ADAPTING TO THE NOBEL PRIZE CHANGES WITH FEYNMAN'S METHOD: THE SHIFT IN THINKING AND PATH RECONSTRUCTION FOR UNIVERSITY PHYSICS TEACHING IN THE AI ERA

In 2024, the Nobel Prize in Physics was awarded to John Hopfield and Geoffrey Hinton, pioneers in the field of artificial neural networks. This event signals a new stage in the deep integration of physics with artificial intelligence and other frontier interdisciplinary fields. Against the dual background of the AI era and the implications of this Nobel Prize, this paper systematically examines the practical challenges facing university physics teaching in terms of teaching content, instructional methods, and the cultivation of students' scientific thinking. It points out that current teaching still shows a significant structural mismatch with the development of modern science and technology as well as the learning needs of different disciplines. Guided by Feynman's educational philosophy of “thinking first, concrete expression, and output-driven learning”, this paper proposes a thinking-centered reconstruction path for university physics teaching. Specifically, teaching objectives should shift from knowledge transmission to the cultivation of physicist-like thinking; teaching content should expand from a classical physics framework to an interdisciplinary “physics + AI” model; the teaching process should move from one-way lecturing to output-driven active inquiry; and teaching evaluation should shift from result-oriented assessment to multidimensional evaluation of thinking ability. Finally, based on the ARCS motivation model, an introductory teaching unit on “AI and Physics” is designed to demonstrate the feasibility and effectiveness of this Feynman-inspired reconstruction in stimulating learning motivation, cultivating scientific thinking, and promoting innovative ability. This study provides a new perspective and practical reference for the transformation of university physics teaching in the AI era.