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. This research was funded by the Educational and Teaching Reform Project of Jiangxi Institute of Fashion Technology (Grant No.

School of Fashion Media, Jiangxi Institute of Fashion Technology, Nanchang, China De Institute of Creative Arts and Design, UCSI University, Kuala Lumpur, Malaysia Business School, Jiangxi Institute of Fashion Technology, Nanchang, China Graduate Business School, UCSI University, Kuala Lumpur, Malaysia School of Industrial and Art Design, Guangxi Eco-Engineering Vocational and Technical College, Liuzhou, China Design College, Zhoukou Normal University, Zhoukou, China Correspondence to Yang Li or Hwee Ling Siek. The authors declare no competing interests. This article does not contain any studies with human participants performed by any of the authors.

Written informed consent was obtained from all participants before data collection on 12 April 2025. The consent was obtained directly by the research team from adult participants, and it covered participation in the study, use of data for research purposes, and permission for publication of anonymised data. All participants were fully informed about the purpose of the research, the voluntary nature of their participation, and the measures to ensure anonymity and confidentiality.