Abstract
Facing increasingly complex innovation demands and design challenges, designers urgently need more effective tools and methodologies to stimulate creativity and break through cognitive limitations. To address this, this study proposes an innovative interdisciplinary approach—the AI-driven workflow, which aims to significantly enhance the conceptual design process. The rapid development of deep learning technologies in recent years has enabled AI not only to assist but also to actively participate in the innovation process, opening up new avenues for collaborative innovation between humans and machines. The core objective of this research is to explore how AI can improve designers' innovative efficiency by optimizing interactive workflows, with a particular focus on the evolution of designers' innovative thinking patterns and cognitive behaviors during AI-mediated inspiration search processes. Preliminary formative research indicates that AI-driven inspiration search can indeed effectively promote designers' innovative thinking, but it also reveals the limitations of existing methods. Based on designers' in-depth feedback, we developed the "Inspiration Bridge" system that integrates AI search and generation capabilities to systematically support creative thinking. Experimental results strongly validate the effectiveness of this system. On this basis, we put forward key recommendations for future AI-assisted design interactions, including enhancing the interpret ability of AI decisions, promoting seamless integration of multi-tools, improving AI-driven design guidance mechanisms, and implementing human-centered design concepts. These recommendations aim to comprehensively enhance the efficiency and user experience of AI-driven design tools.
Keywords
Artificial Intelligence; Human-Machine Collaboration; Generative AI; Design Creativity
DOI
https://doi.org/10.21606/iasdr.2025.365
Citation
Ma, R., Yang, Y.,and Xiao, Y.(2025) Bridges of Inspiration: AI-based Search and Generation for Inspiration-Driven Creative Workflow, in Chang, C.-Y., and Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.365
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 4 - Human-Centered AI
Bridges of Inspiration: AI-based Search and Generation for Inspiration-Driven Creative Workflow
Facing increasingly complex innovation demands and design challenges, designers urgently need more effective tools and methodologies to stimulate creativity and break through cognitive limitations. To address this, this study proposes an innovative interdisciplinary approach—the AI-driven workflow, which aims to significantly enhance the conceptual design process. The rapid development of deep learning technologies in recent years has enabled AI not only to assist but also to actively participate in the innovation process, opening up new avenues for collaborative innovation between humans and machines. The core objective of this research is to explore how AI can improve designers' innovative efficiency by optimizing interactive workflows, with a particular focus on the evolution of designers' innovative thinking patterns and cognitive behaviors during AI-mediated inspiration search processes. Preliminary formative research indicates that AI-driven inspiration search can indeed effectively promote designers' innovative thinking, but it also reveals the limitations of existing methods. Based on designers' in-depth feedback, we developed the "Inspiration Bridge" system that integrates AI search and generation capabilities to systematically support creative thinking. Experimental results strongly validate the effectiveness of this system. On this basis, we put forward key recommendations for future AI-assisted design interactions, including enhancing the interpret ability of AI decisions, promoting seamless integration of multi-tools, improving AI-driven design guidance mechanisms, and implementing human-centered design concepts. These recommendations aim to comprehensively enhance the efficiency and user experience of AI-driven design tools.