Dialogic vs. Structured AI in Physics Education: Theoretical Frameworks and Pedagogical Implications

The incorporation of artificial intelligence (AI) into physics education is transforming the interaction between educators and students with intricate scientific concepts. This work examines the theoretical and pedagogical ramifications of two separate AI paradigms: dialogic systems, represented by ChatGPT, and structured systems, represented by DeepSeek. Dialogic AI facilitates open-ended inquiries, interactive reasoning, and exploratory discourse that correspond with constructivist and sociocultural learning theories. Structured AI prioritizes structure, precision, and focused scaffolding, attributes that align with cognitive load theory and direct instruction approaches. This study analyzes classroom affordances, applications in teacher professional development, exam preparation, and virtual laboratories, along with their contributions to inclusivity and differentiated education, based on contemporary literature. Ethical considerations are also addressed, especially regarding prejudice, transparency, and the maintenance of critical thinking in AI-mediated contexts. The research conducts a comparative analysis, emphasizing the complementary benefits and limits of dialogic and structured AI, and posits that their combined use may yield a more balanced teaching approach. Future directions highlight empirical classroom research, multidisciplinary cooperation, and the development of educational policies that guarantee innovation and equity. This research elucidates the changing role of AI in physics education, contributing to broader dialogues on how technology enhances scientific literacy and educational outcomes.