INNOVATIVE PRACTICE OF COLLEGE PHYSICS TEACHING BASED ON ARTIFICIAL INTELLIGENCE PROGRAMMING

Artificial intelligence (AI) technology, exemplified by DeepSeek, is reshaping the form and pattern of education and teaching in an unprecedented manner, pioneering a new paradigm in the field of physics education. This article integrates various AI tools to conduct innovative teaching practices for complex physical laws and models through dynamic visualization. Leveraging the Matlab and Python programming platforms, we carefully select “dynamic simulation of a highly adjustable snake pendulum” and “construction of atomic lattice imaging using scanning tunneling microscopy” as core teaching cases, striving to establish an innovative teaching model that encompasses “physical modeling-simulation replication-parameter tuning-autonomous exploration”. This teaching model effectively overcomes the physical limitations faced by traditional experimental setups, successfully constructs dynamic physical processes involving multiple parameters, and designs models with dynamically adjustable parameters through programming. Students undergo training from “observation” to “interpretation”, enabling them to gain a deeper understanding of abstract physical concepts, and their scientific research interests and innovation abilities are simultaneously enhanced. Furthermore, by guiding students to explore and replicate academic research frontiers, we greatly stimulate their interest in learning, exercise their systematic thinking, and thus promote students to actively engage in curiosity-driven learning and research.