The integration of artificial intelligence (AI) technology and university physics experiments has become an important trend in the development of physics teaching. Exploring the pathways for the deep integration of these two areas is of great significance for realizing new models of digital and intelligent teaching. This study focuses on reservoir computing (RC), a machine-learning method used in nonlinear experiment teaching, and explores its application in synchronization prediction in coupled Chua’s circuit experiment teaching. It is found that RC can learn the four-voltage signals of the three nonsynchronous states of the coupled Chua’s circuit and accurately predict the resistance value needed for synchronization. This not only provides students with a convenient method for synchronous adjustment but also allows them to intuitively experience the great potential of AI technology in physics experiments. As a result, it enriches the digital and intelligent content of university physics experimental teaching.
WU et al. (2025) studied this question.