The Impact of AI-Based Intelligent Assistance Systems on Student Learning: A Case Study of the Equations of Mathematical Physics Course

The Equations of Mathematical Physics course frequently engenders student anxiety and diminished motivation due to its highly abstract content and complex solution structures. Traditional teaching methods—characterized by knowledge spoon-feeding, delayed feedback, and disconnection from practical applications—further constrain learning efficacy. To address these challenges, this study implemented the Intelligent Tutoring System (ITS) "Xuetang-Cloud" as an intervention tool. The system constructs a structured knowledge graph for precise learning diagnostics and dynamically generates adaptive learning pathways based on real-time student performance data. Through continuous progress tracking and management, it enables dynamic personalized instruction. Empirical results demonstrate that the ITS's personalized incentive mechanisms—including real-time progress visualization and targeted resource recommendations—significantly enhance student motivation and self-directed learning. Research has shown that intelligent transportation system interventions that address learning anxiety in a timely manner can enhance students' confidence; Through case studies of embedded engineering, we aim to bridge the gap between theoretical application and ultimately achieve a synergistic improvement in students' knowledge internalization efficiency and STEM literacy.