Psychological mechanisms of perceived technological dependence on generative artificial intelligence tools: perceived knowledge, trust, anxiety, and self-efficacy

Generative AI tools are increasingly used in design education, yet the relationship between students' perceptions of these tools and their reported reliance on them remains unclear. This study analyzed cross-sectional survey responses from 526 undergraduate students in design-related disciplines using partial least squares structural equation modeling. In the specified model, perceived reliability, perceived usefulness, and perceived intelligence were positively associated with perceived AIGC knowledge, whereas perceived ease of use was not significantly associated with it. Perceived AIGC knowledge was positively associated with trust, self-efficacy, and usage intention and negatively associated with AIGC-related anxiety. Trust, AIGC-related anxiety, and self-efficacy each showed a significant indirect effect in the modeled relationship between perceived AIGC knowledge and usage intention. Usage intention was strongly associated with perceived technological dependence. These findings are consistent with the proposed framework, but the cross-sectional data do not establish the temporal sequence of the constructs or show that reported dependence reflects harmful overuse. The study extends technology acceptance research by examining perceived reliance in AIGC-supported creative work.