Will AI replace Anthropology and Archeology Teachers, Postsecondary?
In theory, AI could do about 52% of the work in Anthropology and Archeology Teachers, Postsecondary. In practice, as of late 2025, almost no one is actually using it that way — yet.
O*NET-SOC 25-1061
How your 23 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a moderate share of this job's tasks (~52%). By late 2025, real-world AI use had reached about 2% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
Read this as a range, not a verdict
The signals here partly disagree — AI's theoretical reach (~52%) and its real-world use (~2%) tell different stories. AI-risk scores also shift a lot by which model does the rating (2.7%–51.5% in one 2026 study), so this is a direction of travel, not a fixed answer.
See all 23 tasks, ratedBased on real task-level AI scores — click to collapse
- Prepare course materials, such as syllabi, homework assignments, and handouts.
- Maintain student attendance records, grades, and other required records.
- Compile, administer, and grade examinations, or assign this work to others.
- Write grant proposals to procure external research funding and review others' grant proposals.
- Write letters of recommendation for students.
- Compile bibliographies of specialized materials for outside reading assignments.
- Review manuscripts for publication in books and professional journals.
- Conduct research in a particular field of knowledge and present findings in professional journals, books, electronic media, or at professional conferences.
- Keep abreast of developments in the field by reading current literature, talking with colleagues, and participating in professional conferences.
- Prepare and deliver lectures to undergraduate or graduate students on topics such as research methods, urban anthropology, and language and culture.
- Evaluate and grade students' class work, assignments, and papers.
- Advise students on academic and vocational curricula, career issues, and laboratory and field research.
- Plan, evaluate, and revise curricula, course content, and course materials and methods of instruction.
- Supervise undergraduate or graduate teaching, internship, and research work.
- Select and obtain materials and supplies, such as textbooks and laboratory equipment.
- Collaborate with colleagues to address teaching and research issues.
- Perform administrative duties, such as serving as department head.
- Participate in student recruitment, registration, and placement activities.
- Initiate, facilitate, and moderate classroom discussions.
- Supervise students' laboratory or field work.
- Maintain regularly scheduled office hours to advise and assist students.
- Serve on academic or administrative committees that deal with institutional policies, departmental matters, and academic issues.
- Participate in campus and community events.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.