This study aims to explore the application value of the AIGC technology in campus transportation scheduling systems, design and implement an intelligent campus transportation scheduling system, and improve the quality of campus transportation services. Taking Hunan University of Technology as the practice base, user demand data are collected through questionnaires (n=104) and in-depth interviews (n=41), and 647 user ride data and the distribution of 26 fixed stops are collected during the two-month practice. A five-dimensional theoretical model containing cognitive experience, sensory experience, interactive experience, emotional experience and value experience is constructed according to the rooted theory. In system development, Cursor encoder is innovatively introduced to realize intelligent code generation and optimization, Python is adopted as the core development language, and CampusNetworkVis and TrafficMonitor classes are used to construct functional modules such as data visualization, intelligent scheduling, and decision support. The system achieves 91.3% scheduling accuracy during peak hours and a user satisfaction score of 4.6/5. By introducing a three-level warning mechanism and progressive visual cues, the user stress index is reduced by 35.7% (p < 0.05). In this study, the AIGC technology is successfully applied to the development of a campus transportation scheduling system, which not only significantly improves the system development efficiency, but also achieves the double optimization of scheduling accuracy and user experience. The research results provide a practical example for the development of a new generation of campus transportation service systems, and at the same time explore a new path for the application of the AIGC technology in the field of transportation scheduling.