Beyond Novelty and Anxiety: Extending TAM to Understand Non-Design University Students’ Evaluations of Generative AI for Art Learning

Despite growing interest in the potential of generative AI art tools, systematic empirical evidence remains limited regarding how non-design university students evaluate such tools after use and the psychological factors associated with these evaluations. Drawing on the Technology Acceptance Model (TAM), this study developed an extended model integrating motivational, affective, and cognitive factors and analyzed data from 317 non-design university students in China using covariance-based structural equation modeling (CB-SEM). Participants received standardized operational guidance and completed a brief image-creation task using Midjourney under a standardized study procedure. Analysis of the full sample showed that 11 of the 15 proposed hypotheses were supported (73.3%). Hedonic motivation, perceived output quality, perceived usefulness, and perceived ease of use were significantly and positively associated with post-use evaluation, whereas AI anxiety was significantly and negatively associated with post-use evaluation. Self-efficacy was indirectly associated with post-use evaluation primarily through perceived ease of use, while the structural path from perceived ease of use to perceived usefulness was not significant. These findings provide empirical support for extending TAM to the context of generative AI-assisted art learning and offer insights into how non-design students evaluate such technologies and how cross-disciplinary participation in art learning may be supported in higher education.