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Time Machine

Criminal Justice and Law Enforcement Teachers, Postsecondary

Scrub through 120years 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.

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1925195019752000now
2026
Known today as Criminal Justice and Law Enforcement Teachers, Postsecondary (BLS SOC 25-1111)
Latest actual · 2024
16K
BLS OEWS-based employment figure from O*NET and the BLS National Employment Matrix 2024-34 baseline. Employment has grown steadily from the 2003 level as criminal justice programs maintained enrollment, partly through online program expansion that reached non-traditional students in rural and working-adult markets. The 2024 figure reflects the stabilized post-pandemic level; many CJ programs absorbed COVID disruption through rapid online pivots that proved durable. The BLS May 2025 OEWS figure for 25-1111 is approximately 3,700 (this figure appears in the BLS current OEWS table, which counts only primary industry employers; the O*NET and National Employment Matrix figure of 16,200 represents the full cross-industry count including all employing institutions and is the appropriate baseline for projections).
Latest actual · 2024
$71,470
BLS OEWS-based median annual wage from O*NET for May 2024. The BLS current OEWS table reports $75,990 for May 2025 (a year later). The 2024 figure of $71,470 is from the O*NET sourced employment projections data. Criminal justice faculty wages have increased substantially from the 2003 baseline in real terms, partly because the field now has a higher share of research-active doctoral faculty at 4-year institutions than it did in the LEEP era.
Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.
Tools of the era

The tools that defined the work

Select an era to see how it reshaped the work.

  • Practitioner lecture + blackboard (early police science era)

    The founding era of criminal justice education operated entirely on practitioner knowledge transmission. August Vollmer's 1916 UC Berkeley course -- and the small cohort of similar programs at Indiana, San Jose State, and Michigan State that followed -- relied on active or retired law enforcement professionals delivering lecture-based instruction from operational experience. There was no textbook industry for police science (Vollmer's own book, "The Police and Modern Society," published in 1936, was among the first), no academic journals, no peer-reviewed research base. The blackboard was the primary technology of instruction; crime scene photographs and physical evidence were the pedagogical materials. Faculty who taught in these programs were practitioners first and academics second, if at all.

    Work toolChanging equipment
  • Mimeograph + 16mm film (post-Vollmer academic era)

    The School of Criminology at UC Berkeley, opened by O.W. Wilson in 1950, was the first graduate-level criminal justice program in the United States. The mid-century faculty toolkit was built around mimeographed course packets, 16mm training films sourced from the FBI and state police academies, and the first dedicated academic journals for the field (Journal of Criminal Law and Criminology, founded 1910 at Northwestern, had predated the professional faculty by decades; Criminology, the flagship ACJS journal, was founded in 1963). Faculty who taught criminal procedure, evidence, and police administration used court reporters' typed transcripts and appellate case collections as primary instructional documents. Forensic science instruction relied on physical evidence kits, spectrographic analysis equipment, and photographic darkrooms.

    AI audit toolsPattern detection
  • Overhead projector + VHS training video (LEEP and post-LEEP era)

    The LEEP-era expansion of criminal justice programs produced a corresponding growth in instructional media: overhead projectors with acetate slides became the standard lecture technology, and VHS-format training videos from the National Institute of Justice and state police commissions replaced 16mm film. The era also saw the first criminal justice simulation exercises -- tabletop crime scene reconstructions, mock trials in law school facilities, and early role-play interrogation training. The post-LEEP contraction of the early 1980s produced a different kind of technological shift: faculty who survived program cuts had to become more broadly competent across criminology, law, and corrections rather than specialists in a single operational subfield, because shrinking departments required each person to cover more curricular ground.

    Effect on the work

    The creation and then defunding of LEEP (1968-1982) produced the first major boom-and-bust in criminal justice faculty employment. Participating institutions grew from 485 in 1970 to 1,036 by 1975; faculty numbers grew proportionally. The 1980 GAO audit found the program in "serious financial disarray"; Congress defunded it in 1982 and enrollment fell, resulting in program contractions and faculty layoffs through the early 1980s.

    Work toolChanging equipment
  • Learning Management Systems (Blackboard, Desire2Learn) + online program expansion

    The 9/11 attacks in 2001 triggered a surge in criminal justice enrollment that persisted through the decade: homeland security, counterterrorism, and forensic science concentrations attracted students who might not otherwise have enrolled in CJ programs, and the establishment of the Department of Homeland Security in 2002 created visible career pathways. Blackboard and its competitors allowed criminal justice programs to scale rapidly through online sections without proportional facility investment, enabling the growth of large-enrollment online programs at regional universities and for-profit institutions (University of Phoenix, American Military University, Kaplan). By 2010, criminal justice was one of the most popular online degree fields in the country. Faculty in this era added an online course design and facilitation competency to their toolkit that had not existed a decade earlier.

    Effect on the work

    Online program expansion grew criminal justice faculty employment substantially through the 2000s, though the growth was uneven: tenure-track faculty at research universities remained roughly stable while non-tenure-track and adjunct positions at community colleges and online-focused institutions grew rapidly. The 9/11 enrollment surge translated into additional faculty hiring with a 2-3 year lag.

    Work toolChanging equipment
  • Predictive policing tools and risk assessment instruments as curriculum subjects (COMPAS, PredPol)

    The ProPublica "Machine Bias" investigation (May 2016) and the simultaneous adoption of predictive policing tools (PredPol, HunchLab) by large police departments gave criminal justice faculty an entirely new curriculum challenge: students entering law enforcement, courts, and corrections careers were going to encounter AI-driven decision tools on day one, and no other academic discipline was positioned to teach those tools critically. Criminal justice faculty became, by default, the primary academic custodians of a curriculum that covered not just how these tools worked but what the documented failures were: the COMPAS false positive rate disparities, the ShotSpotter error rate (90% of Chicago alerts led to no evidence of a shooting, per the 2021 OIG audit), the predictive policing feedback loop described by Lum and Isaac (2016). The physical tool of this era was the classroom display of live platform demos alongside the critical literature -- presenting the PredPol interface alongside the Lum and Isaac methodology paper, and asking students to evaluate the gap between the vendor's claims and the independent audit.

    Work toolChanging equipment
  • Generative AI in law enforcement (Axon Draft One, Canvas AI, Turnitin detection)

    The 2023 rollout of Axon Draft One -- GPT-4 generating incident report first drafts from body camera audio, adopted by 20,000+ agency customers -- gave criminal justice faculty a teaching challenge that was immediate and operational: students entering patrol assignments in 2025-2026 would encounter AI report drafting on the job before graduation. Simultaneously, Canvas AI (January 2025 general availability) compressed the content-update burden for faculty maintaining AI-in-CJ modules, and Turnitin AI detection became a routine part of case analysis grading. The era marks the first time the tools that reshape what faculty teach are the same class of tools that reshape how faculty teach and grade.

    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 (25-1000 major group)
2034
+7%
BLS OOH projects postsecondary teachers overall (25-1000 major group) at +7% employment change 2024-2034, substantially above the +2% projection for the 25-1111 specialty. The gap between the specialty and the major group projection reflects that criminal justice programs are more concentrated in community colleges and regional universities -- sectors that are growing more slowly or contracting in some states -- than STEM, healthcare, and professional fields that are driving the major group average upward. The OOH also projects approximately 114,000 openings per year across all postsecondary teacher specialties, driven in large part by replacement of retiring faculty; the equivalent figure for 25-1111 is approximately 1,200 annual openings, accounting for both growth and replacement.
BLS National Employment Matrix 2024-34
2034
+2%
BLS Employment Projections -- industry-occupation matrix plus labor productivity assumptions. The 2024-34 cycle projects +2.0% employment change for 25-1111, equivalent to approximately 300 additional positions -- from 16,200 (2024) to 16,500 (2034). This is classified as slower than average growth against an all-occupations average of +4% and substantially slower than the all-postsecondary-teachers projection of +7%. The BLS methodology reflects continued demand for criminal justice curriculum at community colleges offset by the structural headwinds facing higher education enrollment broadly (demographic cliff of college-age population, student loan debt concerns, declining associate degree enrollment in some CJ program formats). The projection does not explicitly model the AI-in-CJ curriculum demand wave, which is adding new course content but not necessarily new faculty positions.
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" (2023)
2028
44%
of tasks
GPT-4 task-by-task LLM exposure labeling on O*NET tasks for criminal justice postsecondary teachers. The Eloundou framework scores this occupation in the moderate exposure range (beta around 0.44): lecture preparation, syllabus drafting, case summary writing, and course material updates are meaningfully exposed to LLM assistance. The important caveat, documented in the deep curation of this role: Eloundou measures text-processing exposure, not overall automation risk. Criminal justice faculty hold strong durable advantages in curriculum authority over the AI tools students will use in practice, required law enforcement agency networks that cannot be replicated by LLMs, and expert witness and AI governance advisory roles that are growing as governments grapple with AI in policing. The exposure measure reflects augmentation opportunity at least as much as displacement risk.
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 hereEvaluate and grade student assessments — using Gradescope AI-assisted answer grouping for large-enrollment Introduction to Criminal Justice and Criminal Law courses at community colleges and regional universities (objective assessments: case identification, statutory element matching, constitutional amendment application)

Evaluate and grade student assessments — using Gradescope AI-assisted answer grouping for large-enrollment Introduction to Criminal Justice and Criminal Law courses at community colleges and regional universities (objective assessments: case identification, statutory element matching, constitutional amendment application); running written case analyses and policy papers through Turnitin AI detection as a flag for further scrutiny of assignments (COMPAS bias analyses, policing policy position papers, criminological theory application essays) that may reflect AI generation rather than authentic policy reasoning; and applying expert disciplinary judgment for all evaluations of professional scenario simulations, ethics case analysis papers, and applied research assignments that require current-practice grounding.[11],[12]

Tools picking this up
Where your edge is

Criminal justice programs at community colleges frequently enroll 60-120 students per intro section, and Gradescope's AI-assisted answer grouping is directly applicable to the objective-knowledge assessments that anchor foundation courses (case elements, constitutional amendments, statutory definitions). The faculty-irreplaceable grading work is in applied analysis: a student's COMPAS bias analysis paper requires assessment of whether they applied the ProPublica differential false positive framework correctly, engaged with the Northpointe/Equivant rebuttal methodologically, and drew defensible policy conclusions — assessment that requires disciplinary expertise in both criminal justice and statistical methodology. A high Turnitin AI probability score on a policing policy paper is also clinically significant in a different sense than in most disciplines: students who AI-generate their police use-of-force policy analysis are masking whether they have developed the professional judgment that will govern life-and-death decisions in the field. Use Gradescope to recover time from objective grading; invest in substantive feedback on applied analysis that builds the professional reasoning criminal justice careers require.

AI is sitting alongside you hereDesign and deliver lectures on criminological theory, criminal law, and criminal justice policy — using ChatGPT Edu or Claude to generate first-draft lecture scaffolds (strain theory application to contemporary crime trends, social learning theory and gang dynamics, routine activities theory in cybercrime contexts, comparative sentencing policy across jurisdictions) for courses in Introduction to Criminal Justice, Criminology, Criminal Law, and Corrections, then substantively revising AI output for disciplinary accuracy, current case law, and current-events alignment

Design and deliver lectures on criminological theory, criminal law, and criminal justice policy — using ChatGPT Edu or Claude to generate first-draft lecture scaffolds (strain theory application to contemporary crime trends, social learning theory and gang dynamics, routine activities theory in cybercrime contexts, comparative sentencing policy across jurisdictions) for courses in Introduction to Criminal Justice, Criminology, Criminal Law, and Corrections, then substantively revising AI output for disciplinary accuracy, current case law, and current-events alignment; embedding AI ethics content (predictive policing algorithms, COMPAS risk scores, facial recognition accuracy disparities) as required critical AI literacy modules across relevant course areas per ACJS (2025) guidance.[3],[10]

Where your edge is

ChatGPT Edu can scaffold a lecture outline on social disorganization theory or a comparative corrections policy summary in under two minutes — the structural scaffolding is genuinely useful, particularly for maintaining the current-events components of criminal justice courses where law, technology, and policy shift faster than any other social science discipline. The faculty contribution AI cannot replace: situating AI-generated content in the current operational reality of criminal justice agencies (what predictive policing tools did Chicago and Los Angeles just decommission, what AI risk assessment is the New Jersey courts currently using, what did the NIST FRVT say about the facial recognition vendor your local PD just procured?); applying ACJS curriculum standards for current-practice content grounding; and building the AI ethics modules that students entering policing, courts, and corrections need to navigate these tools with professional judgment. Use Canvas AI and ChatGPT Edu to draft and update lecture scaffolds; invest recaptured time in maintaining agency relationships that keep curriculum current.

AI is sitting alongside you hereConduct and publish criminological research on AI in the criminal justice system — using Elicit and Consensus AI research tools to conduct rapid systematic literature review for grant proposals (NIJ, BJA, MacArthur Safety and Justice Challenge grants) and peer-reviewed scholarship on AI tool accuracy, disparate impact, and governance in policing, courts, and corrections

Conduct and publish criminological research on AI in the criminal justice system — using Elicit and Consensus AI research tools to conduct rapid systematic literature review for grant proposals (NIJ, BJA, MacArthur Safety and Justice Challenge grants) and peer-reviewed scholarship on AI tool accuracy, disparate impact, and governance in policing, courts, and corrections; identifying empirical evidence on predictive policing outcomes, risk assessment instrument accuracy, facial recognition error rates, and AI governance frameworks; maintaining that research question, theoretical contribution, and interpretive conclusions remain the human intellectual core; and publishing findings in Criminology, Justice Quarterly, Crime and Delinquency, or Journal of Criminal Law and Criminology.[13],[14]

Tools picking this up
Where your edge is

Elicit and Consensus can compress literature review time for NIJ and BJA grant proposals — useful for criminal justice research, where relevant literature spans criminology, law, computer science, public policy, and civil liberties scholarship that no single faculty researcher monitors comprehensively. The faculty-irreplaceable contribution: identifying a research question with genuine policy traction (understanding which AI procurement decisions are reversible, which algorithmic biases are measurable with available data, which intervention points in the criminal justice AI adoption cycle are accessible to empirical study); designing research protocols with appropriate IRB protections for vulnerable populations (incarcerated persons, juvenile offenders, people under supervision — all standard CJ research populations with specific federal IRB requirements under 45 CFR 46 Subpart C); and interpreting findings in the context of constitutional law and civil rights accountability frameworks that shape how criminal justice AI research is used by advocates, legislators, and courts. The most impactful CJ AI scholarship — ProPublica "Machine Bias," NIST FRVT, Lum & Isaac on predictive policing — all required deep domain knowledge and practitioner access that AI tools cannot provide.

Where this role is heading

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

A direction you could grow

Social and Community Service Managers

Criminal justice faculty — particularly those who have directed CJ programs, led research centers on justice policy, or built relationships with reentry organizations, public defender offices, and community supervision agencies — are natural candidates for leadership roles at justice-adjacent nonprofits, reentry service organizations, pretrial services agencies, and community corrections programs. The MacArthur Safety and Justice Challenge network, ACLU affiliates, and Vera Institute of Justice field a standing demand for program leaders who understand both criminal justice system operation and evidence-based reform. As agencies adopt AI decision-support tools, these organizations specifically seek leaders who can evaluate AI vendor claims critically, develop organizational AI governance policies, and train frontline staff on algorithmic accountability. Faculty with grant writing experience (NIJ, BJA, MacArthur) are particularly competitive for executive director and program director roles, where grant management is a core competency.

What you'd add
  • · Nonprofit financial management — reading and presenting organizational budgets to boards and funders; federal grant compliance (BJA, NIJ, OJJDP, MacArthur Safety and Justice Challenge reporting requirements); operating reserve and cash flow management for organizations dependent on government contracts
  • · AI governance for justice organizations — developing organizational policies for responsible AI tool use in pretrial, reentry, and community corrections contexts; evaluating vendor accuracy claims against independent audits (NIST FRVT methodology, ProPublica differential false positive analysis); staff training on algorithmic accountability and override obligations
  • · Human resources management in high-turnover social service settings — frontline staff retention in reentry and community supervision organizations; trauma-informed management practices; EEOC compliance in mission-driven organizations
  • · Program evaluation and outcomes measurement — recidivism outcome measurement methodology; logic model development for pretrial services and reentry programs; data sharing agreements with criminal justice agencies for outcomes tracking; HMIS data systems for housing and reentry services
  • · Community and stakeholder engagement — building collaborative relationships with courts, corrections, and law enforcement while maintaining organizational independence; media engagement on criminal justice AI issues; legislative testimony and policy advocacy
What it takesSome new skills to pick up
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The data behind this timeline

On record since1916
Latest tracked employment16,200 (US, 2024)
Latest median pay$71,470 (2024)
Outlook+2% by 2034 (BLS National Employment Matrix 2024-34)
View all 25 cited data points
YearUS employmentMedian annual paySource
1965500n/aESTIMATE
19755,000$14,000ESTIMATE
19853,500n/aESTIMATE
200310,800$47,000BLS-OEWS
20049,550$47,800BLS-OEWS
20059,880$49,240BLS-OEWS
200610,430$49,730BLS-OEWS
200711,110$51,060BLS-OEWS
200811,630$53,640BLS-OEWS
200912,610$57,500BLS-OEWS
201013,860$59,520BLS-OEWS
201114,630$59,480BLS-OEWS
201214,020$58,040BLS-OEWS
201314,870$56,980BLS-OEWS
201414,890$57,200BLS-OEWS
201514,560$58,770BLS-OEWS
201614,620$59,590BLS-OEWS
201714,210$60,400BLS-OEWS
201814,890$61,900BLS-OEWS
201914,070$62,860BLS-OEWS
202014,030$63,560BLS-OEWS
202113,790$64,600BLS-OEWS
202213,900$64,990BLS-OEWS
202313,390$69,030BLS-OEWS
202416,200$71,470BLS-OEWS
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