Multi-Group Analysis.

As an emerging technological tool, generative AI profoundly reshapes higher education teaching and learning processes. Creativity, as a key component of university students’ core competencies, is significantly influenced by technological transformations. However, the mechanisms through which generative AI dependence affects creativity remain underexplored, especially among students majoring in sports. Grounded in Flow Theory, this study investigates the impact of generative AI dependence on the creativity of university students in sports-related disciplines. Using a snowball sampling strategy, the researchers surveyed 453 undergraduates in sports, assessing their self-reported responses across four constructs: generative AI dependence, self-efficacy, flow, and creativity. Partial Least Squares – Structural Equation Modeling was employed to uncover the complex relationships between generative AI dependence and creativity. The results reveal that generative AI dependence has a significant positive direct effect on creativity and an indirect effect mediated by self-efficacy. However, the mediating role of flow was not statistically significant. Furthermore, the multi-group analysis indicates that the positive effects of generative AI dependence on flow and creativity are more pronounced among male students. These findings offer theoretical insights and practical guidance for using generative AI to enhance creativity among students in sports programs.