![Artificial Intelligence and Teacher Self-Efficacy: From Technology Adoption to AI-Mediated Efficacy Reconstruction]()
Artificial Intelligence and Teacher Self-Efficacy: From Technology Adoption to AI-Mediated Efficacy Reconstruction
Nov 03,26 | 01:41 EST
Artificial intelligence (AI) is increasingly embedded in educational practice, transforming lesson planning, instructional delivery, assessment, learning analytics, and teacher professional development. Although research on AI in education has expanded rapidly, the psychological consequences of AI adoption for teachers remain theoretically fragmented. Existing studies have examined technology acceptance, AI literacy, technology self-efficacy, instructional confidence, and professional identity, yet these constructs have rarely been integrated into a coherent account of how AI reshapes teachers' beliefs about their own professional capabilities. This narrative review synthesizes the emerging literature through a theory-building perspective grounded primarily in social cognitive theory and informed by technology acceptance, cognitive load, professional identity, social comparison, and human–AI interaction perspectives. We propose a framework of AI-Mediated Efficacy Reconstruction, conceptualizing AI not as a direct determinant of teacher self-efficacy but as a socio-technical factor that changes the availability, interpretation, and attribution of efficacy-relevant experiences. AI can facilitate mastery experiences, provide vicarious learning opportunities, offer timely feedback, reduce routine cognitive demands, and expand access to professional learning. However, the same technologies may generate cognitive overload, technical anxiety, professional comparison, identity threat, reduced autonomy, and technological dependence. We identify Efficacy Attribution as a potentially central mechanism determining whether AI-supported performance is internalized as evidence of teachers' own professional capability or attributed primarily to the technological system. On this basis, we distinguish Augmented Efficacy, in which AI extends teachers' capabilities while preserving professional agency, from Dependent Efficacy, in which perceived competence becomes increasingly contingent upon AI assistance. The framework further proposes that these processes are moderated by teacher characteristics, AI-system characteristics, teaching tasks, organizational context, and time. We conclude by outlining implications for measurement, teacher professional development, AI design, organizational policy, and future longitudinal and experimental research.
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