Generative AI and Youth Creativity: An Extended TAM2 Perspective

Abstract

Background: Generative Artificial Intelligence is increasingly transforming learning, work, and creative activities among young people. However, limited evidence explains how technological, social, behavioral, and individual factors jointly influence creativity in Generative Artificial Intelligence-assisted environments. Objective: This study examines the determinants of Generative Artificial Intelligence usage and their effects on youth creativity by integrating the Technology Acceptance Model 2 and the Componential Theory of Creativity. Methods: A quantitative survey was conducted with 400 young users who had prior experience with Generative Artificial Intelligence. Data were analyzed using Cronbach’s Alpha, Exploratory Factor Analysis, Confirmatory Factor Analysis, and Structural Equation Modeling. Results: The proposed model demonstrated excellent fit, with a chi-square to degrees-of-freedom ratio of 1.373, Comparative Fit Index of 0.981, Tucker-Lewis Index of 0.978, and Root Mean Square Error of Approximation of 0.031. Social influence and perceived usefulness significantly promoted habitual use, while technology trendiness and innate creativity positively influenced usage purpose. Usage purpose was the strongest predictor of creativity enhancement, followed by habitual use and innate creativity. The model explained 62% of the variance in creativity enhancement. Conclusion: Generative Artificial Intelligence serves not only as a productivity tool but also as a catalyst for youth creativity. The integration of the Technology Acceptance Model 2 and the Componential Theory of Creativity highlights how technological, social, behavioral, and individual factors jointly contribute to creativity enhancement, with practical implications for education, policy, and technology development. Keywords: GenAI, Creativity, TAM2, Componential Theory of Creativity, SEM