GDT Framework: Integrating Generative Design and Design Thinking for Sustainable Development in the AI Era

Yongliang Chen, Zhongzhi Qin, Li Sun (), Jiantao Wu, Wen Ai, Jiayuan Chao, Huaixin Li and Jiangnan Li
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Yongliang Chen: School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China
Zhongzhi Qin: School of Arts and Design, Hebei Design Innovation and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China
Li Sun: School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China
Jiantao Wu: School of Arts and Design, Hebei Design Innovation and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China
Wen Ai: North Automatic Control Technology Institute, Taiyuan 030006, China
Jiayuan Chao: School of Arts and Design, Hebei Design Innovation and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China
Huaixin Li: School of Arts and Design, Hebei Design Innovation and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China
Jiangnan Li: School of Arts and Design, Hebei Design Innovation and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China

Sustainability, 2025, vol. 17, issue 1, 1-28

Abstract: The ability of AI to process vast datasets can enhance creativity, but its rigid knowledge base and lack of reflective thinking limit sustainable design. Generative Design Thinking (GDT) integrates human cognition and machine learning to enhance design automation. This study aims to explore the cognitive mechanisms underlying GDT and their impact on design efficiency. Using behavioral coding and quantitative analysis, we developed a three-tier cognitive model comprising a macro-cycle (knowledge acquisition and expression), meso-cycle (creative generation, intelligent evaluation, and feedback adjustment), and micro-cycle (knowledge base and model optimization). The findings reveal that increased task complexity elevates cognitive load, supporting the hypothesis that designers need to allocate more cognitive resources for complex problems. Knowledge base optimization significantly impacts design efficiency more than generative model refinement. Moreover, creative generation, evaluation, and feedback adjustment are interdependent, highlighting the importance of a dynamic knowledge base for creativity. This study challenges traditional design automation approaches by advocating for an adaptive framework that balances cognitive processes and machine capabilities. The results suggest that improving knowledge management and reducing cognitive load can enhance design outcomes. Future research should focus on developing flexible, real-time knowledge repositories and optimizing generative models for interdisciplinary and sustainable design contexts.

Keywords: generative design thinking; cognitive model; sustainable design innovation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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