The influence of generative artificial intelligence on creative cognition of design students: a chain mediation model of self-efficacy and anxiety

Abstract

Introduction: This study investigated the role of generative Artificial Intelligence (AI) in enhancing the creative cognition of design students, examining the mediating effects of self-efficacy and anxiety reduction. Methods: A quantitative approach was employed, collecting data through online surveys from 121 design students at universities in southern China. The study utilized scales for AI knowledge and perception, self-efficacy, anxiety, and creative cognition, adapted from previous studies and evaluated on 5-point Likert scales. Data analysis was conducted using SPSS 24.0 for exploratory factor analysis and PROCESS v3.5 for mediation analysis. Results: The findings confirmed that AI positively impacted students’ innovative thinking (* β * = 0.610, * p * < 0.001). Self-efficacy (standardized * β * = 0.256, 95% CI [0.140, 0.418], * p * < 0.001) and anxiety reduction (standardized * β * = 0.093, 95% CI [0.018, 0.195], * p * < 0.05) positively mediated the relationship between generative AI and creative cognition. Additionally, a serial mediation effect through self-efficacy and anxiety reduction was observed (standardized * β * = 0.053, 95% CI [0.012, 0.114], * p * < 0.05). Discussion: Our empirical analysis demonstrates that AI positively affects design students’ innovative thinking, with self-efficacy and anxiety reduction serving as significant mediators. These findings provide valuable insights for educators and policymakers, suggesting that AI-integrated design curricula can significantly foster creative cognition, promote academic achievement, and enhance designer capabilities. Understanding AI’s impact on students’ creative processes is crucial for developing effective teaching strategies in today’s evolving educational landscape.