GenAI Use and GenAI-Assisted Learning Procrastination in University Students: The Roles of Planned Behavior Constructs and Learning GenAI Anxiety

The rapid proliferation of generative artificial intelligence (GenAI) has reshaped university students’ learning practices, yet the association between GenAI-assisted learning and procrastination remains insufficiently understood. Drawing on the Theory of Planned Behavior as an established framework, this study offered a contextual extension of TPB and procrastination research into the GenAI domain by examining the association between students’ use of GenAI for learning task completion and GenAI-assisted learning procrastination among 1243 Chinese university students. Path analysis showed that students’ use of GenAI was negatively associated with GenAI-assisted learning procrastination (β = −0.195, p < 0.001). Behavioral attitude (β = −0.164, p < 0.001), subjective norm (β = −0.171, p < 0.001), perceived behavioral control (β = −0.331, p < 0.001), and behavioral intention (β = −0.137, p < 0.001) were each negatively associated with GenAI-assisted learning procrastination. The indirect associations linking GenAI use to GenAI-assisted learning procrastination through these Theory of Planned Behavior constructs were statistically significant, with the total indirect association accounting for approximately 30.9% of the total association (standardized indirect association = −0.086, p < 0.01). Learning GenAI anxiety moderated the association between behavioral intention and GenAI-assisted learning procrastination, with the negative association being stronger among students with higher anxiety (b = −0.268, SE = 0.040, p < 0.001) than among those with lower anxiety (b = 0.006, SE = 0.052, p = 0.915). The model explained 71.6% of the variance in procrastination in GenAI-assisted learning. Given the cross-sectional design and the sample’s specific characteristics, these findings should be interpreted as conditional associations rather than causal evidence, and their generalizability requires further investigation. Nevertheless, the results highlighted the roles of beliefs, intentions, and emotional experiences in understanding procrastination in GenAI-assisted learning.