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Anthropology and Archeology Teachers, Postsecondary

Scrub through 157years of this role's history, from when it first emerged, through every wave of technology that reshaped it, to the cited projections for where it's heading next.

2026drag to travel through time
19001925195019752000now
2026
Known today as Anthropology and Archeology Teachers, Postsecondary (BLS SOC 25-1061)
Latest actual · 2024
7K
BLS OEWS May 2024, sourced from O*NET which reflects the BLS establishment-survey figure. The lower count relative to 2023 likely reflects both survey-year variation in this small occupation (BLS notes sampling error is higher for small occupational categories) and continued structural contraction in tenure and tenure-track lines. The American Academy of Arts and Sciences 2023 HDS-4 survey found 4,910 full- and part-time faculty across 448 departments, suggesting the OEWS count may include some off-campus appointment types not captured by the departmental survey.
Latest actual · 2024
$95,770
BLS OEWS May 2024, sourced from O*NET. The jump from the 2023 figure likely reflects the well-documented survey volatility for small occupational categories (n approx. 6,500) as well as possible compositional shifts in who is captured by the establishment survey in a given year. Real base year is 2024.
Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.
Beat · 2025

The Society for American Archaeology holds its first dedicated AI in Archaeology symposium at its annual meeting in Denver (2025). The American Anthropological Association's AI Task Force publishes "Artificial Intelligence and the Future of Anthropological Research." Both organizations simultaneously document AI tools reducing data-processing burdens by 40-60% in documentation-intensive research workflows and flag new ethical responsibilities around AI applied to Indigenous cultural materials, human skeletal remains, and community-sourced oral histories. The discipline reaches a bifurcation: faculty who adopt AI field-methods tools and qualitative coding assistants become measurably more productive and better positioned in a competitive job market; those who do not adapt face growing pressure from AI-augmented peers.

Tools of the era

The tools that defined the work

Select an era to see how it reshaped the work.

  • Field notebook, hand-drawn stratigraphy, glass-plate photography

    The foundational toolkit of the anthropology and archaeology field practitioner was purely manual: field notebooks for recording observations, hand-drawn stratigraphic profiles and artifact sketches, and glass-plate photography for site and ethnographic documentation from the 1880s onward. Lewis Henry Morgan's systematic kinship charts (1871) and Franz Boas's physical measurement calipers and anthropometric data tables were the quantitative infrastructure of the era. Teaching these methods meant transmitting embodied craft knowledge through apprenticeship: working alongside a senior researcher in the field or museum laboratory until the student developed independent observational judgment. No mechanical device mediated the core intellectual work.

    Work toolChanging equipment

  • Mimeograph course materials, punch-card statistical computing, radiocarbon dating (1949)

    Willard Libby's radiocarbon dating method, published in 1949 and receiving the Nobel Prize in 1960, transformed the chronological infrastructure of archaeology teaching by giving faculty and students access to absolute dates for the first time. What had been relative chronologies based on stratigraphy and typology could now be anchored in calendar years. For anthropology teachers, the arrival of punch-card statistical computing in the late 1950s and early 1960s enabled quantitative analysis of large ethnographic and archaeological datasets that had previously required weeks of manual tabulation. Mimeograph machines and later photocopiers transformed the preparation and distribution of course materials, separating the intellectual work of syllabus creation from the physical labor of reproduction.

    Punch-card systemsBatch accounting

  • Desktop computers, SPSS and SAS statistical software, photocopy course packs

    Desktop computers arrived in anthropology and archaeology departments in the late 1970s and early 1980s, with SPSS (Statistical Package for the Social Sciences) becoming the dominant tool for quantitative analysis of survey data, skeletal measurements, and artifact assemblage statistics. Word processors replaced typewriters for writing, enabling easier manuscript revision and syllabus updating. Photocopy course packs replaced mimeograph materials and allowed instructors to build semester-length reading collections from journal articles and book chapters. The introduction of scanning electron microscopy into university labs enabled artifact surface analysis that opened new research questions in lithic and ceramic studies. These tools accelerated research output but did not fundamentally change the structure of teaching: lecture, seminar, lab, and fieldwork remained the core pedagogical forms.

    Work toolChanging equipment

  • GIS (ArcGIS), the World Wide Web, ATLAS.ti and NVivo qualitative analysis software

    Geographic Information Systems entered archaeology curricula in the early 1990s: the search results note that "the 1990s will probably be remembered in the history of archaeology as the age of GIS." ArcGIS enabled spatial analysis of site distributions, survey data, and cultural landscape patterns that had previously required manual drafting. The World Wide Web (from 1993) transformed access to scholarly literature, archaeological databases, and digital museum collections. ATLAS.ti (1993) and NVivo (1999) introduced computer-assisted qualitative data analysis, allowing ethnographic field notes and interview transcripts to be coded and analyzed systematically rather than exclusively by hand. For anthropology faculty, these tools widened the research questions addressable within a standard faculty workload without fieldwork travel costs and began shifting the teaching of methods from craft apprenticeship toward documented, software-mediated workflows.

    Effect on the work

    GIS and qualitative data analysis software reduced the time required for the data-organization phases of research without replacing the interpretive judgment that constitutes the scholarly contribution. The effect was productivity gain within existing employment levels, not displacement of faculty positions.

    Work toolChanging equipment

  • LiDAR remote sensing, drone photogrammetry, Agisoft Metashape structure-from-motion

    Airborne LiDAR (Light Detection and Ranging) became accessible to archaeology faculty through federally funded data repositories (OpenTopography, launched 2009) and through NSF-supported field projects in the 2010s, enabling detection of ancient settlement patterns and earthworks hidden under dense vegetation. The rediscovery of the Maya metropolis of Caracol in Belize via LiDAR in 2010 was widely covered and brought the technology to mainstream disciplinary attention. Drone photogrammetry, accelerated by the commercial drone market after 2013, and Agisoft Metashape structure-from-motion software made high-resolution 3-D site documentation possible within a field day rather than a field season. These tools entered undergraduate field school curricula at major programs in the early 2010s and are now standard at Arizona State, UC Santa Barbara, and other large field-methods programs. The effect on teaching was transformative for the methods curriculum: assignments that had previously required expensive instrument rental or physical field access could now be completed using publicly available LiDAR datasets and student-accessible drone hardware.

    Effect on the work

    LiDAR and photogrammetry AI reduced site-documentation time by 40-60% for standard excavation workflows (documented by the SAA in 2025), freeing field school time for interpretation and mentorship rather than surveying and drawing. This productivity gain benefited faculty whose research was documentation-intensive.

    Work toolChanging equipment

  • Generative AI (large language models, AI qualitative coding, AI literature synthesis)

    Large language models entered the anthropology and archaeology teaching workflow in 2022-2023 through three channels: (1) AI qualitative coding tools (ATLAS.ti Intentional AI Coding, MAXQDA AI Assist) that reduced first-pass ethnographic transcript coding from weeks to days; (2) AI literature synthesis tools (Elicit, NotebookLM) that compressed systematic literature review time; and (3) general-purpose LLMs (ChatGPT, Claude) used for lecture outline drafting, syllabus scaffolding, and reading list generation. The Federal Reserve FEDS Notes (Timmerman and Xiao, February 2026) identified social sciences among the highest LLM-exposed academic disciplines, and the AAA AI Task Force (2025) acknowledged the dual disruption: AI is transforming the very research methods these faculty teach (ethnographic coding, archaeological prospection, ancient DNA analysis) while simultaneously exposing the text-heavy teaching tasks (lecture preparation, grading, course material drafting) to AI augmentation. Faculty who integrate these tools effectively recover substantial preparation and data-processing time; those who do not face increasing competitive pressure from better-equipped peers and from students whose AI-generated coursework is increasingly difficult to assess.

    Effect on the work

    AI tools have not yet caused measurable displacement in anthropology and archaeology faculty employment. The SAA (2025) and AAA (2025) both frame the current moment as one of augmentation opportunity rather than displacement threat, with the durable core of embodied fieldwork, ethics judgment on culturally sensitive materials, and graduate student mentorship identified as AI-resistant.

    AI audit toolsPattern detection
Projection cone · present → 2034

What credible sources project

Scrub the slider past now to anchor each scenario on the scrubber. The spread is the range of futures credible sources project for this role.

Employment outlook
Projected change in the number of people doing this work.
BLS Occupational Outlook Handbook 2024-34 (Postsecondary Teachers overall)
→ 2034
+7%
BLS OOH projects overall postsecondary teacher employment growing 7 percent from 2024 to 2034, much faster than the average for all occupations (4%). About 114,000 openings for postsecondary teachers are projected each year over the decade, including new positions and replacements for those who retire or leave. The 7% sector-wide projection is notably more optimistic than the occupation-specific 2.7% projection for 25-1061, reflecting that the broader postsecondary sector is growing faster than anthropology and archaeology specifically. The gap is consistent with the documented enrollment trend away from social sciences and humanities toward STEM and professional programs over the past decade.
BLS National Employment Matrix 2024-34
→ 2034
+2.7%
BLS Employment Projections national matrix for 25-1061, 2024-34 cycle. Projects employment growing from 6,500 (2024) to approximately 6,700 (2034), an increase of roughly 200 positions over the decade. The BLS employment projections methodology uses an industry-occupation matrix combined with labor productivity and enrollment demand assumptions. The modest positive projection reflects continued undergraduate demand for anthropology and archaeology courses in the context of stable higher education enrollment and modest retirements from the aging faculty cohort. The projection does not explicitly model the rate of non-tenure-track substitution for tenure-track lines, which could affect the quality and stability of faculty employment without changing the OEWS headcount.
American Academy of Arts and Sciences Humanities Indicators Survey 2023
→ 2027
-5%
The American Academy of Arts and Sciences HDS-4 survey (fall 2023) found that 41% of anthropology department chairs reported experiencing decreases in tenure-line positions over the 2021-2023 period, while only 21% reported increases. Twenty-seven percent anticipated further tenure-track contraction over the next three years, compared to 23% expecting growth. Departments lost an average of 400 faculty over the two-year survey window (2021-2023), with 55% of losses attributed to retirements and 40% of departments making at least one permanent hire for 2023-24. The -5% estimate reflects the structural headwind from continued tenure-line contraction and departmental budget pressure through 2027, partially offset by retirement-replacement hiring. This is a rough conservative scenario from disciplinary survey data rather than a formal econometric projection.
AI task exposure
Share of the role’s tasks that researchers estimate AI can do. This is a measure of task exposure, not a forecast of jobs lost.
Eloundou et al. (2023) — "GPTs are GPTs"
→ 2030
48%
of tasks
GPT-4 task-by-task LLM exposure labeling on O*NET tasks for postsecondary social science teachers. Anthropology and archaeology postsecondary teachers score in the moderate-to-high range for LLM task exposure, with the Federal Reserve FEDS Notes (Timmerman and Xiao, February 2026) placing social sciences among the highest LLM-exposed academic disciplines. The 48% figure represents the estimated share of occupational tasks for this role that have meaningful LLM exposure, consistent with the Fed FEDS Notes upper-middle placement of social science disciplines. The most LLM-exposed tasks are lecture preparation, course material drafting, reading list curation, ethnographic transcript first-pass coding, and literature synthesis. The exposure does not translate linearly to displacement: the fieldwork, ethics guidance, and graduate mentorship tasks that are least LLM-exposed are also the tasks most valued in tenure and promotion decisions. The change percent here represents task exposure share, not projected employment change.
Today, in this role

What's shifting in the work right now

The historical view above shows how this role has moved. This is the present-day detail: which AI tools are picking up which tasks, where the edge still is, and the natural directions this work can grow.

What's changing in your day

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you herePrepare course materials — syllabi, reading lists, homework assignments, and handouts — using ChatGPT Edu or Claude to generate first-draft syllabi and reading list suggestions for standard topics (Introduction to Cultural Anthropology, World Prehistory, Archaeological Theory), then revising with disciplinary expertise to include current excavation reports and ethnographic publications AI training data does not cover, and ensuring field safety protocols and IRB ethics requirements are accurately reflected.

Prepare course materials — syllabi, reading lists, homework assignments, and handouts — using ChatGPT Edu or Claude to generate first-draft syllabi and reading list suggestions for standard topics (Introduction to Cultural Anthropology, World Prehistory, Archaeological Theory), then revising with disciplinary expertise to include current excavation reports and ethnographic publications AI training data does not cover, and ensuring field safety protocols and IRB ethics requirements are accurately reflected.[1],[2]

Where your edge is

AI tools can generate a structurally adequate syllabus for standard anthropology courses in minutes — including weekly topic sequencing, reading categories, and assignment types. The currency gap is the critical editorial challenge: AI training data is sparse on recent excavations (sites reported in American Antiquity 2023-2025 may be absent), ongoing ethnographic fieldwork results, and subdiscipline debates that have shifted in the last 2-3 years. Use AI syllabus drafts as a structural scaffold and invest your preparation time in the reading list selection — ensuring the assigned scholarship reflects the current state of each debate rather than AI's necessarily dated snapshot of disciplinary knowledge. Also check AI-generated syllabi carefully for IRB and field safety protocol accuracy, where errors can have real compliance consequences.

AI is sitting alongside you hereGrade and provide feedback on student work — research papers, artifact analysis reports, ethnographic field journals, site report write-ups — using Gradescope's AI-assisted grouping for structured short-answer assessments (artifact identification questions, site chronology multiple-choice), and Turnitin AI detection to flag likely AI-generated prose in research papers, while providing expert evaluative feedback on the analytical depth of interpretive arguments that only a disciplinary expert can assess.

Grade and provide feedback on student work — research papers, artifact analysis reports, ethnographic field journals, site report write-ups — using Gradescope's AI-assisted grouping for structured short-answer assessments (artifact identification questions, site chronology multiple-choice), and Turnitin AI detection to flag likely AI-generated prose in research papers, while providing expert evaluative feedback on the analytical depth of interpretive arguments that only a disciplinary expert can assess.[7],[1]

Tools picking this up
Where your edge is

Gradescope handles high-volume structured grading (artifact identification quizzes, map questions, site chronology fill-ins) efficiently and is deployed at 3,000+ institutions including those with strong anthropology programs. Turnitin AI detection flags essays where AI-generated prose is probable. The expert grading effort that AI cannot replace: evaluating whether a student's interpretation of an artifact assemblage is contextually sound, whether a student's ethnographic description demonstrates genuine observational depth, and whether a theoretical argument about cultural change applies the relevant framework correctly. Anthropology grading is particularly demanding because the discipline values holistic, contextual interpretation — the kind of evaluation that requires exactly the expert judgment AI currently cannot supply for novel field contexts. Use AI tools to clear the mechanical grading backlog; invest your grading time in the extended written feedback that actually develops anthropological thinking.

AI is sitting alongside you hereConduct and synthesize literature reviews for research proposals and publications — using Elicit to triage 138M+ academic papers, NotebookLM to build structured Q&A over uploaded journal article corpora, and Consensus to categorize findings by directional agreement — then applying disciplinary expertise to identify which AI-synthesized claims reflect genuine scholarly consensus versus contested interpretations in ongoing archaeological or anthropological debates.

Conduct and synthesize literature reviews for research proposals and publications — using Elicit to triage 138M+ academic papers, NotebookLM to build structured Q&A over uploaded journal article corpora, and Consensus to categorize findings by directional agreement — then applying disciplinary expertise to identify which AI-synthesized claims reflect genuine scholarly consensus versus contested interpretations in ongoing archaeological or anthropological debates.[8],[1]

Where your edge is

AI literature synthesis tools deliver genuine time savings for anthropology and archaeology researchers who produce literature-intensive publications and grant applications. Elicit can triage and extract from hundreds of papers in minutes; NotebookLM turns a corpus of uploaded PDFs into a queryable document; Consensus identifies directional agreement across studies. The critical expert step: evaluating whether AI-synthesized conclusions from the archaeological or ethnographic literature reflect actual scholarly consensus or are artifacts of citation patterns — subdisciplines with smaller literatures (e.g., bioarchaeology of a specific geographic region) may have 20-30 key papers total, and AI tools calibrated on larger disciplines will overweight widely-cited but older studies relative to recent fieldwork reports that have revised the picture. Always run AI literature synthesis against your own expert knowledge of the current state of the specific subfield debate.

Where this role is heading

Natural next steps for someone with your foundation: not exits, evolutions.

A direction you could grow

Education Administrators, Postsecondary

Anthropology and archaeology faculty frequently move into department chair, director of undergraduate studies, or associate dean roles — particularly those who have served on curriculum committees, led field school programs that required logistical and compliance coordination, managed NIH/NSF/NEH grant portfolios, or developed institutional partnerships with museums and heritage agencies. Anthropology departments are under pressure from enrollment trends, interdisciplinary restructuring pressures, and the need to articulate the value of field-based education in an AI-disrupted humanities environment; faculty with both disciplinary authority and administrative experience are valued for leadership roles. The CRI increase (+2) reflects that education administration is moderately augmented for data analytics tasks while the strategic judgment and faculty-relations core is durable.

What you'd add
  • · Higher education budget management: faculty line planning, field school financial modeling (equipment, travel, insurance, student fees), and research overhead cost recovery from sponsored projects
  • · Accreditation and curriculum review: HLC and regional accreditor standards for interdisciplinary programs; assessment frameworks for field-based learning outcomes that satisfy both humanities and social science accreditation criteria
  • · Faculty personnel processes: promotion-and-tenure committee leadership for a mixed-methods field where both qualitative ethnographic research and quantitative archaeological science outputs count; academic hiring in a constrained job market
  • · AI governance for humanities and social science departments: developing IRB and institutional policy on AI processing of fieldwork data, archived cultural materials, and student research; faculty professional development planning for AI methods integration
What it takesSome new skills to pick up
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The data behind this timeline

On record since1879
Latest tracked employment6,500 (US, 2024)
Latest median pay$95,770 (2024)
Outlook-5% by 2027 (American Academy of Arts and Sciences Humanities Indicators Survey 2023)
View all 28 cited data points
YearUS employmentMedian annual paySource
190250n/aESTIMATE
1945300n/aESTIMATE
1963n/a$9,470ESTIMATE
19703,500n/aESTIMATE
19854,200n/aESTIMATE
20007,200$56,800BLS-OEWS
20034,690$59,710BLS-OEWS
20044,990$60,860BLS-OEWS
20055,320$60,710BLS-OEWS
20065,040$62,820BLS-OEWS
20075,290$64,530BLS-OEWS
20085,500$67,750BLS-OEWS
20095,880$69,520BLS-OEWS
20105,850$73,600BLS-OEWS
20115,790$75,460BLS-OEWS
20125,690$76,020BLS-OEWS
20135,920$75,930BLS-OEWS
20146,100$74,750BLS-OEWS
20156,000$77,650BLS-OEWS
20165,700$81,350BLS-OEWS
20175,770$81,580BLS-OEWS
20185,890$83,940BLS-OEWS
20195,850$86,220BLS-OEWS
20205,590$89,220BLS-OEWS
20215,010$97,340BLS-OEWS
20224,930$85,000BLS-OEWS
20237,870$87,240BLS-OEWS
20246,500$95,770BLS-OEWS
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