Exploring Teachers’ Latent Resistance to AI-Supported Teaching: A Qualitative Study
TL;DR
Teachers’ latent resistance to AI-supported teaching is explored by examining how teachers described AI use, set professional boundaries, justified cautious or selective engagement, and identified conditions that might reduce such resistance.
Abstract
Teachers’ engagement with artificial intelligence (AI) cannot be fully understood through a simple acceptance–rejection distinction. Limited attention has been given to indirect, cautious, and professionally justified forms of resistance that may shape how teachers engage with AI-supported teaching. This study explored teachers’ latent resistance to AI-supported teaching by examining how teachers described AI use, set professional boundaries, justified cautious or selective engagement, and identified conditions that might reduce such resistance. A basic qualitative study was conducted with eight teachers working in two public educational institutions in Van and Hakkari, Türkiye. Data were collected through face-to-face semi-structured interviews and analyzed using codebook thematic analysis. Five descriptive categories emerged: pragmatic and conditional AI use; trust, privacy, and ethical boundaries; teacher agency and human-centered teaching; competence, time, and support needs; and ambivalent positioning. These categories were interpreted through four analytic dimensions: epistemic distrust, ethical boundary-setting, professional identity protection, and competence-based withdrawal. Teachers’ cautious engagement with AI may reflect professional boundary work rather than simple reluctance or technophobia. Responsible AI integration should combine critical AI literacy, ethical guidance, reliable tools, institutional support, and professional development that respects teacher agency and human-centered teaching.
