Evaluating the Impact of Mobile-Based Generative AI Tools on Visual Design Education

Abstract

Mobile-based generative artificial intelligence (AI) tools have been increasingly adopted in visual design education. These tools have introduced multidimensional impacts on traditional educational models. However, significant gaps remain in current research regarding their application in visual design education. Existing studies have predominantly addressed the general educational use of generative AI tools, while the specific affordances of mobile platforms and the disciplinary characteristics of visual design have not been sufficiently integrated. Furthermore, research methodologies have largely relied on surveys and interviews, with acceptance modeling limited to surface-level analysis. Some critical factors have been underexplored, resulting in constrained accuracy and comprehensiveness in predicting user acceptance. Centering on the context of visual design education, this study investigated the acceptance of mobile-based generative AI tools among teachers and students. The study was conducted in two parts. First, an acceptance model was constructed by incorporating technological attributes, pedagogical demands, and disciplinary background to identify key influencing factors and their underlying mechanisms. Second, based on the proposed model, scientific prediction methods were employed to dynamically forecast acceptance levels among different types of teachers and students across various teaching stages. This study aims to provide a theoretical foundation for the deep integration of mobile-based generative AI tools in visual design education, thereby supporting the refinement of instructional strategies and fostering the cultivation of high-caliber design professionals equipped to adapt to technological transformation.