Evolution of University Students’ AI Dependency and Creativity in the Technology Diffusion Process: A Four-Wave Repeated Cross-Sectional Study in China

Generative artificial intelligence is rapidly reshaping higher education, yet potential risks accompany over-reliance on these tools. Grounded in Rogers’ diffusion-of-innovations theory, the present study employed a mixed-methods design. Four waves of independent cross-sectional quantitative data (T1–T4, N = 788) were complemented by semi-structured interviews with 20 undergraduate students. Key patterns emerged as follows: (i) Across later waves, the direct association between external regulation and intrinsic motivation attenuated, while the indirect association operating via identified regulation strengthened. The path from identified regulation to intrinsic motivation was the only relationship that demonstrated significant cross-pair differences while satisfying invariance requirements for all three wave-pair comparisons. Because scalar invariance could not be established, this pattern is interpreted solely as a descriptive group-level temporal trend and should not be read as evidence of intra-individual change. (ii) AI-assisted creativity showed the largest and most consistent downward shift: estimates in later waves were significantly lower relative to the T1 baseline, with effect sizes increasing progressively (Δ = −0.391 to −0.549; d = −0.61 to −0.95; all p < 0.001 following Holm correction). This pattern remained largely unchanged after adjusting for gender, year of study, and academic major, and held up in four predefined subsamples as well as under propensity-score weighting. (iii) The linkage between AI dependency and creativity was significantly positive only at T3 and became negative but non-significant at T4, pointing to a conditional, wave-specific association rather than a stable linear relationship. (iv) Observed wave-level differences for AI dependency and intrinsic motivation did not withstand correction for multiple comparisons. Sensitivity tests yielded only two significant coefficients for AI dependency and none for intrinsic motivation; accordingly, these fluctuations are not given substantive theoretical interpretation. Retrospective qualitative accounts provided supplementary process-oriented context: participants reported initial exposure to AI driven by prevailing social trends, sustained use based on perceived utility, subsequent disappointment in unreliable or misleading outputs, and eventual shifts toward more strategic, self-regulated engagement. Given their retrospective nature, interview narratives were not used to triangulate or validate the quantitative findings. Collectively, this work suggests that as AI-supported learning becomes normalized within a diffusion-driven context, group-level indicators of students’ subjective creative experiences tend to decline. Higher-education institutions may therefore prioritize protecting learners’ creative agency as an explicit educational goal.