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Introduction: With the widespread application of artificial intelligence (AI) technology in design education, exploring how AI tools shape students' innovative abilities has become increasingly important. Existing technology acceptance models mainly explain the adoption behavior of AI tools but have not examined how technological features influence innovation outcomes through user psychological processes. Methods: This study employs a cross-sectional quantitative design to examine how AI tool quality dimensions influence design innovation ability (DIA). Specifically, it tests the pathways through which interaction quality (IQT) and information quality (INQ) affect DIA via satisfaction (SAT) and intention to use (INU). Based on an online survey of 1,016 Chinese industrial design students, PLS-SEM data analysis was employed. Results: The study found that both IQT and INQ positively influence SAT. Subsequently, SAT, IQT, and INQ jointly affect INU. INU significantly predicts DIA. Mediating effect analysis confirmed that SAT plays a partial mediating role between the quality dimensions and INU. Discussion: Notably, the study reveals a pattern in which information quality exerts stronger effects than interaction quality, and direct functional evaluation outweighs affective mediation, both of which challenge conventional technology acceptance assumptions. The findings extend technology acceptance theory by identifying dual mechanisms, including direct functional and indirect emotional pathways, in AI-assisted creative education.
Wu et al. (Mon,) studied this question.