Traditionally, users have perceived that only manual laborers or those in repetitive jobs would be subject to technological substitution. However, with the emergence of technologies like Midjourney, ChatGPT, and Notion AI, known as Artificial Intelligence-Generated Content (AIGC), we have come to realize that cognitive laborers, particularly creative designers, also face similar professional challenges. Yet, there has been relatively little research analyzing the acceptance and trust of artificial intelligence from the perspective of designers. This study integrates the TAM/TPB behavioral measurement model, incorporating intrinsic characteristics of designers, to delineate their perceived risks of AIGC into functional and emotional dimensions. It explores how these perceived characteristics, risks, and trust influence designers’ behavioral intentions, employing structural equation modeling for validation. The findings reveal the following: (1) designer trust is the primary factor influencing their behavioral choices; (2) different dimensions of perceived risks have varying degrees of impact on trust, with functional risks significantly positively affecting trust compared to emotional risks; (3) only by enhancing the transparency and credibility of Artificial Intelligence-Generated Content (AIGC) can the perceived characteristics of designers be elevated; and (4) only by effectively safeguarding designers’ legitimate rights and interests can perceived risks be significantly reduced, thereby enhancing trust and subsequently prompting actual behavioral intentions. This study not only enhances the applicability and suitability of AIGC across various industries but also provides evidence for the feasibility of intelligent design in the creative design industry, facilitating the transition of AIGC to Artificial Intelligence-Generated Design (AIGD) for industrial upgrading.

Despite the widespread recognition of artificial intelligence’s advantages, it cannot replace human independent thinking and creativity, especially in fields such as artistic design that require creativity. Previous studies often examined its development trends from the perspective of technical advantages or application processes. This study explores the attitudes and acceptance of creative industry practitioners towards Artificial Intelligence Generated Content (AIGC) from the perspective of user behavior modification. Utilizing the Stimulus-Organism-Response Model (SOR) as the theoretical background, this research integrates the Technology Acceptance Model, Theory of Planned Behavior, and Self-Efficacy to form the research framework. By employing a mixed-method approach combining quantitative and qualitative analyses, data from 226 designers were explored, and structural equation modeling was used to verify the correlations between endogenous factors. The results indicate that users’ facilitating conditions significantly influence self-efficacy, which in turn determines their intention to adopt AIGC. Additionally, semi-structured interviews revealed that factors hindering the widespread application of AIGC mainly encompass legal security, ethical risks, and fairness. This study extends the application scope of the Stimulus-Organism-Response Model (SOR), enriches the Technology Acceptance Model, and provides a new research framework for the application of AIGC in the creative industry, detailing the responsibilities, processes, and content of designers in the Artificial Intelligence Generated Design (AIGD) process.

Traditional safeguarding of intangible cultural heritage often prioritizes the preservation of craftsmanship, while allocating less attention to the innovation and activation of derivatives. This investigation extracts the identifiable intellectual property from Chinese woodblock New Year pictures, establishes a design framework to transition from two-dimensional to three-dimensional elements, and integrates generative design to examine the viability and adaptability of generative Design methods. Specifically, employing Chinese woodcut New Year pictures as a pivotal case, design constituents are elicited through interval questionnaires and on-site surveys. Subsequently, employing a comprehensive experiential approach and user journey mapping, a compendium of generative Design methods is distilled. Augmenting this, recommendations are formulated by considering generative design as a variable. Conclusively, through a dependent sample T-test, it is discerned that generative design effectively enhances both the quantity and caliber of design propositions. This inquiry dissects the plausibility of the generative Design approach for Chinese woodblock New Year pictures, extrapolating a blueprint for said derivatives and unearthing three-dimensional design concepts within the methodological framework. Ultimately, this endeavor facilitates the dynamic preservation and widespread propagation of intangible cultural heritage, concurrently bridging the chasm between planar and three-dimensional realms through innovative paradigms.

The trend in technological development is to utilize AI-based methods to optimize workflows and swiftly achieve desired objectives. Despite extensive exploration into the principles, acceptance, and application modes of new technologies, the research on the compatibility of artificial intelligence with work tasks remains limited. This study integrates the Stimulus-Organism-Response (SOR) model as a framework, combining Task Technology Fit (TTF), and Technology Acceptance Model (TAM), employing a mixed research approach that combines qualitative and quantitative methods to analyze data from 226 designers. Structural equation modeling is utilized to validate research hypotheses. The findings reveal that perceived usefulness and perceived security significantly influence designers’ behavioral intention to use Artificial Intelligence Generated Content (AIGC). Moreover, Technology Characteristics significantly impact technological compatibility and perceived ease of use, while technological compatibility significantly affects perceived usefulness. This study extends the application scope of the SOR theory, enriches the research on technological compatibility of AIGC in the creative design industry, explores the application process of human-machine collaborative innovation, provides valuable theoretical validation for the study of designers’ behavior in using AIGC, and discusses the key factors in transforming AIGC into Artificial Intelligence Generated Design (AIGD).

Artificial intelligence technology, based on big data algorithms, can replace much of the repetitive and rule-based work. However, whether AI can substitute the roles of artists or designers in the art and design industry, which prioritizes innovation and originality, remains a focal point of academic debate. This study, drawing on the Technology Acceptance Model (TAM) and the Technology Readiness Index (TRI), explores designers’ acceptance of AIGC and analyzes their usage intentions and influencing factors. The findings reveal that: (1) Perceived ease of use and technology readiness are the primary factors affecting designers’ adoption of AIGC, with technology readiness, representing personal traits, being particularly important. (2) Perceived usefulness has no significant impact on designers’ intention to use AIGC, suggesting that AI technology’s knowledge dissemination and skill training for users are largely complete. (3) Designers’ traits of innovativeness, optimism, and insecurity are key antecedents, influencing their usage intention through perceived ease of use, with insecurity being the critical area for improvement. (4) The Technology Acceptance Model is one of the most explanatory models in promoting new technologies but requires adjustments and extensions when applied to the creative design field. This study theoretically integrates the objectivity of TAM with the subjectivity of TRI, providing a more comprehensive understanding of users’ willingness and attitudes toward new technology, thereby offering theoretical support and empirical research for the integration of AI technology into the art and design industry.