ARTIFICIAL INTELLIGENCE-EMPOWERED INNOVATIVE TEACHING CASES AND REFLECTIONS IN FUNDAMENTAL MECHANICS COURSES

This paper primarily presents two artificial intelligence (AI) teaching cases in a fundamental mechanics course, which are accomplished through collaboration among teachers, students, and artificial intelligence (teacher–student–AI collaboration). The first is a project-based practical case, in which AI is used to model the aerial twist and flip movements in freestyle skiing, helping students understand and apply the law of conservation of angular momentum. The second is an innovative-thinking inspiration case, in which AI is used to inspire students to think deeply about and explore the optimal design of beam cross-section shapes. The paper introduces the teaching scenarios and functions realized by these cases, as well as the resulting teaching models and inspiration for innovative thinking, with a focus on the value-added and cognitive impact brought by AI-assisted teaching.