Abstract
including Yu-Syuan Guo, Wan-Ting Jiang, and Cheng-Yuan Wu, Researchers, and Shyhnan Liou, Director of R&D, for their invaluable support and collaboration throughout this project. This study aims to explore the evolution of in-house design processes through AI technology. Historically, design projects lacked standardization, result- ing in inconsistent deliverables, costs, and quality, especially in cross-disciplinary projects. To address this, this research developed two AI design think- ing tool prototypes. The goal is to leverage generative AI's strength in organizing nonlinear data to assist enterprises with varying design application ex- periences. This includes structuring first-hand research data using natural language, enhancing asset ret rie vability and project development phase visibil- ity, enabling multi-modal data conversion and reuse, and exploring the potential for enterprises.
Keywords
Human-AI Interaction; Personalization; Object Recognition; Design strategies
DOI
https://doi.org/10.21606/iasdr.2025.478
Citation
Guo, Y.S., Jiang, W.T., Wu, C.Y.,and Liou, S.(2025) Design Asset Formation through AI-Driven Interpretation of Text and Visual Data, in Chang, C.-Y., and Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.478
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 4 - Human-Centered AI
Design Asset Formation through AI-Driven Interpretation of Text and Visual Data
including Yu-Syuan Guo, Wan-Ting Jiang, and Cheng-Yuan Wu, Researchers, and Shyhnan Liou, Director of R&D, for their invaluable support and collaboration throughout this project. This study aims to explore the evolution of in-house design processes through AI technology. Historically, design projects lacked standardization, result- ing in inconsistent deliverables, costs, and quality, especially in cross-disciplinary projects. To address this, this research developed two AI design think- ing tool prototypes. The goal is to leverage generative AI's strength in organizing nonlinear data to assist enterprises with varying design application ex- periences. This includes structuring first-hand research data using natural language, enhancing asset ret rie vability and project development phase visibil- ity, enabling multi-modal data conversion and reuse, and exploring the potential for enterprises.