AI Resilience Report forEducation Teachers, Postsecondary

Right now, AI is showing up more as a helper than a replacement for college professors who train future teachers. The clearest examples are behind-the-scenes work: generative AI can quickly draft syllabi, lesson plans, rubrics, and assessments ^[1], and a new framework released by the American Association of Colleges for Teacher Education (AACTE) urges preservice programs — which prepare over 90% of new educators ^[2] — to teach candidates how to plan, differentiate instruction, and even evaluate student work that was produced using AI tools. But the human core of the job — mentoring student teachers, observing them in real classrooms, and modeling professional judgment — is holding steady.

AACTE members note that small mentoring relationships and personalized feedback ^[1] still define the field, and the AACTE framework insists AI should support, not replace, teachers' judgment and relationships with students ^[2].

Adoption is happening, but unevenly. The Digital Education Council's 2026 global survey of 18,114 faculty found that 64% of faculty have participated in AI literacy training ^[3], yet only 29% of students think their instructors are ready to guide AI use — and in the US & Canada, faculty intent to use AI in teaching actually dropped from 76% in 2025 to 67% in 2026 ^[3], with over half worried AI could harm students' intellectual development. Pressure to adopt is still strong: many colleges are rolling out new AI requirements in general education and majors this fall ^[4] to meet employer demand, and Brookings researchers argue schools must move carefully because generative AI carries real risks like bias, hallucinations, and effects on critical thinking ^[5].

The bottom line: if you love teaching future teachers, your empathy, ethical judgment, and coaching skills are exactly what AI can't copy — and they're becoming more valuable, not less.