Artificial Intelligence (AI) applications in different fields are developing rapidly, among which AI painting technology, as an emerging technology, has received wide attention from users for its creativity and efficiency. This study aimed to investigate the factors that influence user acceptance of the use of AIBPS by proposing an extended model that combines the Extended Technology Acceptance Model (ETAM) with an AI-based Painting System (AIBPS). A questionnaire was administered to 528 Chinese participants, and validated factor analysis data and Structural Equation Modeling (SEM) were used to test our hypotheses. The findings showed that Hedonic Motivation (HM) and Perceived Trust (PE) had a positive effect (+) on users’ Perceived Usefulness (PU) and Perceived Ease of Use (PEOU), while Previous Experience (PE) and Technical Features (TF) had no effect (−) on users’ Perceived Usefulness (PU). This study provides an important contribution to the literature on AIBPS and the evaluation of systems of the same type, which helps to promote the sustainable development of AI in different domains and provides a possible space for the further extension of TAM, thus helping to improve the user experience of AIBPS. The results of this study provide insights for system developers and enterprises to better motivate users to use AIBPS.

In recent years, the rapid advancement of artificial intelligence (AI) in the video sector has captured widespread attention. Despite the proliferation of AI video generation tools targeted at general consumers, there remains a gap in addressing the specialized requirements and advanced functionalities sought by industry professionals. This study aims to identify and overcome the barriers hindering the adoption of AI video tools in the Chinese video industry, thereby facilitating their seamless integration. The research unfolded in two phases employing a comprehensive methodology. Initially, we delineated the industry’s video usage standards, drawing upon both established norms and insights gleaned from seasoned industry experts through focus group interviews. These insights informed the formulation of semi-structured interview questions. Subsequently, in-depth semi-structured interviews were conducted with ten Chinese industry experts, leading to the identification of eight primary adoption barriers: innovation, market demand, technological maturity, cross-disciplinary collaboration, ethics and privacy, public acceptance, data security and copyright, and global perspectives and localization. In the second phase, a detailed questionnaire survey involving 401 Chinese industry practitioners validated these factors. A data analysis underscored the significant impact of these eight factors on industry adoption, particularly emphasizing technological maturity. Furthermore, exhaustive examinations and discussions were undertaken for each identified barrier. The findings of this study theoretically bridge the gaps in understanding the impediments to the application of AI video generation tools in the video industry. They offer valuable insights into the current application landscape and furnish pertinent suggestions for advancing technology promotion and development in the future. Ultimately, this research aspires to augment the integration and utilization of AI technology within the Chinese video production industry, thereby propelling its progress and development forward.

In recent years, research and applications related to the metaverse have garnered widespread attention in the field of online education. However, enhancing user experiences in metaverse learning remains a challenging issue. This study aims to explore how gamification enhances the metaverse learning experience by boosting learning motivation. In the first phase of the research, the relationship between gamification, learning motivation, and user learning satisfaction was examined. The results indicated higher user satisfaction with gamified metaverse learning experiences, with intrinsic and external regulations serving as mediating factors between gamification and learning satisfaction. In the second phase of the study, the five elements of gamification (challenge, reward, feedback, PBL, social interaction) were further validated for their role in enhancing learning motivation and, consequently, improving learning satisfaction. Notably, the rewarding element emerged as the most significant factor. These research findings hold practical significance for providers of metaverse learning experiences and the application of gamification in metaverse learning. They provide valuable insights for future research and practical implementation in this evolving field.

As an innovative form in the digital age, VR art exhibitions have attracted increasing attention. This study aims to explore the key factors that influence visitors’ continuance intention to VR art exhibitions using the expectation confirmation model and experience economy theory and to explore ways to enhance visitor immersion in virtual environments. We conducted a quantitative study of 235 art professionals and enthusiasts, conducted using the partial least squares structural equation modeling (PLS-SEM), to examine the complex relationship between confirmation (CON), Perceived Usefulness (PU), Aesthetic Experiences (AE), Escapist Experiences (EE), Satisfaction (SAT), and Continuance Intention (CI). The results show that confirmation plays a key role in shaping PU, AE, and EE, which in turn positively affect visitors’ SAT. Among these factors, AE positively impacts PU, but EE have no impact. A comprehensive theoretical model was then constructed based on the findings. This research provides empirical support for designing and improving VR art exhibitions. It also sheds light on the application of expectation confirmation theory and experience economy theory in the art field to improve user experience and provides theoretical guidance for the sustainable development of virtual digital art environment.

This research investigates the key factors influencing young users’ continuous use of digital twin-enhanced metaverse museums. Attracting young users to use the metaverse museum for a more extended period consistently contributes to increasing the frequency of visits and content usage and promoting its sustainable development and innovation. However, there is a lack of research on the key factors influencing young users’ continuous use of digital twin-enhanced metaverse museums, which makes the theoretical basis for the in-depth design of user motivation for metaverse museums insufficient. This study constructed a model covering four dimensions—hedonic, utilitarian, social, and technological—based on communication’s uses and gratification theory (UGT). It was validated in the Metaverse Digital Twin Museum (MDTM). Using Spatial.io’s IES Goya Museum as the experimental platform, the research team conducted Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4.0 software through experiments and surveys with 307 participants aged 18 to 30. Quantitative analyses revealed that MDTM’s hedonic gratification (hope), utilitarian gratification (information and self-presentation), social gratification (social interaction and social presence), and technological gratification (immersion) significantly influenced young users’ continued intention. The findings reveal that these six key factors can be the focus of MDTM’s future development to enhance user experience. This study fills the gap in applying UGT in the field of metaverse museums, provides metaverse museum managers with references to the key factors that can prolong users’ continued intention to use, and points out the key factors that need further attention in future research and practice.

With the rise of the metaverse, digital transformation is profoundly affecting the field of art exhibitions. Museums and galleries are actively adopting metaverse technologies to present artworks through virtual platforms, providing audiences with novel opportunities for immersive engagement and art experiences and shaping high-quality user experiences. However, the factors influencing user engagement in the metaverse art exhibition platform (MeAEP) remain unclear in the current research. This research combines the information systems success model (ISSM) and the hedonic motivation system adoption model (HMSAM) to construct a theoretical model that provides insights into the factors influencing MeAEP users’ intention to engage and their immersion behavior, with a focus on the sustainability of the art exhibition. We quantitatively analyzed 370 users that experienced MeAEP and analyzed the data and measurement model using SPSS 27 and partial least squares structural equation modeling (PLS-SEM). The results showed that information quality (IQ), system quality (SQ), and perceived ease of use (PEOU) significantly and positively influenced perceived usefulness (PU), curiosity (CUR), joy (JOY), and control (CON). PU, JOY, and CON have a positive and significant effect on Immersion (IM). Finally, PU, CUR, JOY, and CON had a positive effect on behavioral intention (BI). In conclusion, only one of the twenty hypotheses was not supported. The research findings not only enrich the academic and managerial theories related to the metaverse and art exhibition platforms, but also provide practical insights for administrators, developers, and MeAEP designers to create higher-quality and more immersive art content, as well as provide constructive ideas for the sustainability of art exhibitions to further enhance user experience.