Education · SOC 25-1022
Grading and course prep can be AI-assisted, but mathematical pedagogy, proof-teaching, and research require human depth.
Parts of the job are exposed, but judgment and context still matter.
Timeline: AI tutoring systems will handle routine problem-solving instruction within 3-5 years, but graduate-level mathematical research and pedagogy remain durable roles through 2035.
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Industry pages show which sectors this occupation feeds and the AI companies driving the shift.
AI job-risk card
Mathematical Science Teachers, Postsecondary is watch on AIFear. Parts of the job are exposed, but judgment and context still matter.
AIFear.com
Our AI Fear Score for Mathematical Science Teachers, Postsecondary is 40/100 (Watch). Parts of the job are exposed, but judgment and context still matter.
40 out of 100, based on an AI-model review of this occupation's O*NET tasks against what current AI can actually do.
Most exposed: Grading student assignments and problem sets, Drafting syllabi, handouts, and homework, Advising on standard curricula and career paths, Revising standard course content and structure. Harder to automate: Teaching mathematical reasoning and proof, Conducting original research in mathematics or statistics, Mentoring PhD students on novel problems, Serving on institutional committees.
Lean into AI-assisted grading tools, Mathematical communication, Statistical computing (R, Python), Research mentorship. Consider roles such as Applied Statistician or Quantitative Research Scientist or Data Science Program Director.
Tasks, skills and AI-exposure for this role were reviewed by an AI model against its O*NET task profile (curated roles use published research anchors). Pay and employment, where shown, are U.S. BLS OEWS (May 2024) figures. This is an estimate, not a prediction about any individual's job. Compare all occupations.