Cognitive Regulation in Generative AI Use: A Network Study of Anxiety and Dependence Among Foreign Language Majors

Anxiety accompanying students’ reliance on generative AI has been linked to appraisals of their own competence and to comparison with peers, two complementary routes usually modeled separately. This study asked which nodes retain conditional associations with AI-related anxiety and dependence when both routes are modeled together. Survey responses from 283 foreign language majors formed a 17-node regularized partial correlation network, supplemented by written responses from 21 students. Social-comparison anxiety was the only anxiety node connected with dependence (partial r = 0.177, 95% bootstrap interval [0.077, 0.280]); exploratory facet analyses located it mainly in fear of falling behind or missing out, less consistently in peer competence pressure, and not in upward comparison. The three measured self-appraisal nodes, which assessed broad confidence, were unrelated to anxiety conditionally and at the zero-order level. The dependence association was modest, had no resolved direction, and was retained only in the fourth-year network; no cohort difference was established, and study year was confounded with collection period. Written responses did not corroborate this association but revealed integrity boundaries, guilt, and redefined language competence, which the survey omitted. The findings identify candidate correlates of reliance that require replication in independent samples, and content missing from AI anxiety instruments.