Will AI replace Mathematical Science Teachers, Postsecondary?
On paper, AI could touch ~54% of the work in Mathematical Science Teachers, Postsecondary — and unlike most jobs, it's already showing up in the real workday, not just the theory.
O*NET-SOC 25-1022
How your 13 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 (~54%). By late 2025, real-world AI use had caught up to about 43% of its task activity (already common). 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.
The signals here line up
Theoretical reach (~54%), real-world use (~43%) and the task-level picture mostly agree — so this read is more reliable than for jobs where the signals contradict each other. Even so, AI-risk estimates shift by model (a 2026 study saw the "high-risk" share swing 2.7%–51.5%), so treat these as directional, not destiny.
See all 13 tasks, ratedBased on real task-level AI scores — click to collapse
- Prepare and deliver lectures to undergraduate or graduate students on topics such as linear algebra, differential equations, and discrete mathematics.
- Maintain student attendance records, grades, and other required records.
- Prepare course materials, such as syllabi, homework assignments, and handouts.
- Collaborate with colleagues to address teaching and research issues.
- Compile, administer, and grade examinations, or assign this work to others.
- Evaluate and grade students' class work, assignments, and papers.
- Plan, evaluate, and revise curricula, course content, and course materials and methods of instruction.
- Keep abreast of developments and technological advances in the mathematical field by reading current literature, talking with colleagues, and participating in professional conferences.
- Select and obtain materials and supplies, such as textbooks.
- Advise students on academic and vocational curricula and on career issues.
- Maintain regularly scheduled office hours to advise and assist students.
- Initiate, facilitate, and moderate classroom discussions.
- Serve on academic or administrative committees that deal with institutional policies, departmental matters, and academic issues.
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.