Author(s): LI, J., Institution: WUHAN UNIVERSITY OF TECHNOLOGY, Country: CHINA, Abstract-ID: 825

With the advancement of digital campuses, AI sports attendance systems have been integrated into the physical education evaluation systems of Chinese universities. While these systems encourage students to exercise and set a minimum required frequency, the negative impact of "rigid monitoring" driven by algorithms needs to be examined. Based on Self-Determination Theory (SDT), this study explores how AI attendance systems affect students' exercise motivation and sense of psychological fairness. We conducted a random survey and in-depth interviews with 500 university students who use AI attendance system. The study tests three main issues: 1) whether the system weakens students' sense of competence and autonomy; 2) whether evaluation mechanisms that rely too much on physical data (such as GPS tracks and movement frequency) cause a sense of unfairness; and 3) whether algorithmic errors and the lack of appeal ways lead to student resentment. This research aims to build a human-machine collaborative evaluation model. We suggest introducing explainable algorithms to increase transparency and increasing the weight of human evaluation for subjective effort. These steps will help physical education return to its core goal of inspiring lifelong exercise motivation.