Date
Publisher
arXiv
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.
Study at a glance
- Question: How has research on artificial intelligence in physics education evolved in volume, geography, and thematic focus?
- Setting and data: 138 peer-reviewed articles from the Web of Science Core Collection, published between 2021 and 2025 across 62 countries.
- Method: Bibliometric analysis and visualization mapping (using VOSviewer and CiteSpace).
Why it matters
Artificial intelligence is driving a transformation in physics education, yet systematic analyses examining the global application status, research hotspots, and evolutionary trends in this emerging field remain rare.
Key findings
- Publications on AI in physics education grew sharply starting in 2023, with the United States, China, and Germany leading in total research output.
- The research focus evolved from basic machine learning data analysis into generative AI applications, physics-informed neural networks in computational physics, and intelligent medical physics education.
- A widely cited thread of literature centers on evaluating ChatGPT's performance on introductory physics courses and standardized exams, highlighting both potential and beginner-level errors.
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