Understanding the Behavioral Intention of Generative AI Among Art and Design Students: An Integrated Framework of Self-Determination Theory and Technology Acceptance Model

Abstract

Although the Technology Acceptance Model (TAM) has been widely used to explain users’ adoption of technologies, its explanatory power in creative disciplines such as art and design remains constrained. This limitation arises mainly from the neglect of intrinsic psychological factors associated with creative engagement. To address this gap, this study developed an integrated model combining self-determination theory (SDT) with the TAM to examine the psychological mechanisms underlying art and design students’ adoption of generative artificial intelligence (AI). A total of 398 valid responses collected from art and design students were analyzed using structural equation modeling (SEM). The analysis showed that intrinsic motivation was positively associated with behavioral intention, and its standardized path coefficient was numerically larger than those of the two classical TAM variables: perceived usefulness and perceived ease of use. The results also indicated that psychological need satisfaction was indirectly associated with behavioral intention through intrinsic motivation and cognitive evaluations. Theoretically, this study contextualizes and empirically tests the integrated SDT-TAM framework within art and design education. Practically, it has implications for educators seeking to support the responsible integration of generative AI into art and design education.