Acceptance of Artificial Intelligence (AI) In Science Education: Factors Influencing Science Teachers' Intention to Use: A Systematic Literature Review

Abstract

Artificial Intelligence (AI) is rapidly transforming the educational landscape, with profound implications for science education. However, the successful integration of AI-based tools depends on science teachers’ acceptance and their ability to effectively incorporate these technologies into their instructional practices. This systematic review examines the acceptance of AI in science education by analyzing the factors influencing science teachers' intention to use AI-based tools and the challenges associated with AI adoption. Following PRISMA guidelines, this review includes 12 empirical studies published between 2021 and 2025. The results indicate that teachers’ intention to use AI is primarily influenced by the Technology Acceptance Model (TAM), which is rooted in the Theory of Reasoned Action (TRA) and its extension, the Theory of Planned Behavior (TPB). Some studies also incorporate other models, such as TPACK and path analysis frameworks. Key influencing factors include technological perceptions, psychological attributes, social influences, and pedagogical considerations. The review further highlights challenges that impede AI integration, including inadequate training and professional development (PD), technological and infrastructural limitations, pedagogical concerns, and ethical considerations. The findings of this review provide valuable insights for educators, policymakers, and researchers aiming to promote effective and equitable AI adoption in science education.