THE ROLE OF AI-POWERED ADAPTIVE LEARNING SYSTEMS IN SCIENCE EDUCATION: EFFECTIVENESS AND FUTURE PROSPECTS

Contemporary science education faces a persistent structural challenge: a one-size-fits-all instructional model that fails to account for the heterogeneous prior knowledge, cognitive styles, and learning paces of individual students. This mismatch contributes to low engagement, high rates of misconception persistence, and declining achievement in disciplines such as physics, chemistry, and biology. Artificial intelligence (AI)-powered adaptive learning systems offer a compelling response to this challenge by dynamically personalizing instructional content, pacing, and feedback in real time. This paper reviews the design principles and empirical effectiveness of such systems within science education contexts, drawing on recent research published between 2020 and 2024. The findings indicate that adaptive platforms improve learning outcomes by an average of 15–22% compared to conventional instruction, enhance student motivation, and demonstrate notable efficacy in identifying and correcting disciplinary misconceptions. The paper also examines critical challenges, including algorithmic bias, data privacy concerns, the digital divide, and insufficient teacher preparation. It concludes with forward-looking policy recommendations and identifies productive directions for future research, including integration with virtual laboratory environments and human-AI co-teaching models.