Against the backdrop of the rapid development of artificial intelligence technology, big data-assisted teaching methods have brought new changes to university physics teaching. This study explores how to leverage big data technology and artificial intelligence algorithms to optimize the process of university physics teaching and enhance teaching efficiency and quality. By analyzing multidimensional data such as student learning behavior data, physics experiment data, and teaching effectiveness feedback, combined with artificial intelligence techniques such as machine learning and deep learning, we have constructed a personalized learning recommendation system, an intelligent evaluation and feedback mechanism, and an intelligent assistance platform for physics experiments. These innovative applications not only help teachers accurately grasp students' learning status and achieve differentiated teaching but also stimulate students' interest in learning and enhance their autonomous learning abilities. Practical results show that the integration of big data and artificial intelligence significantly improves the pertinence and effectiveness of university physics teaching, providing strong support for the modernization of physics education.

Gao Huiping. Research on Big Data-Assisted University Physics Teaching under Artificial Intelligence. International Journal of New Developments in Education (2025), Vol. 7, Issue 4: 30-35. https://doi.org/10.25236/IJNDE.2025.070405.

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