Artificial intelligence-assisted design management model with a human-centered approach

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

As artificial intelligence (AI) continues to reshape creative industries, design management is facing critical transformation. Despite growing interest in human-AI collaboration, current literature lacks a comprehensive and structured understanding of how socio-technical, cognitive, ethical, and organizational factors interact in design environments. Most existing studies either focus on isolated aspects such as algorithmic efficiency or ethical guidelines, while overlooking dynamic interplay among cultural, participatory, and managerial dimensions essential to human-centered innovation. Addressing this gap, the present study proposes and validates an interpretive structural model that maps the hierarchical interrelations among twelve key dimensions of AI-human collaboration in design management, grounded in the principles of Industry 5.0. The novelty of this research lies in its dual-layered methodology. It integrates interpretive structural modeling and MICMAC analysis with real-world validation through a case study involving a multidisciplinary design team. The results not only reveal foundational drivers such as Human-AI Co-Creation Culture and Human-Centered Value Integration but also highlight the dynamic roles of Explainability, Agile Management, and Organizational Readiness. Notably, the model was refined based on empirical insights, introducing new relational pathways and feedback loops that enhance its practical applicability. The findings demonstrate that successful AI-human collaboration requires a layered ecosystem of trust, transparency, cultural alignment, and adaptive capacity. This study contributes a validated, actionable framework that guides both researchers and practitioners in implementing ethical, effective, and scalable AI-assisted design strategies. By bridging conceptual modeling and field-based validation, it offers a significant step forward in operationalizing the values of human-centered design in AI-integrated environments.