Avoidant AI Use and Career Exploration Among University Students: The Curvilinear Moderating Role of AI-Shock Awareness

Some university students choose not to use artificial intelligence (AI) tools, yet research on students and AI has focused largely on adoption and has treated avoidance as maladaptive. That view cannot explain when avoiding AI might support, rather than undermine, student development. Drawing on transactional coping theory, threat-appraisal reasoning, and self-regulation theory, this study examined whether avoidant AI use is associated with career exploration and whether AI-shock awareness moderates that association linearly or curvilinearly. We hypothesized a positive main effect, a negative linear moderation, and a U-shaped curvilinear moderation. Survey data from 289 students at multiple Chinese universities (recruited via snowball sampling; 289 valid cases from 314 returned questionnaires) were analyzed with three-step hierarchical regression and bias-corrected and accelerated bootstrap confidence intervals (5000 resamples). Avoidant AI use was positively associated with both environment exploration and self-exploration. The linear interaction was negative in sign, but unreliable for environment exploration and only marginally supported for self-exploration. The quadratic interaction was significant for both outcomes: the association held at low awareness for both outcomes and at high awareness for self-exploration, but not at moderate awareness. For environment exploration, the high-awareness rebound rested on a small subgroup and is treated as preliminary. These results are consistent with the view that the developmental consequences of avoiding AI depend on the self-regulatory cost of maintaining that stance, a mechanism we treat as inferred rather than directly measured, and they carry implications for career education and AI-literacy instruction in higher education.