How Does Self-Regulated Learning Resist AI Dependence? A Mediating Effect Study Based on College Students Who Frequently Use AIGC Tools

This study examines the impact pathway and mediating mechanisms of self-regulated learning (SRL) on AI dependence among university students who frequently use Artificial Intelligence Generated Content (AIGC) tools. A questionnaire survey was conducted among 367 college students in Fujian Province in China. Structural equation modeling analysis revealed that: (1) SRL ability significantly negatively affects AI dependence (β = −.255) and further suppresses AI dependence through the mediating effect of reduced AIGC usage frequency, with the mediation effect accounting for 55.6%; (2) AIGC usage frequency positively exacerbates AI dependence (β = .656), with the highest dependence risk observed in course learning scenarios ( r = .379); (3) Significant group differences exist—female students are more adept at utilizing AIGC for self-directed learning and exhibit lower dependence, while senior students effectively balance tool usage by enhancing their SRL ability. The study reveals the dual inhibitory mechanisms of SRL in technology dependence (direct and mediating effects), emphasizing the need for metacognitive training and differentiated tool usage guidelines tailored to various learning scenarios in educational practice. This provides a theoretical basis for mitigating the risks of AI dependence and ensuring the beneficial application of AIGC tools as an “empowering rather than substitutive” resource.