Supporting Critical Human–AI Interaction Without Reducing Adoption: A Two-Site Study of Teacher Professional Development with Generative AI
Abstract
Generative AI tools are entering classrooms faster than evidence about how teach- ers learn to use them well, and a central design worry is overreliance: that fluent, sometimes wrong outputs will be accepted uncritically. We report a two-site, pre– post professional development study with school teachers in Karnataka, India (122 pre- and 119 post-intervention responses). Both sites received hands-on practice on authentic teaching tasks; one additionally received structured training in prompt construction and output verification. Adoption intention was already at ceiling be- fore training and did not move. What moved was interaction readiness: perceived ease of use (pooled d = 0.42) and AI self-efficacy (d = 0.34). Where verification was trained, teachers’ reported propensity to scrutinise AI outputs rose most of all (d = 0.52) with no accompanying decline in adoption. We measured disposi- tion rather than detection performance, so this is an initial result: it indicates that guarding against overreliance need not be traded against adoption.
