
Task automatabilityclaude-sonnet-52/5Generic academic advising chatbots can handle some routine curriculum questions, but personalized career advising drawing on a student's specific goals, faculty relationships, and program nuances still requires human judgment and rapport that current AI cannot replicate end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5Academic advising in postsecondary institutions often has institutional and accreditation requirements that a human advisor (faculty or professional staff) must perform or co-sign; liability for poor career guidance, student outcomes, and institutional relationships create strong friction against full automation.
Adoption barriersclaude-sonnet-53/5No strict licensing requirement for academic advising, but institutional norms, accreditation expectations, and student preference for a real faculty mentor create meaningful friction against pure AI substitution.
Cost vs. human wageclaude-haiku-4-5-202510012/5An AI system capable of personalized, reliable advising would require significant integration, customization to institutional curricula, and human oversight to catch errors; these setup and ongoing costs would be substantial relative to a faculty advisor's time on individual students.
Cost vs. human wageclaude-sonnet-52/5While chatbot infrastructure is cheap, effective advising still requires human oversight and follow-up, so all-in costs remain comparable to or only modestly less than a faculty advisor's marginal time.
Technical feasibility todayclaude-haiku-4-5-202510012/5Chatbots and career-advice systems exist but perform narrowly and with material limitations in personalization and contextual understanding. No deployed product reliably handles the full scope of academic and vocational advising in postsecondary settings; products function mainly as supplementary information tools rather than genuine advisors.
Technical feasibility todayclaude-sonnet-52/5Some universities deploy AI advising chatbots for basic FAQ-type curriculum questions, but no deployed product reliably handles nuanced career and academic guidance for a niche field like forestry/conservation science.

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

23

CI 21–25 · exposure 25 · augmentation 75 · importance 3.8/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Academic institutions and field researchers have adopted AI for literature search, statistical assistance, and writing support, but very limited deployment of AI-driven research production in peer-reviewed journals. Adoption remains experimental and supplementary, not transformational.
Sector adoption velocityclaude-sonnet-52/5Academia adopts AI tools for writing and literature review at a moderate pace, but higher education and scientific research remain conservative sectors with slow institutional and cultural adoption for core research tasks.
Augmentation potentialclaude-haiku-4-5-202510014/5AI substantially augments researcher productivity through literature synthesis, data visualization, statistical scripting assistance, and manuscript drafting, enabling faster iteration and broader literature engagement while the human researcher remains in control of hypothesis, methodology, and interpretation.
Augmentation potentialclaude-sonnet-54/5AI significantly assists with literature reviews, statistical analysis, drafting manuscripts, and identifying research gaps, meaningfully boosting researcher productivity while the human remains the primary investigator.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can assist with literature review, data analysis, and draft generation, the creative synthesis, novel hypothesis formation, and interpretative judgment central to original research are beyond current AI capability. Publication-quality research requires domain expertise, methodological rigor, and original insight that AI cannot currently produce end-to-end at the 50% time-saving threshold.
Task automatabilityclaude-sonnet-52/5AI can assist with literature review, data analysis, and drafting, but original field research (e.g., forest plot studies, experiments) and generating novel scientific insight require human expertise, physical fieldwork, and judgment that current AI cannot replace end-to-end.'
Adoption barriersclaude-haiku-4-5-202510014/5Strong barriers exist: institutional authorship norms require human accountability, peer review and editorial gatekeeping mandate expert human judgment, professional credentials and tenure depend on demonstrable original contribution, and liability for incorrect research findings falls on the institution and researcher, not an AI vendor.
Adoption barriersclaude-sonnet-54/5Academic publishing requires named human authorship, peer review, and institutional accountability; journals and universities have strict norms against AI-generated research without human oversight and verification.
Cost vs. human wageclaude-haiku-4-5-202510011/5The human cost of a postsecondary researcher (fully loaded: $80k–150k+ annually) is far lower than the combined cost of AI inference, domain-specific fine-tuning, human review, and liability for erroneous findings. Research integrity cannot be achieved cheaply via AI alone.
Cost vs. human wageclaude-sonnet-52/5AI tools reduce costs for literature review and drafting portions, but the core research (fieldwork, data collection, peer-reviewed analysis) still requires expensive human expert labor, keeping overall costs comparable to human-led research.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed product reliably conducts original research across the forestry and conservation sciences independently. AI tools can draft sections and organize citations, but no production system handles the full research lifecycle—problem formulation, experimental design, data collection, analysis, and novel interpretation—with the reliability required for peer review.
Technical feasibility todayclaude-sonnet-52/5Products like AI writing assistants and literature-search tools exist and are used by researchers, but no deployed system independently conducts forestry/conservation research and publishes original findings reliably.

Participate in student recruitment, registration, and placement activities.

21

CI 11–30 · exposure 13 · augmentation 50 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Higher education adoption of automation in recruitment and placement remains cautious; while registration systems are digitized, the relational core of recruitment and placement is defended by institutional inertia and professional norms favoring human faculty involvement.
Sector adoption velocityclaude-sonnet-52/5Higher education administrative functions adopt AI slowly outside of chatbots for FAQs; recruitment and placement still rely heavily on human advisors and personal outreach.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist faculty by automating data management, generating outreach templates, and identifying placement opportunities, but the task fundamentally depends on faculty judgment, relationship-building, and institutional knowledge that AI augments rather than replaces.
Augmentation potentialclaude-sonnet-53/5AI can help draft recruitment materials, manage CRM data, or answer routine student queries, providing moderate assistance to faculty engaged in these activities.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can help draft recruitment materials and manage registration data, the core requirement of genuine student interaction, relationship-building, and personalized placement requires human judgment and presence. Current systems cannot autonomously perform the full recruitment-to-placement pipeline with the interpersonal authenticity demanded in academic contexts.
Task automatabilityclaude-sonnet-51/5This task involves in-person events, personal advising, and relationship-building that cannot be end-to-end automated by current AI systems; only minor sub-components like scheduling or drafting emails could be assisted.
Adoption barriersclaude-haiku-4-5-202510014/5Strong adoption barriers exist: institutional practice expects faculty-led recruitment and advising; legal and ethical requirements around educational equity and accreditation emphasize human judgment; liability concerns in student placement; and organizational culture values direct faculty-student relationships in academic advising.
Adoption barriersclaude-sonnet-53/5No strict licensing requirement, but institutional policy, personal advising relationships, and accreditation/administrative processes create moderate friction against full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI tools for partial tasks (email drafting, data entry) reduce some overhead, but the labor cost of faculty involvement in recruitment and placement mentoring remains substantial relative to the narrow cost savings from automation of administrative subtasks.
Cost vs. human wageclaude-sonnet-52/5While AI tools could cheaply handle some communications, the bulk of recruitment/placement requires human judgment and interpersonal engagement, so overall cost is not meaningfully reduced versus faculty time.
Technical feasibility todayclaude-haiku-4-5-202510012/5Limited production systems exist for isolated components (e.g., chatbots for initial inquiries, registration form automation), but no mature end-to-end product reliably handles recruitment outreach, relationship development, and placement matching with the contextual awareness required in academic settings.
Technical feasibility todayclaude-sonnet-51/5No deployed AI product performs student recruitment, registration, and placement activities for a postsecondary program; these remain human-run administrative and relational functions.

Initiate, facilitate, and moderate classroom discussions.

19

CI 9–30 · exposure 20 · augmentation 50 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Higher education remains a laggard sector in AI displacement; faculty roles are protected by tenure, union agreements, and institutional culture emphasizing human mentorship. No evidence of production-scale AI replacing classroom instruction in postsecondary settings.
Sector adoption velocityclaude-sonnet-52/5Higher education adoption of AI for direct classroom facilitation is nascent; most current use is for content generation or administrative support rather than live discussion moderation.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist instructors by preparing discussion prompts, transcribing and summarizing discussions, or flagging engagement patterns, meaningfully boosting their preparation and post-class analysis. However, the core facilitation remains human-led.
Augmentation potentialclaude-sonnet-53/5AI can help instructors generate discussion questions, summarize readings, or analyze participation patterns, meaningfully aiding preparation even though it doesn't replace real-time facilitation.
Task automatabilityclaude-haiku-4-5-202510012/5AI can generate discussion prompts and summarize key points, but cannot replicate the real-time interpersonal dynamics, classroom presence, and adaptive facilitation that define effective moderation. Core elements—drawing out quiet students, managing group conflict, reading social cues—remain beyond current automation.
Task automatabilityclaude-sonnet-52/5Live classroom facilitation requires real-time reading of student engagement, adaptive questioning, and interpersonal presence that current AI cannot replicate end-to-end, though AI can help prep discussion prompts.
Adoption barriersclaude-haiku-4-5-202510015/5Educational institutions and accreditors require human faculty for pedagogical design and direct student engagement; many universities have contractual and regulatory requirements that faculty conduct classes. Removing the human instructor from classroom discussion raises legal, accreditation, and liability concerns.
Adoption barriersclaude-sonnet-53/5No formal licensing barrier exists, but institutional norms, accreditation expectations, and student preference for instructor-led discussion create real friction against substitution.
Cost vs. human wageclaude-haiku-4-5-202510011/5A full-time faculty instructor moderating discussions costs less per session than integrating, maintaining, and oversighting an AI system with sufficient maturity and reliability to replace them. The overhead of ensuring quality and safety outweighs labor savings.
Cost vs. human wageclaude-sonnet-52/5Since AI cannot yet substitute for the live facilitation itself, any cost comparison favors the human faculty member who is already compensated for broader teaching duties.
Technical feasibility todayclaude-haiku-4-5-202510012/5Experimental tools exist for generating discussion questions and tracking participation, but no deployed system reliably initiates, facilitates, and moderates live classroom discussions. Current products lack the social reasoning and real-time responsiveness required in a classroom setting.
Technical feasibility todayclaude-sonnet-51/5No deployed product autonomously runs live in-person seminar discussions for postsecondary forestry/conservation courses; AI discussion tools remain limited to asynchronous or text-based settings.

Perform administrative duties, such as serving as department head.

17

CI 0–34 · exposure 20 · augmentation 50 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Higher education adoption of AI for core administrative functions remains slow; most institutions use isolated tools rather than integrated systems, and faculty governance structures create organizational friction against full automation of department leadership.
Sector adoption velocityclaude-sonnet-51/5Higher education administration is slow-moving and highly institutionalized, with governance roles resistant to AI substitution.
Augmentation potentialclaude-haiku-4-5-202510013/5AI tools can usefully assist with meeting scheduling, document drafting, and data aggregation for reports, reducing routine administrative burden while the department head retains decision authority on personnel and budgeting.
Augmentation potentialclaude-sonnet-53/5AI tools can help draft reports, schedules, budgets, or correspondence, easing some administrative burden even though the core leadership role remains human.
Task automatabilityclaude-haiku-4-5-202510013/5Selected administrative tasks like scheduling, email triage, and document organization could achieve 50% time savings through AI, but core duties—hiring decisions, budget prioritization, faculty evaluations—require human judgment and accountability that current AI cannot reliably handle end-to-end.
Task automatabilityclaude-sonnet-51/5Departmental leadership involves relational judgment, personnel decisions, budget negotiation, and institutional politics that current AI cannot execute end-to-end.atable
Adoption barriersclaude-haiku-4-5-202510014/5Department head roles carry legal and fiduciary responsibility for personnel decisions, budget allocation, and institutional policy; most institutions require a credentialed human to sign off on hiring, evaluations, and resource decisions, creating strong adoption barriers.
Adoption barriersclaude-sonnet-55/5Department head roles require institutional authority, tenure/faculty governance status, and accountability that only a qualified human faculty member can hold.
Cost vs. human wageclaude-haiku-4-5-202510012/5Current AI services for administrative support (virtual assistants, scheduling tools) still require significant human oversight and integration cost; the loaded salary of a department head is high enough that AI cost savings remain modest relative to total department overhead.
Cost vs. human wageclaude-sonnet-51/5There is no AI substitute performing this role, so cost comparison favors the human by default since AI cannot deliver the output.
Technical feasibility todayclaude-haiku-4-5-202510012/5Administrative support tools exist for specific subtasks (calendar management, meeting scheduling), but no deployed system reliably handles the full suite of department head responsibilities, which involve sensitive HR, legal, and fiduciary decisions requiring human discretion.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs department head duties autonomously; this remains firmly a human administrative role.

Provide professional consulting services to government or industry.

16

CI 6–25 · exposure 13 · augmentation 63 · importance 2.8/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5While forestry and conservation organizations use AI tools for data analysis, adoption of AI for autonomous consulting delivery is minimal. Government procurement and industry risk management strongly favor human-led consulting relationships, limiting velocity of AI replacement in this domain.
Sector adoption velocityclaude-sonnet-52/5Higher education and specialized environmental/forestry consulting sectors show slow, uneven AI adoption compared to fast-moving digital-native industries.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can meaningfully assist by processing large datasets, generating literature reviews, modeling scenarios, and drafting preliminary analyses—raising consulting productivity. However, the human consultant remains essential for client interaction, judgment calls, and accountability.
Augmentation potentialclaude-sonnet-54/5AI can meaningfully assist with literature synthesis, data analysis, report drafting, and scenario modeling, significantly boosting the consultant's productivity while they retain final judgment.
Task automatabilityclaude-haiku-4-5-202510011/5Professional consulting inherently requires domain expertise, judgment, negotiation, and client relationship management that current AI cannot perform end-to-end. While AI can assist with research and analysis, the core consulting work—advising government or industry on forestry/conservation strategy—demands human authority and accountability that AI systems cannot assume.
Task automatabilityclaude-sonnet-52/5This task requires field-specific expertise, judgment on unique local ecological/regulatory contexts, and often on-site assessment or stakeholder negotiation that AI cannot fully replicate end-to-end today.
Adoption barriersclaude-haiku-4-5-202510015/5Government and industry clients typically require a licensed or credentialed professional to formally deliver consulting advice and bear responsibility for recommendations. Professional liability, regulatory compliance, and the need for human expert sign-off create hard legal and institutional barriers to automation.
Adoption barriersclaude-sonnet-54/5Consulting often requires credentialed expertise, professional reputation, and accountability for advice given to government/industry, creating liability and trust barriers that favor human experts.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI can reduce research and analysis costs within consulting workflows, but the consulting service itself—client engagement, strategy development, delivery of recommendations—must still be performed by a human expert. Total cost per engagement would remain dominated by human expert time.
Cost vs. human wageclaude-sonnet-52/5While AI can cut research and drafting time, the core value of consulting is expert judgment and liability-bearing recommendations, so oversight and validation costs keep the all-in cost comparable to or only modestly below human expert fees.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs professional consulting services autonomously. Consulting requires understanding client context, regulatory landscape, site-specific conditions, and providing recommendations clients will act upon—tasks where liability and trust make autonomous AI deployment impractical.
Technical feasibility todayclaude-sonnet-52/5AI tools can assist with literature review, data analysis, or drafting reports, but no deployed product independently delivers professional consulting advice to government/industry clients in this specialized domain.

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

13

CI 5–21 · exposure 13 · augmentation 50 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Universities remain highly resistant to automation of core pedagogical and research supervision functions; adoption is concentrated in administrative tasks only (scheduling, grading rubrics), not supervisory responsibility itself. Sector digitization on this specific task remains very low.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools for writing and research assistance but formal supervisory responsibilities remain firmly human-led with slow institutional change.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist supervisors by automating progress tracking, summarizing research outputs, flagging scheduling conflicts, and suggesting feedback on written work, moderately raising the efficiency of administrative supervision work while the faculty member retains all judgment and accountability.
Augmentation potentialclaude-sonnet-53/5AI can help faculty by summarizing student progress, assisting in feedback drafting, or supporting research design and literature review, but cannot replace the personalized mentoring itself.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can assist with scheduling, progress tracking, and documentation review, the core supervisory activities—mentoring, evaluating research direction, addressing interpersonal issues, and making judgment calls on student advancement—require human oversight and accountability. Current AI systems cannot autonomously replicate the full responsibility chain or the adaptive feedback needed in mentoring relationships.
Task automatabilityclaude-sonnet-51/5Supervising students' research, internships, and teaching requires ongoing personalized mentorship, evaluation of judgment, and relationship-building that current AI cannot replicate end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5Academic institutions have strong regulatory and ethical requirements that a faculty member (often with specific credentials) must directly supervise student research, internships, and teaching. Accreditation bodies, funding agencies (NSF, NIH), and institutional policies mandate human supervisory authority that cannot be delegated to an automated system.
Adoption barriersclaude-sonnet-54/5Institutional accreditation, degree-granting requirements, and academic credentialing standards require a qualified faculty member to supervise and sign off on student work.
Cost vs. human wageclaude-haiku-4-5-202510011/5Supervision in academic settings generates institutional liability and requires continuous human oversight; the cost of AI systems adequate to handle edge cases (research misconduct, student welfare, conflict resolution) plus human oversight would likely exceed the cost of direct faculty supervision.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute performing this supervisory role, so cost comparison favors the human by default.
Technical feasibility todayclaude-haiku-4-5-202510012/5No production systems reliably perform end-to-end supervision of academic work. AI can augment administrative aspects (scheduling, progress documentation), but systems lack the judgment, relationship continuity, and institutional authority to supervise research quality or guide student development at scale in real universities.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs supervisory mentorship of graduate/undergraduate research or internships; this remains a research-stage aspiration at best.

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

9

CI 0–18 · exposure 8 · augmentation 50 · importance 3.3/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Academic institutions operate on consensus-driven, relationship-heavy governance models with deep human and social requirements. Adoption of AI agents for committee work is negligible; institutions show no signal of replacing human committee members with AI systems.
Sector adoption velocityclaude-sonnet-51/5Higher education governance and committee structures are slow-moving, tradition-bound, and show minimal AI displacement in this specific function.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist a faculty member preparing for committees by drafting agenda summaries, highlighting policy implications, or synthesizing document background. However, the assistance remains confined to preparation; the deliberative and representative work itself remains firmly human-driven.
Augmentation potentialclaude-sonnet-53/5AI can help prepare meeting materials, summarize policy documents, draft minutes, or analyze data for committee decisions, offering moderate support to the human committee member.
Task automatabilityclaude-haiku-4-5-202510012/5Committee work involves reading documents, drafting memos, and summarizing positions—tasks AI can partially assist with. However, the core activity requires real-time deliberation, political judgment, institutional knowledge, and consensus-building that AI cannot meaningfully perform end-to-end. AI might automate 20–30% of preparation work, falling well short of the 50% threshold.
Task automatabilityclaude-sonnet-51/5Committee service requires interpersonal negotiation, institutional judgment, political awareness, and representation of departmental interests that AI cannot perform end-to-end.hoe
Adoption barriersclaude-haiku-4-5-202510015/5Institutional policies require a human faculty member to sit on committees as a representative actor accountable for decisions. Governance structures, accreditation bodies, and employment law typically mandate that tenured or designated faculty hold these roles and bear responsibility for votes and commitments.
Adoption barriersclaude-sonnet-54/5Institutional governance, shared governance norms, and faculty bylaws typically require actual faculty members to serve on committees, creating strong structural barriers to automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5While AI text generation is cheap, integrating it into genuine committee work requires human oversight of every contribution and decision. The overhead of human review and validation approaches or exceeds the cost of the human simply doing the work themselves.
Cost vs. human wageclaude-sonnet-51/5There is no AI substitute performing this role, so cost comparison favors the human by default since AI cannot deliver the output at all.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs committee participation autonomously. AI can draft text or analyze documents, but cannot represent a faculty member in deliberation, vote, or sign policy decisions. This remains research-stage.
Technical feasibility todayclaude-sonnet-51/5No deployed AI product substitutes for a human serving as a committee member; this remains entirely a human governance function.

Maintain regularly scheduled office hours to advise and assist students.

7

CI 4–11 · exposure 0 · augmentation 50 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Higher education remains highly conservative on core instructional and advising functions; adoption of AI for direct student advising is minimal and limited to peripheral scheduling or FAQ tasks, not the substitution of office hours themselves.
Sector adoption velocityclaude-sonnet-52/5Higher education is a slower-adopting sector for replacing personal advising functions, though AI chatbots for administrative FAQs are seeing some uptake.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist by pre-screening student questions, drafting responses to common inquiries, or organizing appointment logistics, but the core advisory conversation remains human-led. Moderate augmentation potential for workflow efficiency.
Augmentation potentialclaude-sonnet-53/5AI can help by answering routine student questions, scheduling, or providing supplementary resources, but the core advising interaction still depends on human judgment and presence.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires real-time, personalized human interaction involving active listening, judgment of student needs, and relationship-building—core elements that current AI cannot substitute end-to-end. Office hours are inherently synchronous advisory sessions that depend on human presence and professional judgment.
Task automatabilityclaude-sonnet-51/5This task requires a physical/scheduled human presence and personal relationship-building for advising; AI cannot fulfill the institutional and interpersonal role of holding office hours.rounded 'presence' cannot be automated end-to-end today.
Adoption barriersclaude-haiku-4-5-202510015/5Institutional and regulatory expectations, accreditation standards, and implicit faculty employment contracts typically require faculty to maintain regular student contact hours. Legal and professional norms treat student advising as a non-delegable duty of the instructor.
Adoption barriersclaude-sonnet-54/5Academic advising, mentorship, and student support are tied to faculty roles, institutional accreditation, and personal accountability, creating strong organizational and relational barriers to substitution.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI scheduling tools and FAQ assistants are cheap, but they do not replace the full office-hours function; true replacement would require a human equivalent (salary ~$80k+), making any partial AI augmentation still far more expensive than the marginal human cost.
Cost vs. human wageclaude-sonnet-52/5AI chat tools are cheap per interaction, but they don't replace the credentialed advising function, so a true cost comparison for equivalent output favors the human role remaining necessary.
Technical feasibility todayclaude-haiku-4-5-202510011/5While chatbots can answer FAQs, no deployed system reliably handles the nuanced, personalized advising and support that constitutes actual office hours. Scheduling and basic Q&A exist, but the advisory relationship itself remains a human function in production.
Technical feasibility todayclaude-sonnet-51/5No deployed product substitutes for a professor's scheduled office hours; chatbots exist for FAQs but not for the actual advising relationship required here.

Collaborate with colleagues to address teaching and research issues.

4

CI 0–7 · exposure 0 · augmentation 38 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Academic institutions remain slow to adopt AI in core governance and collegial decision-making processes; such collaboration remains firmly in human hands across postsecondary settings.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI tools slowly for collaborative/administrative academic work, with pilots for research assistance but little penetration into collegial decision-making processes.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could potentially assist by summarizing prior discussions or flagging literature relevant to research issues, but the core collaborative work of addressing teaching and research problems requires human judgment, buy-in, and interpersonal dynamics that AI cannot meaningfully augment.
Augmentation potentialclaude-sonnet-53/5AI can help by summarizing research literature, drafting grant sections, or organizing meeting notes, meaningfully aiding preparation for and follow-up on collaborative discussions.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires nuanced interpersonal negotiation, contextual judgment about research priorities, and relationship-building among colleagues. Current AI systems cannot autonomously engage in genuine collaborative problem-solving with human stakeholders in ways that would achieve meaningful outcomes.
Task automatabilityclaude-sonnet-51/5This is an interpersonal, collegial coordination activity requiring shared professional judgment, trust-building, and institutional context that current AI cannot substitute for end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Academic governance structures, tenure and research autonomy norms, and the requirement for human judgment and accountability mean that collaborative decisions on teaching/research must involve human faculty members with institutional authority and stake in outcomes.
Adoption barriersclaude-sonnet-54/5Faculty governance, academic norms, and tenure/promotion structures require genuine human collegial engagement, creating strong organizational and cultural barriers to automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5Any AI assistance would require substantial human oversight and validation; the combined cost of AI plus required human review would exceed the cost of direct human collaboration.
Cost vs. human wageclaude-sonnet-51/5There is no AI substitute performing this task, so cost comparison favors the human doing the actual collaborative work; AI cannot replace the interpersonal exchange.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI system can reliably conduct authentic colleague collaboration to resolve institutional teaching and research challenges. This requires sustained interaction, trust-building, and accountability that exceed current AI capabilities.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs collegial collaboration on teaching/research issues; at best AI tools assist with scheduling or drafting shared documents, not the collaboration itself.

Act as advisers to student organizations.

4

CI 0–7 · exposure 0 · augmentation 38 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Higher education has been slow to automate advising roles; student organizations rely on continuity, trust, and human judgment that institutions are reluctant to displace, and no measurable adoption of AI advisors for student organizations is evident in the sector.
Sector adoption velocityclaude-sonnet-52/5Higher education administrative and mentorship roles show slow AI adoption, especially for interpersonal advising functions like this one.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could potentially assist with administrative tasks (scheduling meetings, tracking organization records, suggesting resources), but the core advising work—listening, mentoring, exercising judgment—is difficult for AI to augment meaningfully without raising questions about the quality and integrity of the advisory relationship.
Augmentation potentialclaude-sonnet-53/5AI can help with scheduling, drafting communications, budgeting suggestions, or event planning support, but the core advisory/mentorship relationship remains human-driven.
Task automatabilityclaude-haiku-4-5-202510011/5Advising student organizations requires nuanced interpersonal judgment, mentorship, understanding of individual student needs and organizational dynamics, and the ability to guide students through complex social and developmental challenges. Current AI cannot reliably replicate the contextual understanding and relational trust necessary for meaningful advisement.
Task automatabilityclaude-sonnet-51/5Advising student organizations requires ongoing relationship-building, mentorship, judgment on interpersonal and logistical matters, and institutional presence that current AI cannot substitute for end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Educational institutions have strong legal and fiduciary responsibilities to students; advisors are expected to exercise judgment, duty of care, and accountability that cannot be delegated to AI. Student welfare expectations create hard barriers to automation.
Adoption barriersclaude-sonnet-54/5Universities typically require a designated faculty/staff adviser for liability, institutional accountability, and mentorship purposes, creating strong organizational and policy barriers to automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5An AI system adequate to advising student organizations would require substantial customization, integration with institutional systems, and human oversight to manage liability and ensure quality, making it more expensive than the human labor it might replace.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute performing this task, so cost comparison favors the human by default.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI product exists that can independently serve as a reliable advisor to student organizations in production environments; this task is inherently relational and requires sustained human presence, accountability, and discretionary judgment that AI systems do not possess.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs the role of a student organization adviser; this remains a human relational and administrative function.

Participate in campus and community events.

4

CI 0–7 · exposure 0 · augmentation 13 · importance 2.9/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5There is no meaningful AI adoption for this task because it is inherently human-centric. Sectors where participation in events occurs (academia, community institutions) have not and cannot adopt AI substitutes for physical human attendance.
Sector adoption velocityclaude-sonnet-52/5Higher education adopts AI moderately for administrative and instructional support, but physical community engagement tasks see essentially no AI-driven displacement.
Augmentation potentialclaude-haiku-4-5-202510011/5AI offers minimal assistance for attending and participating in events. While AI could support preparation (e.g., scheduling suggestions), it cannot augment the core act of participation itself.
Augmentation potentialclaude-sonnet-52/5AI can help draft talking points, event summaries, or promotional materials, but offers minimal assistance to the actual act of attending and participating in events.
Task automatabilityclaude-haiku-4-5-202510011/5Participating in campus and community events requires human presence, social interaction, relationship building, and contextual judgment. Current AI systems cannot physically attend events or meaningfully substitute for the human networking and community engagement that defines this task.
Task automatabilityclaude-sonnet-51/5Physical attendance, networking, and representation at campus and community events require embodied human presence and social judgment that current AI cannot perform.It is inherently non-automatable end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Strong barriers exist: institutional and professional norms require actual human presence at community events, and the task explicitly requires human participation as part of faculty duties and professional expectations.
Adoption barriersclaude-sonnet-54/5Community and campus engagement typically requires an identifiable human representative with institutional standing, relationships, and accountability, creating strong organizational and social barriers to substitution.
Cost vs. human wageclaude-haiku-4-5-202510011/5AI cannot perform this task, making cost comparison inapplicable. Any AI-supported alternative (e.g., virtual messaging) would not be a true substitute for participation.
Cost vs. human wageclaude-sonnet-51/5There is no AI substitute performing this task, so no meaningful cost comparison exists; the human cost is the only viable option.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI product can autonomously participate in campus or community events in any meaningful way. The task fundamentally requires human embodiment and social presence.
Technical feasibility todayclaude-sonnet-51/5No deployed AI product substitutes for a professor's physical participation in events; this remains entirely outside current product capabilities.

Supervise students' laboratory or field work.

3

CI 0–5 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Educational institutions, especially in postsecondary forestry and conservation, have not adopted AI systems for actual student supervision; adoption remains minimal due to safety, liability, and accreditation requirements that favor human oversight.
Sector adoption velocityclaude-sonnet-51/5Postsecondary field-based science instruction is a low-digitization, physical-presence-dependent sector with minimal AI adoption for this specific task.
Augmentation potentialclaude-haiku-4-5-202510012/5AI might assist with limited tasks like data logging or preliminary safety checklists, but the core supervisory work—observing, correcting, and safeguarding students—resists meaningful augmentation since the human instructor must maintain full situational awareness and authority.
Augmentation potentialclaude-sonnet-52/5AI can help with pre-lab instructions, safety checklists, or data logging support, but offers little direct augmentation to the act of physically supervising students.
Task automatabilityclaude-haiku-4-5-202510011/5Supervising students' laboratory or field work requires real-time safety monitoring, individual feedback, adaptive instruction, and in-person presence that current AI systems cannot provide. This task fundamentally depends on human judgment and physical oversight that no automated system can meaningfully replace today.
Task automatabilityclaude-sonnet-51/5Supervising live laboratory or field work requires physical presence, real-time safety oversight, and hands-on demonstration that current AI cannot perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Strong legal and regulatory barriers exist: institutions are liable for student safety during lab and field work, and a licensed educator must be physically present and accountable. Educational accreditation and institutional policy mandate human supervision.
Adoption barriersclaude-sonnet-54/5Liability for student safety in field/lab settings, institutional accreditation requirements, and need for qualified human supervisors create strong barriers to automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5Any AI system capable of real-time supervision would require expensive multimodal monitoring, safety systems, and integration; costs would far exceed the loaded wage of a teaching assistant or instructor actually present in the field.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute for physical supervision, so any comparison shows AI as effectively non-functional relative to human cost.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably supervises hands-on laboratory or field work autonomously; this remains solely within human instructor domains in production educational settings. The task involves dynamic, context-dependent safety and pedagogical decisions that exceed current AI capability.
Technical feasibility todayclaude-sonnet-51/5No deployed products supervise students in physical field or lab settings involving safety-critical outdoor or hands-on tasks; this remains research-stage at best.

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.