A Study of Factors Influencing Chinese Design Students’ Adoption of AIGC Tools - Xiang Wang; Mingxing Li; Yu Yao; Xiaoyang Zhu (IEEE Access)
As a critical factor in determining a product’s success, users’ willingness to adopt artificial intelligence-generated content (AIGC) tools is a key driver for their sustainable development. As a new productivity tool, AIGC tool adoption by design students is influenced by various factors. However, there is a lack of systematic research on this topic in the academic community, which hinders the sustainable development of AIGC tools. Based on this, the study combines the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-Technology Fit (TTF) models to construct a dual-perspective model—integrating “technology (external)” and “individual (internal)” dimensions—to investigate the key factors influencing design students’ willingness to adopt AIGC tools in their professional learning. A questionnaire was distributed to Chinese design students, resulting in 517 valid responses. The structural equation model (SEM) analysis yielded the following findings: Characteristics of AIGC Technology (CoAT) and students’ task characteristics positively influence task-technology fit. CoAT positively affect performance expectations (PE) but have no significant impact on effort expectations (EE). Task-technology fit positively influences use behaviors and performance expectations. Additionally, performance expectations, effort expectations, and social influences (SI) significantly enhance willingness to use, which in turn drives use behaviors (UB). Conversely, facilitating conditions (FC) do not affect use behaviors. Based on the empirical findings, this study discusses strategies for enhancing design students’ adoption of AIGC tools and provides recommendations for promoting their adoption and sustainability in design education.eing rejected by search engines.