@article{LIU2026, 
author = {Qihao LIU and Xiaorong YANG and Chengfang YAN},
title = {EXPLORING THE CONSTRUCTION OF PHYSICS VIRTUAL SIMULATION EXPERIMENTS BASED ON GENERATIVE AI: A CASE STUDY USING DEEPSEEK},
year = {2026},
journal = {Physics and Engineering},
volume = {46},
number = {4},
pages = {286-292},
keywords = {physics teaching, DeepSeek, HTML code, virtual simulation experiment, development paradigm, teaching model},
url = {https://www.sciopen.com/article/10.26599/PHYS.2026.9320445},
doi = {10.26599/PHYS.2026.9320445},
abstract = {This paper explores an innovative approach to developing lightweight physics virtual simulation experiments using generative AI, with DeepSeek as a case study. To address the shortage of extracurricular and contextualized virtual experiment resources, this study proposes a method in which users describe experimental requirements in natural language and DeepSeek generates simulation experiments that can run directly in a web browser. The paper also discusses several existing problems and corresponding solutions. This approach provides a practical reference for teachers to rapidly create dynamic demonstration tools and significantly lowers the technical threshold for developing virtual simulation resources. Based on this method, three related teaching models are further proposed, offering a feasible technical pathway for integrating AI into physics teaching.}
}