Comparative Analysis of AI-Assisted Design in East Asia and Europe

Thesis Abstract

This study investigates how AI-assisted design practices differ between East Asia and Europe, addressing concerns about cross-cultural adoption, design autonomy, and ethical implications of algorithmic creativity within contemporary art and design education and industry. The problem centers on whether AI-enabled design workflows yield comparable creative outcomes, user experiences, and professional competencies across regionally distinct design cultures, and how regional values, regulatory environments, and tooling ecosystems shape these practices. The aim is to illuminate comparative patterns and convergences in AI-assisted design to inform pedagogy, policy, and industry standards. Specific objectives are (1) to map the prevalence and modes of AI tool usage (generative design, constraint solvers, and perceptual models) among professional designers and design students; (2) to evaluate perceived design quality, efficiency, and ethical considerations across regions; (3) to examine how organizational context (firm size, sector, and collaboration networks) moderates AI adoption and outcomes; (4) to identify skill requirements and training gaps in AI literacy for designers; and (5) to propose a cross-regional framework for responsible AI-driven design practice. Methodologically, the study adopts a comparative mixed-methods design combining cross-sectional surveys and qualitative interviews. The population comprises professional designers and design students in industrial, product, and graphic design sectors across six metropolitan centers Tokyo, Seoul, Shanghai, Shenzhen, Berlin, and Milan. A stratified random sample of 480 respondents (240 industry practitioners and 240 students) will be selected, ensuring proportional representation by sector and experience. Data collection instruments include a structured questionnaire developed from validated scales measuring AI tool usage, perceived design quality, workflow efficiency, ethical considerations, and design competency, augmented by semi-structured interviews with 36 participants (6 per city) to capture contextual nuances. Instrument validity will be established through expert review and pilot testing, with reliability assessed via Cronbach's alpha and composite reliability. Data analysis will proceed in three stages. Quantitative data will be analyzed using descriptive statistics to profile usage patterns, multivariate regression to identify predictors of perceived design quality and efficiency, and multigroup structural equation modeling (SEM) to test regional invariance across East Asia and Europe, incorporating latent constructs for AI proficiency, organizational support, and ethical stance. Thematic analysis will be applied to interview transcripts, guided by Braun and Clarke's framework, to extract regional narratives around creativity, authorship, and governance of AI-assisted design. Triangulation will synthesize quantitative and qualitative insights, while sensitivity analyses will test the robustness of results to sample composition and tool type. Ethical considerations include informed consent, data anonymization, and compliance with regional data protection regulations. The theoretical framework interlaces the Technology Acceptance Model (TAM) with the Responsible AI design paradigm and sociotechnical theory to account for human-centered design practices, organizational structure, and cultural value orientations. The study expects to reveal both convergences and divergences in AI-assisted design East Asian contexts may show higher integration of AI in rapid prototyping and manufacturing ecosystems, with heightened attention to efficiency and standardization, whereas European contexts may prioritize ethical governance, user autonomy, and critical discourse on machine authorship. Anticipated findings include significant regional differences in perceived design quality linked to tool maturity and workflow integration, and a stronger emphasis on governance and ethics in Europe compared to East Asia, where performance and speed may dominate. The study contributes to knowledge by empirically detailing cross-regional design practices, informing design education curricula, and guiding policy on AI ethics, intellectual property, and interoperability standards in design software ecosystems. It offers a validated cross-regional framework for assessing AI-assisted design readiness and provides recommendations for curriculum development, industry training programs, and transregional collaboration models to foster responsible and innovative AI-driven design practices. The main conclusion is that while AI-assisted design yields comparable creative outputs across regions, the governing priorities—efficiency versus ethics—reflect deeper sociocultural and regulatory contexts, suggesting that holistic, regionally informed strategies are essential for the sustainable integration of AI into design practice. Recommendations emphasize enhanced cross-border knowledge exchange, standardized ethical guidelines for AI-generated design, and targeted professional development to bridge AI literacy gaps among designers.