Impact of Artificial Intelligence (AI) on Teacher Self-Efficacy: A Systematic Literature Review

Abstract

This systematic literature review (SLR) examined the impact of AI on teacher self-efficacy in empirical studies published between 2024 and 2025. Guided by the PRISMA-2020 framework, peer-reviewed articles were retrieved from major academic databases, including Scopus, Web of Science, ERIC, and Google Scholar. Following rigorous screening and eligibility procedures, 15 empirical studies were included for narrative and thematic synthesis. The findings reveal that teacher self-efficacy functions dynamically within AI-integrated educational environments, operating not only as an outcome of AI literacy, training, and professional development, but also as a mediator and predictor within AI adoption and instructional models. Key antecedents influencing AI-related teacher self-efficacy include AI literacy, attitudes toward AI, AI-TPACK competencies, and pedagogically grounded professional development. Higher levels of self-efficacy were consistently associated with positive instructional intentions, sustained AI integration, and adaptive professional engagement. However, the review also highlights notable gaps, including the predominance of cross-sectional designs, limited focus on in-service teachers, and conceptual inconsistencies in measuring AI-related self-efficacy. The study concludes that strengthening teacher self-efficacy should be a central objective of AI-oriented teacher education and professional development.