AI in design education: Factors affecting students' professional learning adaptability

Against the backdrop of accelerated reconstruction of the design-education ecosystem by artificial intelligence, this study focuses on the core issue of insufficient adaptability of design-major college students to AI-supported learning environments and examines the factors and mechanisms that influence their adaptability. Through a survey employing a questionnaire, 784 valid responses were gathered and subjected to empirical analysis using a structural equation model. The study reveals that the overall level of professional learning adaptability is moderately high. Notably, five factors-learning motivation and goals, learning self-efficacy, teacher support and teaching intervention, resource platforms and technical environment, and intelligent literacy-exert significant positive influences on professional learning adaptability. Particularly, learning motivation and goals, along with learning self-efficacy, exhibit the most substantial direct effects. Teacher support and teaching intervention indirectly bolsters adaptability by reinforcing learning motivation and self-efficacy. This study introduces and validates a novel five-factor model within the context of AI-supported design professional education. This model contributes to the theoretical underpinning of learning adaptability and furnishes empirical support for universities seeking to bolster students' adaptability through curriculum restructuring, tailored interventions, and the establishment of an intelligent education environment. Furthermore, it presents feasible strategies for advancing design education, focusing on three key areas: the synergistic stimulation of motivation and efficacy, customized interventions for diverse student cohorts, and the enhancement of the resource-literacy continuum.