Physics Education under the Application of Artificial Intelligence: Bibliometric Analysis Based on Web of Science Core Library (2021-2025)

TL;DR

This bibliometric analysis examines the rapid growth and evolving focus of AI applications in physics education from 2021 to 2025, highlighting key countries, research trends, and future directions.

Contribution

It provides a comprehensive overview of the development, hotspots, and interdisciplinary nature of AI in physics education through systematic bibliometric analysis.

Findings

Publications are increasing exponentially from 2023.

Main research countries are the US, China, and Germany.

Research focus has shifted to generative AI and neural networks in physics education.

Abstract

The rapid development of artificial intelligence technology is driving the transformation of physics education from traditional models to intelligent and data-driven approaches. To explore the evolution and cutting-edge hotspots in this field, this study conducted a systematic bibliometric analysis of 138 core literature published between 2021 and 2025 using VOSViewer and CiteSpace, based on the Web of Science Core Collection database. Research shows that the number of related publications will increase exponentially from 2023, with the United States, China, and Germany being the main research forces. The research focus has rapidly evolved from early machine learning assisted data analysis to the application of generative AI in teaching, the integration of physics information neural networks in computational physics courses, and the exploration of intelligent medical physics education. At present, this field is in the early stages of explosive growth and exhibits significant interdisciplinary characteristics. Future research should focus on building an adaptive learning ecosystem, reconstructing evaluation systems, and cultivating students' AI ethics and physical intuition.