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Political Science Teachers, Postsecondary

Scrub through 179years 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
187519001925195019752000now
2026
Known today as Political Science Teachers, Postsecondary (BLS SOC 25-1065)
Latest actual · 2024
22K
BLS OEWS May 2024 as reported in O*NET 30.3 (accessed June 2026). The occupation employs 12,300 at state and local institutions and 9,400 at private colleges and universities. BLS projects growth to 22,200 by 2034 -- a 2% increase over ten years, substantially below the all-postsecondary-teachers average of 7%, reflecting continued enrollment pressure in political science as a standalone undergraduate major and ongoing shift toward contingent faculty appointments.
Latest actual · 2024
$94,680
BLS OEWS May 2024 median annual wage for SOC 25-1065, sourced from O*NET 30.3. This is the real-base-year anchor for the wage series (2024 dollars). Political science teachers are paid above the all-postsecondary-teachers median of approximately $83,980 for May 2024, reflecting the discipline's concentration in doctoral and research universities where salaries are structurally higher. The substantial gap between this median and the 1970 all-faculty average reflects both real wage growth and the increasing premium on research-intensive faculty appointments.
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 APSA EDUCATE initiative launches in 2025, positioning AI literacy and civic engagement as core disciplinary competencies for political science undergraduates. Simultaneously, the APSA Presidential Task Force on AI (Tucker, Persily et al., 2026) identifies political science as distinctively positioned to contribute to the analysis of AI's effects on democratic governance, elections, disinformation, and administrative state capacity. The task force recommends open-weight AI models for reproducibility of peer-reviewed research -- a direct response to the replication-crisis awareness in the discipline -- and flags the absence of agreed-upon benchmarks for evaluating AI-assisted political science research as an urgent disciplinary gap.

Tools of the era

The tools that defined the work

Select an era to see how it reshaped the work.

  • Seminar + printed journal + library (pre-quantitative era)

    The defining technology of early political science was the German seminar model: a small group of graduate students reading primary texts -- Aristotle, Machiavelli, Hobbes, Locke, constitutions, statutes, diplomatic dispatches -- under a professor's direction, then writing original research. Columbia's School of Political Science, founded in 1880, operated this way. The American Political Science Review, founded 1906, was the key transmission mechanism for research. The library catalogue card, the bound journal run, and the typewriter were the principal tools. Faculty prepared lectures by hand or typewriter, duplicated documents by mimeograph for seminar reading packets, and cited sources through note cards organized in library-style boxes.

    Work toolChanging equipment

  • Survey research + punch-card statistical computing (behavioral revolution)

    The behavioral revolution in political science -- roughly 1945-1970 -- transformed the discipline from a primarily legal and institutional approach into an empirical social science. The Survey Research Center at Michigan (founded 1946) and the Bureau of Applied Social Research at Columbia pioneered large-sample survey methodology. The first National Election Study was conducted in 1948. Faculty who wanted to do quantitative political science needed access to IBM punch-card machines, then early mainframe computers (the IBM 360 arrived at research universities in the late 1960s). Statistical packages like SPSS (first release 1968) and SAS (first release 1972) became the tools of the empirical political scientist. This era split the discipline: quantitative faculty at research universities gained access to expensive computing infrastructure; political theorists, comparativists working on non-democratic cases, and international relations scholars remained in the qualitative seminar tradition.

    Effect on the work

    The behavioral revolution drove a structural hierarchy in the discipline: research universities with computing infrastructure produced empirical work that dominated top journals, while teaching colleges remained qualitative. This infrastructure gap persisted until personal computers democratized statistical analysis in the 1980s.

    Punch-card systemsBatch accounting

  • Xerox photocopier (Xerox 914, 1960) and mimeograph for course materials

    The Xerox 914, introduced in 1960, was the first office machine to make permanent copies on ordinary paper. As prices dropped through the late 1960s, photocopiers spread to university departments and libraries, transforming course preparation. Faculty could now assemble article-length course packets from journal issues without retyping or hand-copying. The mimeograph had preceded the copier for lecture notes and syllabi since the 1920s; the Xerox replaced it for higher-quality reproduction. This era also introduced the course packet -- a stapled bundle of photocopied journal articles and book chapters -- as the standard delivery vehicle for assigned readings in political science seminars, replacing the textbook as the primary content medium at research universities.

    Accounting softwareIntegrated ledgers

  • Personal computer + email + Stata/SPSS on the desktop

    The IBM PC (1981) and Apple Macintosh (1984) brought statistical computing to individual faculty desks for the first time, democratizing quantitative methods across the discipline. Stata (first release 1985) and the desktop version of SPSS brought regression analysis, cross-tabulation, and time-series tools to any faculty office with a PC. Email arrived at most research universities between 1988 and 1993, accelerating collaboration and manuscript exchange. Word processors -- WordPerfect dominated academic writing in the late 1980s, then Microsoft Word -- replaced the typewriter and allowed faculty to revise and reformat manuscripts without retyping. Laser printers (widely available by 1990) produced professional-quality syllabi, handouts, and manuscripts without going through the department secretary.

    Effect on the work

    The personal computer era substantially reduced the labor support infrastructure political science departments had relied on: department secretaries who typed manuscripts, maintained correspondence, and reproduced course materials. The 1980s-90s saw steady reduction in non-faculty department staff as these tasks moved to faculty computers. The flip side: faculty who mastered PC-based statistical analysis could now do empirical research that previously required a computing center with a dedicated programmer.

    Work toolChanging equipment

  • Internet + JSTOR + course management systems (Blackboard, Moodle)

    JSTOR launched in 1995 as a digital archive of academic journals, initially focused on a small set of back-issue runs. By 2003 it had grown to cover most of the major political science journals including APSR, the American Journal of Political Science, and Comparative Political Studies -- making thirty years of literature searchable at the desktop. The internet transformed political science research by making government documents (congressional records, executive orders, treaty texts, international organization publications) freely downloadable, and by enabling access to large datasets (the Correlates of War Project, the Polity IV democracy scores) without institutional data transfers. Course management systems -- Blackboard (widely adopted 1999-2005) and later Moodle -- digitized the syllabus, assignment submission, grade book, and discussion forum. Political science simulations (Model UN, mock Congress) began incorporating online preparation tools.

    Work toolChanging equipment

  • Text-as-data methods (quantitative text analysis, Python/R for political science)

    The 2010s brought a new methodological wave to political science research and teaching: computational text analysis at scale. Justin Grimmer and Brandon Stewart's 2013 paper "Text as Data" laid out a framework for using machine learning on political text corpora -- congressional speeches, party platforms, newspaper editorials, UN resolutions, social media. Python and R became standard research tools at doctoral programs, with packages like tm, quanteda, and stm (Structural Topic Model) enabling analysis of datasets too large for any manual coding effort. APSA began recommending computational methods training as a doctoral competency. For political science teachers, this era meant adding a new strand to methods courses: alongside regression analysis and survey design, faculty were now expected to teach or at least introduce text-as-data tools, creating a substantial professional development demand for a cohort that had been trained before these methods existed.

    Effect on the work

    Text-as-data methods eliminated a large category of research assistant work: hand-coding of political text (scoring the ideology of congressional speeches, categorizing press releases by topic, coding news articles for political bias) shifted from labor-intensive RA projects to automated pipelines. This directly affected the labor demand for political science doctoral students doing RA work, compressing the funding model of some graduate programs.

    Work toolChanging equipment

  • Large language models and AI research tools (Elicit, NotebookLM, ChatGPT, BERTopic)

    The arrival of GPT-3 (2020) and then GPT-4, Claude, and their successors transformed both the research toolkit and the academic integrity landscape for political science faculty. On the research side, tools like Elicit can screen thousands of papers for a systematic review in minutes; NotebookLM synthesizes working-paper corpora; BERTopic and PolitBERT enable topic modeling of congressional speech and social media at scales no human coder could match; ATLAS.ti 24 provides AI-assisted first-pass coding of qualitative interview transcripts. A 2025 study (Tornberg, Social Science Computer Review) documented that LLMs now outperform human expert coders on political social media annotation tasks -- the clearest evidence that a core research task has been substantially automated. On the teaching side, the APSA Presidential Task Force on AI (Tucker, Persily et al., 2026) documented that 61% of political science faculty had experimented with AI tools in course design by spring 2025, but also that policy brief and analytical essay assignments -- the discipline's most common assessment format -- are now among the top-10 AI-generated submission types detected by Turnitin.

    Effect on the work

    LLMs automated a significant share of the RA-level text-coding work that doctoral students had previously performed, compressing graduate research funding models and changing the nature of doctoral training in empirical methods. On the teaching side, the need to redesign AI-resistant assessments added a new professional task that consumed substantial faculty time with no corresponding reduction in other responsibilities.

    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 -- Postsecondary Teachers (all fields, 2024-34)
→ 2034
+7%
BLS OOH overall postsecondary teacher projection for context. Postsecondary teachers across all fields are projected to grow 7% from 2024-2034, driven by rising college enrollment. Political science (2%) grows substantially slower than the all-teacher average, reflecting the discipline-specific enrollment trends noted above. This cross-check illustrates that the headwinds facing political science faculty are occupational and disciplinary, not sector-wide: the broader postsecondary teaching workforce is expanding while political science specifically is flat to modestly growing.
BLS National Employment Matrix 2024-34
→ 2034
+2%
BLS Employment Projections program -- National Employment Matrix, 2024-34 cycle. Projected growth from 21,800 (2024) to 22,200 (2034), an increase of approximately 400 positions. State and local institutions drive most of the growth (12,300 to 12,800, +4%); private colleges and universities hold flat (9,400 to 9,400, 0%). Self-employment contracts marginally (-20%). The 2% figure is well below the all-postsecondary-teachers average of 7%, reflecting ongoing enrollment contraction in political science as a standalone major at many institutions, continued shift toward contingent appointments rather than tenure-track lines, and structural budget pressure at public universities. The projection does not explicitly model AI-driven RA-task compression or the pace of AI tool adoption in classrooms.
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. -- "GPTs are GPTs" (Science, 2024)
→ 2029
41%
of tasks
GPT-4 task-by-task LLM exposure labeling on O*NET tasks for postsecondary social science teachers. Eloundou et al. seed political science teachers at an augmentationUpside of 41 (shallow seed), reflecting moderate LLM task exposure. The curated deep-tier assessment (25-1065.00.ts) raised this to 72 after accounting for the political-science-specific AI toolstack (BERTopic, ATLAS.ti 24, Elicit, Qualtrics XM AI, PolitBERT) that substantially augments research and course-prep tasks. The key finding is that LLM exposure is concentrated in the literature-review, syllabus-drafting, and text-coding tasks; the deliberation-facilitation, qualitative fieldwork mentorship, and normative theory pedagogy tasks score near zero for LLM substitutability. The 41% figure is reported as the shallow-seed exposure estimate; the curated augmentation opportunity is substantially higher.
Frey and Osborne (2013) -- "The Future of Employment"
→ 2033
3%
of tasks
Gaussian-process classifier on O*NET task features. Frey and Osborne (2013) placed postsecondary teachers as one of the occupations "untouched by automation" -- among the lowest computerization-probability occupations in their dataset. The reasoning: tertiary education requires a combination of social intelligence (reading student comprehension, motivating learning, facilitating discussion), creative judgment (designing novel assessments, mentoring research), and interdisciplinary knowledge synthesis that presented strong bottlenecks to 2013-era automation. The 3% here represents F&O's low computerization probability as an implied automation floor -- not reflecting the AI tools (LLMs, text-coding assistants) that arrived after 2020 and that F&O did not model. Eloundou et al. (2024) provide the more current exposure estimate for the LLM era.
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 guides, discussion questions, and lecture outlines — using ChatGPT Edu and Claude to generate first drafts for standard political science topics (electoral systems, democratic theory, international security, comparative constitutionalism), then editing with disciplinary expertise to: replace AI-generated readings with the current field-defining work, correct normative framework misapplications (AI routinely conflates realism and neorealism, or Rawls and Nozick), and ensure AI-use policies in the syllabus comply with the institution's current stance and APSA's 2025-2026 guidance on student AI use in research and writing.

Prepare course materials — syllabi, reading guides, discussion questions, and lecture outlines — using ChatGPT Edu and Claude to generate first drafts for standard political science topics (electoral systems, democratic theory, international security, comparative constitutionalism), then editing with disciplinary expertise to: replace AI-generated readings with the current field-defining work, correct normative framework misapplications (AI routinely conflates realism and neorealism, or Rawls and Nozick), and ensure AI-use policies in the syllabus comply with the institution's current stance and APSA's 2025-2026 guidance on student AI use in research and writing.[9],[1]

Where your edge is

ChatGPT and Claude can draft a 14-week comparative politics syllabus, write discussion questions for a Federalist Papers close reading, and produce a lecture outline on democratic backsliding in reasonable time. The editorial investment that remains essential: AI-generated reading lists for political science tend toward canonical 20th-century texts and miss the 2022-2025 field-defining empirical work that journals like APSR, AJPS, and BJPS have published; AI-generated normative theory content reliably blurs distinctions that political theorists consider load-bearing (Kantian vs. Rawlsian vs. communitarian approaches to international justice); and AI-generated AI-use policies are usually copied from 2023-era template language that does not reflect your institution's current academic integrity framework or the APSA task force (2026) guidance on disciplinary AI norms.

AI is sitting alongside you hereTeach core political science subfields — American politics, comparative politics, international relations, and political theory — using AI-generated discussion prompts and policy-brief examples as pedagogical objects: project a ChatGPT or Claude response to a policy question onto the screen (e.g., "assess the democratic legitimacy of judicial review"), then have students identify where the AI conflated procedural and substantive democracy, applied normative frameworks inconsistently, or missed the institutional context that changes the analysis

Teach core political science subfields — American politics, comparative politics, international relations, and political theory — using AI-generated discussion prompts and policy-brief examples as pedagogical objects: project a ChatGPT or Claude response to a policy question onto the screen (e.g., "assess the democratic legitimacy of judicial review"), then have students identify where the AI conflated procedural and substantive democracy, applied normative frameworks inconsistently, or missed the institutional context that changes the analysis. The live critique of a plausible-but-flawed AI answer is both the lecture content and the AI-resistant assessment format.[9],[2]

Where your edge is

The APSA Teaching and Learning survey (2025) documents 61% of political science faculty experimenting with AI tools for course design, with AI-generated role-play prompts for simulations as the most-cited innovative use. The lecture format that remains irreplaceable: Socratic facilitation of contested political arguments. AI can generate a plausible summary of Rawlsian justice or liberal internationalism, but it cannot model what it looks like to hold a contested normative position in good faith, respond to pushback, and revise a political argument under pressure — the core cognitive skill political science courses aim to build. Use ChatGPT and Claude outputs as lecture props; invest preparation time in designing the critique questions and the Socratic follow-up moves that require genuine disciplinary expertise.

AI is sitting alongside you hereStay current with political science research developments — using Elicit and NotebookLM to monitor new empirical findings and theoretical debates in assigned subfields (APSR, AJPS, BJPS, IO, World Politics, Comparative Political Studies), and serve on professional committees including APSA panels, journal review boards, and grant evaluation panels — applying expert disciplinary judgment to evaluate the quality, replicability, and theoretical contribution of AI-assisted political science research submissions, which APSA (2026) identifies as a growing peer-review challenge.

Stay current with political science research developments — using Elicit and NotebookLM to monitor new empirical findings and theoretical debates in assigned subfields (APSR, AJPS, BJPS, IO, World Politics, Comparative Political Studies), and serve on professional committees including APSA panels, journal review boards, and grant evaluation panels — applying expert disciplinary judgment to evaluate the quality, replicability, and theoretical contribution of AI-assisted political science research submissions, which APSA (2026) identifies as a growing peer-review challenge.[2],[1]

Where your edge is

Elicit and NotebookLM compress literature triage from days to hours — a weekly Elicit query on "electoral interference machine learning 2025" surfaces new papers across APSR, PSRM, and Comparative Political Studies in seconds. The professional service task that AI cannot perform is the peer-review judgment: evaluating whether a submitted political science article using LLMs for text coding followed the reproducibility guidance in PSRM (2024), whether the theoretical contribution is genuinely novel or AI-generated paraphrase of existing frameworks, and whether the research design's identification strategy is valid for the political context under study. APSA (2026) explicitly flags this as a growing challenge — reviewers now need to evaluate not just the political science quality of a submission but whether its AI-assisted methods are documented, reproducible, and free of systematic bias.

Where this role is heading

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

A direction you could grow

Education Administrators, Postsecondary

Political science faculty frequently move into department chair, director of undergraduate studies, dean of social sciences, or provost-track roles — particularly those who have led curriculum redesign efforts, chaired APSA accreditation self-studies, served on AI governance task forces, or managed politically sensitive departmental personnel processes. Political science departments are under pressure from declining undergraduate enrollment trends in some institutions, the need to modernize curriculum for AI-era analytical skills, and the governance challenge of developing coherent AI-use policies for a discipline where AI's political implications are themselves a course subject. The CRI increase (+5) reflects that postsecondary education administration is moderately AI-augmented for data analytics and reporting tasks while the strategic leadership and faculty-relations core is durable, and that political science faculty bring disciplinary authority on AI governance that is increasingly valuable in academic leadership.

What you'd add
  • · Higher education budget management — faculty line planning, research overhead cost recovery, endowment income projections, and departmental capital planning in the context of social science funding trends
  • · Accreditation and program review — HLC or regional accreditor self-study documentation; APSA guidelines for undergraduate political science curriculum assessment; enrollment analytics and program viability evaluation
  • · Faculty personnel processes — promotion-and-tenure committee leadership, academic hiring logistics (job-market flyout, offer management, start-up negotiation), and faculty grievance and discipline processes
  • · AI governance for political science departments — developing institutional policy on student AI use in research and writing, evaluating AI tools for academic integrity, and representing the department on university-wide AI governance committees with a political scientist's lens on power and accountability
  • · APSA administrative resources — Committee on Department and Program Development (CDPD) training workshops; APSA career resources for department chairs and administrators; best practices in political science undergraduate curriculum for the AI era
What it takesSome new skills to pick up
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The data behind this timeline

On record since1857
Latest tracked employment21,800 (US, 2024)
Latest median pay$94,680 (2024)
Outlook+2% by 2034 (BLS National Employment Matrix 2024-34)
View all 29 cited data points
YearUS employmentMedian annual paySource
1903300n/aESTIMATE
19402,800n/aESTIMATE
19605,500n/aESTIMATE
1970n/a$12,500ESTIMATE
197514,000n/aESTIMATE
1990n/a$38,000ESTIMATE
200015,800n/aESTIMATE
200312,320$57,340BLS-OEWS
200413,230$58,980BLS-OEWS
200513,710$59,850BLS-OEWS
200613,850$61,820BLS-OEWS
200714,160$63,100BLS-OEWS
200814,340$67,200BLS-OEWS
200915,180$68,790BLS-OEWS
201015,930$70,540BLS-OEWS
201117,260$71,470BLS-OEWS
201216,770$72,170BLS-OEWS
201317,660$73,760BLS-OEWS
201417,050$73,790BLS-OEWS
201517,460$76,370BLS-OEWS
201616,720$79,210BLS-OEWS
201716,200$81,430BLS-OEWS
201815,890$83,370BLS-OEWS
201915,750$85,930BLS-OEWS
202015,130$85,760BLS-OEWS
202114,060$81,980BLS-OEWS
202215,190$83,770BLS-OEWS
202317,090$93,810BLS-OEWS
202421,800$94,680BLS-OEWS
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