AI Resilience Report forEnvironmental Science Teachers, Postsecondary

Right now, AI is mostly augmenting environmental science professors, not replacing them. The tasks with the highest automation risk — record-keeping, building reading lists, and grading — are exactly the ones tools like ChatGPT already help with. In a national survey, about a quarter of faculty don't use any AI tools at all, and about a third don't use them in teaching, but among those who do, generative AI is showing up in lesson planning, syllabus prep, and drafting quizzes.

Environmental education groups are actively exploring these uses: the North American Association for Environmental Education hosted a panel called "AI, Education, and Ethics for a Changing World" ^[1] that looked at both the promise and pitfalls of AI in classrooms. Research on postsecondary environmental courses found that AI-supported learning can boost engagement when paired with human instructors ^[2], but the "supervise students' laboratory and field work" tasks — muddy boots, water samples, ecological observation — remain deeply human. As one ecology journal put it, AI's rapid entry into ecology is changing how entry-level data tasks are structured ^[3], pushing professors to teach judgment about AI outputs rather than raw data crunching.

Adoption is moving fast for back-office work but slowly in teaching. On the fast side, Inside Higher Ed reports colleges are racing to deploy AI for administrative advantage ^[4] because tools are cheap and widely available. On the slow side, 86% of faculty say AI's impact will be significant, but many worry it harms critical thinking and fuels cheating ^[4].

Environmental scientists also raise an ethical brake: researchers warn that data centers powering AI could emit as much carbon as 10 million cars ^[5], which clashes with the field's core mission. Together, these forces mean your future professors will likely use AI as a helper — while human mentorship, fieldwork, and ethical judgment stay firmly in demand.