ARTIFICIAL INTELLIGENCE AS A CATALYST FOR PERSONALIZED LEARNING IN SCIENCE EDUCATION

Abstract

Artificial Intelligence (AI) is increasingly reshaping educational practices worldwide, with significant implications for science education. This position paper argued that AI serves as a powerful catalyst for personalized learning by enabling adaptive instruction, real-time feedback, and data-driven decision-making in science classrooms. Traditional one-size-fits-all approaches to teaching science often fail to address learners' diverse abilities, prior knowledge, and learning paces. AI-powered systems such as intelligent tutoring platforms, learning analytics tools, and adaptive assessment technologies offer opportunities to tailor instructional content to individual student needs, thereby enhancing conceptual understanding and engagement. The paper contends that the integration of AI can support differentiated instruction, reduce learning gaps, and foster deeper scientific inquiry by providing customized simulations, virtual laboratories, and interactive problem-solving environments. However, effective implementation requires adequate teacher preparedness, infrastructural support, and ethical safeguards to ensure equitable access and responsible data use. By repositioning AI as a complementary instructional partner rather than a replacement for teachers, science education can move toward more inclusive, responsive, and learner-centered pedagogical practices. The paper concluded that systematic and policy-guided integration of AI technologies is essential to harness their full potential in transforming science education through personalized learning.