Education · SOC 25-1053
Environmental fieldwork and policy engagement are strong anchors; AI primarily threatens routine grading and content preparation.
Parts of the job are exposed, but judgment and context still matter.
Timeline: AI modeling tools will enhance environmental research capability within 3-5 years, but field-based and policy-engaged teaching roles remain robust well into the 2030s.
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Industry pages show which sectors this occupation feeds and the AI companies driving the shift.
AI job-risk card
Environmental 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 Environmental 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 lab and class work, Drafting syllabi and course handouts, Advising on standard curricula and career paths, Planning and revising standard course content. Harder to automate: Conducting environmental field research, Engaging with regulatory and policy processes, Mentoring students in applied environmental work, Teaching systems thinking and environmental ethics.
Lean into GIS and remote sensing, AI-assisted environmental modeling, Environmental policy analysis, Research grant writing. Consider roles such as Environmental Consultant or EPA Research Scientist or Sustainability 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.