Application of Generative Artificial Intelligence in Physics Education: a Case Study of Electrostatic Field Simulation Using Python Programming
Year: 2025
Type: article
Abstract: This paper explores the application of generative artificial intelligence (AI) and Python programming in physics education. With the assistance of the generative AI tool DeepSeek, the authors carried out three iterative cycles to generate and refine Python code, ultimately developing an electrostatic-field simulation case. This demonstrates how generative AI can be leveraged to create and visualize such simulations. Practical experience shows that while AI can substantially lower the programming barrier for simulation experiments, the results it produces may deviate from actual physical laws and therefore require human verification and adjustment.
Source: Wuli yu gongcheng.
Authors: Zhangsheng YE, Shulin YANG
Institution: Huanggang Normal University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W7127941344
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