a study on Technostress, Perceived Innovativeness, and AI Anxiety based on the UTAUT model approach
Description
Generative AI is increasingly integrated into design education, creating new requirements for the engineering and implementation of AI-enabled learning environments. However, limited research has examined how students’ psychological responses interact with technological and educational conditions during AIGC adoption. This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating technostress, perceived innovativeness, and AI anxiety as moderating factors to investigate the adoption mechanism of AIGC technologies in university design education. A structured survey was conducted among Chinese university students, yielding 489 valid responses from 562 participants. The proposed model was evaluated using partial least squares structural equation modeling (PLS-SEM). Results show that performance expectancy, effort expectancy, social influence, and facilitating conditions positively affect adoption intention and subsequent use behavior. Technostress negatively moderates the effect of performance expectancy on adoption intention but positively moderates the effect of social influence. AI anxiety strengthens the positive effects of both performance expectancy and social influence, whereas perceived innovativeness shows no significant moderating effect. These findings indicate that effective AIGC integration requires more than technological functionality; it also depends on the interaction between system conditions, instructional support, and students’ psychological responses. The study provides an engineering-oriented framework for designing and implementing AI-supported educational environments, with implications for technical infrastructure, instructional integration, and differentiated student support.