Unveiling college students’ adoption of AIGC in design learning: an integrated model

Abstract This study investigates the factors that influence college students’ intention to use artificial intelligence generated content (AIGC) technology in design learning. An extended technology use and diffusion model is proposed and validated by integrating the artificial intelligence device use acceptance (AIDUA) model with the innovation diffusion theory (IDT). The present study collected data from 385 Chinese college students majoring in design through online surveys. The proposed model, which includes technology concerns, emotional acceptance, and behavioral transformation, was empirically tested using structural equation modeling (SEM) on data collected from students across different academic levels. The research findings suggest that, in the first stage, relative advantage and compatibility exert a significant and positive influence on both performance expectancy and effort expectancy. However, complexity negatively affects both. In the second stage (emotional acceptance), effort expectancy has a highly positive and significant influence on the adoption and diffusion. Conversely, the impact of performance expectancy on use and diffusion is not substantial. In the final stage (behavioral transformation), both social influence and individual innovation positively and significantly impact the use and diffusion of AIGC. Thus, the empirical results support the integration of AIDUA and IDT. This study provides a conceptual AIGC use and diffusion framework that other researchers can use to investigate AIGC-related topics in design learning.