Possibilities for Optimizing the Teaching of Theoretical Disciplines through Artificial Intelligence

This study explores the potential of artificial intelligence (AI) in enhancing educational processes, with a focus on optimizing the teaching of theoretical disciplines. Within this framework, a methodological model for the integration of AI into Classical Electrodynamics and Quantum Mechanics instruction is proposed, outlining a structured activity trajectory for implementation. The approach aims to improve students’ mastery of abstract and conceptually complex physical phenomena by enabling personalized learning experiences, interactive visualization, automated assessment, and data-driven instructional adaptation. It is expected that such integration will contribute to deeper conceptual understanding and more efficient learning outcomes. Moreover, the proposed model may serve as a scalable framework for the instruction of other theoretical disciplines, thereby expanding the scope of AI-assisted pedagogy in higher education