User experience factors influencing art professionals’ use of AI painting tools: an extended technology acceptance model in human–AI interaction

Artificial Intelligence Painting Tools (AIPT) have emerged as human-AI co-creative systems that enable professional artists to collaborate with generative algorithms throughout the creative process. However, the mechanisms underlying the adoption and sustained use of these interactive tools among professional artists remain insufficiently understood, particularly from the user experience (UX) and usability perspectives. To address this gap, this study proposes an Extended Technology Acceptance Model (ETAM) that integrates core Technology Acceptance Model (TAM) constructs with UX factors to explain art professionals’ adoption behaviour toward AI-assisted technologies. Survey data collected from 465 art professionals in Nanchang were analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). Results indicate that creativity (CRE), hedonic motivation (HM), and AI literacy (AIL) significantly enhance both perceived usefulness (PU) and perceived ease of use (PEOU); while social influence (SI) primarily affects PU, and self-efficacy (SE) mainly affects PEOU. PEOU positively influences PU, and both contribute to behavioural intention (BI), which subsequently predicts use behaviour (UB). Several conventional TAM/UTAUT relationships, including the effects of facilitating conditions and selected direct pathways, were not supported. In addition, perceived trust (PT) significantly moderates the relationship between BI and UB. The findings demonstrate that intrinsic experiential and creative factors play a more influential role than the external environmental determinants in shaping AI painting tool adoption among professional artists. Theoretically, the study extends TAM by integrating creativity, AI literacy and trust moderation within a professional artistic context, identifying trust as a critical boundary condition and highlighting the importance of experiential factors in AI-assisted creative adoption. Practically, it provides insights for the user-centred design and sustainable deployment of AIPT.