This study examines the pedagogical potential of Artificial Intelligence (AI), particularly big language models like ChatGPT, in elementary science education, emphasizing physics instruction. The article, which is grounded in modern constructivist and inquiry-based learning theories, explores how AI tools might assist young learners in cultivating scientific comprehension through dialogic engagement, scaffolded inquiry, and contextually enriched explanations. The analysis synthesizes previous worldwide research, demonstrating ChatGPT's ability to generate narrative-driven learning experiences, develop age-appropriate experimental activities, and rectify prevalent errors in fundamental physics concepts, including force, motion, and energy. From an educational standpoint, AI serves as both a teaching resource and a cognitive instrument, facilitating varied learning and advancing student-centered methodologies. ChatGPT's versatility enables educators to incorporate it into many educational settings, providing tailored explanations, exemplifying scientific reasoning, and fostering inquiry-based debates. The study highlights its significance in assisting non-specialist educators by offering immediate help in lesson preparation, experimental design, and formative evaluation, thus improving teacher confidence and instructional quality. Effective integration relies on providing educators with AI pedagogical literacy—competencies to critically assess AI outputs, connect them with curriculum objectives, and cultivate epistemically rich learning environments. The article warns against excessive dependence on AI, emphasizing the necessity of maintaining the primacy of teacher mediation and inquiry-based pedagogy. This paper presents AI to enhance, rather than supplant, human instruction, positioning ChatGPT as a driver of pedagogical innovation in primary scientific education. It asserts that strategically deployed AI can enhance early science education by promoting curiosity, profound conceptual comprehension, and active engagement in scientific inquiry processes. Article visualizations:
Konstantinos Τ. Kotsis (Wed,) studied this question.