The rapid diffusion of artificial intelligence (AI) across educational systems has elevated AI literacy from a peripheral skill to a core professional competency for teachers. This mixed-methods study investigates the opportunities, risks, and societal demands associated with teacher AI literacy and examines how professional development structures shape educators ‘ competencies and attitudes. Building on contemporary frameworks and empirical studies, we synthesize the state of the field and report findings from a cross-sectional survey (N = 412) and semi-structured interviews (n = 24) conducted with in-service teachers across primary, secondary, and vocational settings. Quantitative analyses indicate that motivational and acceptance factors predict AI literacy more strongly than demographic or purely technical variables, while participation in structured training is associated with significant gains in self-reported AI literacy and confidence. Qualitative analyses highlight tensions between promise and peril: teachers identify opportunities for personalization, workload relief, and formative assessment, while expressing concerns about bias, opacity, data protection, and overreliance on opaque tools. We integrate results with recent literature to propose an educator-centered AI literacy model that balances technical, pedagogical, ethical, and socio-institutional domains. We argue that meeting the demands of modern society requires: (a) sustained, curriculum-integrated teacher learning anchored in ethics and explainability; (b) institutional policies and governance; and (c) assessment approaches tied to real teaching tasks. We conclude with implementation guidance and a roadmap for future research, including the need for validated measures and equity-focused evaluation.
Toirova et al. (Thu,) studied this question.
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