Will AI replace Criminal Justice and Law Enforcement Teachers, Postsecondary?
In theory, AI could do about 50% of the work in Criminal Justice and Law Enforcement Teachers, Postsecondary. In practice, as of late 2025, almost no one is actually using it that way — yet.
O*NET-SOC 25-1111
How your 17 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 (~50%). By late 2025, real-world AI use had reached about 0% 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.
Don't trust a single AI-risk score here
For this job, the signals disagree sharply. AI's theoretical reach looks moderate (~50%), but real-world use is only ~0%, and how much AI "can" do shifts wildly by model — one 2026 study found the share of "high-risk" jobs swung 2.7% to 51.5% just by changing which AI did the rating. This page shows the spread instead of pretending there's one number.
See all 17 tasks, ratedBased on real task-level AI scores — click to collapse
- Maintain student attendance records, grades, and other required records.
- Prepare course materials, such as syllabi, homework assignments, and handouts.
- Write letters of recommendation for students.
- Compile bibliographies of specialized materials for outside reading assignments.
- Prepare and deliver lectures to undergraduate or graduate students on topics such as criminal law, defensive policing, and investigation techniques.
- Evaluate and grade students' class work, assignments, and papers.
- Compile, administer, and grade examinations, or assign this work to others.
- Keep abreast of developments in the field by reading current literature, talking with colleagues, and participating in professional conferences.
- Plan, evaluate, and revise curricula, course content, course materials, and methods of instruction.
- Advise students on academic and vocational curricula and on career issues.
- Collaborate with colleagues to address teaching and research issues.
- Participate in student recruitment, registration, and placement activities.
- Select and obtain materials and supplies, such as textbooks.
- Initiate, facilitate, and moderate classroom discussions.
- 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.