Teacher leaders enacting AI leadership identities: Brokering,buffering and professional rationalities

Abstract

This study investigated how teacher leaders enact AI leadership identities in K–12 schools. Drawing on an interpretive qualitative design, 21 semi-structured interviews were conducted with AI-advocate teacher leaders. Data were analysed using reflexive thematic analysis supported by NVivo 15, with the analysis organised around four theoretically derived lenses: enacting AI leadership through relational influence, crafting AI leadership as professional identity, brokering and buffering AI in school ecologies and reasoning about AI through competing rationalities. Findings show that teacher leaders lead AI ‘from the classroom’, using visible experimentation, informal interactions and accumulated trust to shape colleagues’ practice, while simultaneously filtering the AI toolscape, slowing or reshaping initiatives they perceive as harmful, and protecting students’ data, dignity and agency. AI leadership identities emerge as co-authored by colleagues and students, experienced as both empowering and exposing and sustained through continual calibration between accountability demands and educational purpose. The study contributes to teacher leadership scholarship and AI-in-education research by foregrounding teacher leaders as pivotal human actors who, in practice, structure the architectures through which AI is woven into schooling, and by underscoring that teacher leadership itself must be conceptualised as fluid and non-static in the face of AI's rapidly reconfiguring educational landscape.