open access
메뉴Background: Artificial Intelligence Generated Content (AIGC) technology has shown rapid advancement in the design field. However, problems such as the homogenization of artistic language had restricted the high-quality development of the design industry. This study analyzes Zhou Lingzhao's stamp design as a reference system to overcome this bottleneck. Purpose：The core objective of this study is to analyze the challenges associated with the application of AIGC technology, to extract the innovative paradigms and aesthetic strategies from the classic case of Zhou Lingzhao's stamp design, and to construct a practical path for AIGC to collaborate with hu-man-machine interactions in generating stamp designs. Methods: The research systematically organizes the status of AIGC technology and the connotative characteristics of Zhou Lingzhao's artistic idea adopting literature analysis and case study method, while exploring the symbiotic potential of the two at the level of creative mechanism. Results: Horizontal comparison shows that Zhou Lingzhao's stamp design method provides a triple optimization dimension for the application of AIGC technology in the field of design: 1) in terms of national expression; 2) concerning artistic technique; 3) in relation to emotional narrative. The existing AIGC-generated content is difficult to achieve the equivalent depth of artistic expression. Conclusion: This study proposes three major strategies for optimizing AIGC technology: Firstly, constructing a framework for human-computer collaborative creativity; Secondly, developing a national cultural knowledge graph; Thirdly, to develop domain-specific professional models. Eventually, an innovative path of integrating the evolution of intelligent technology with traditional artistic wisdom will emerge.
Background: The rapid advancement of artificial intelligence (AI) has accelerated the growth of AI-generated content (AIGC), with China's AIGC industry projected to surpass 70 billion yuan by 2026. AI-generated advertising is transforming the advertising landscape, however, research on its characteristics and their impact on consumer behavior remains limited compared to traditional advertising. Purpose: This study examines the entertainment value, informativeness, and innovativeness of AI-generated advertisements and their influence on consumers’ purchase intentions. Through a comparative analysis of multiple brand cases and empirical research, this study aims to develop a framework for analyzing the characteristics of AI-generated advertising and provide theoretical insights that can enhance intelligent advertising practices Methods: A literature review was conducted to synthesize prior research on advertising characteristics. Case studies of three distinct brands were analyzed to explore the features of AI-generated advertisements. Empirical research was employed to test theoretical hypotheses and assess how these characteristics affect consumers’ purchase intentions. Conclusion: Unlike prior research, which predominantly focuses on traditional advertising, this study highlights the role of entertainment, informativeness, and innovativeness in AI-generated advertisements. The results of the multi-brand case analysis and empirical research demonstrate that all three characteristics significantly enhance consumers’ purchase intentions, with informativeness exerting the strongest influence, followed by entertainment and innovativeness.
Background: Artificial Intelligence Generated Content(AIGC) technology has brought about profound changes to the paradigm of art creation, rendering the traditional assessment system in the field of digital art ineffective. As a result, there is increasing pressure to reevaluate the standard used to judge value in the art market. Purpose: This study aims to analyze how AIGC technology disrupts the cultural capital-based valuation paradigm of traditional art. It seeks to construct a new theoretical framework for digital art valuation by integrating Bourdieu’s cultural capital theory and Roland Barthes’ concept of the “death of the author.” The research proposes dynamic appraisal dimensions, such as algorithmic transparency and social consensus mechanisms, to reconcile technological innovation with the institutional norms of the art market. Methods: The research employs a mixed-methods approach, involving a critical interpretation of cultural capital theory within the context of algorithmic production. It features a comparative analysis of over 300 traditional and digital art auction records (2018–2024) from platforms like Christie’s and OpenSea. Additionally, in-depth case studies of high-profile disputes, including the 2023 Picasso-style AI artwork infringement case, will be conducted to trace shifts in value attribution. Findings: The study reveals a paradigm shift from “cultural capital accumulation” to “algorithmic capitalization,” where technical parameters and social participation metrics emerge as core value drivers. This change challenges the authority of traditional art institutions. It highlights the necessity of legal innovations, such as hybrid copyright systems recognizing “algorithmic authorship with human oversight,” to address issues realated to ownership. Conclusion: This research offers an interdisciplinary framework for reconstructing art valuation as a process that is integrated with technology and encourages public participation. It provides practical guidelines for art markets, including algorithmic audit protocols and decentralized governance models. Additionally, the study provides policy recommendations advocating adaptive legal frameworks to balance innovation and cultural heritage protection.
