Abstract
This study empirically investigates how data can strengthen multidisciplinary collaboration and explores the potential role of generative AI (GAI) as a complementary enabler. Eight teams, composed of designers and business professionals, participated in co-creation workshops where the availability of Big Data and Thick Data was systematically manipulated. The resulting ideas were evaluated across three dimensions: ‘Novelty’, ‘Usefulness’, and ‘Commercial Appeal’. Additionally, follow-up interviews were conducted to capture participants’ reflections on the collaborative idea generation process, utilizing the collected data. The findings show that teams with access to both Big Data and Thick Data produced qualitatively superior ideas compared to those that did not. Data was shown to function not merely as a validation tool but as a catalyst that refines problem definition, clarifies goal setting, and fosters shared understanding among participants, thereby enhancing idea quality. At the same time, participant experiences revealed structural challenges, including difficulties in data interpretation, friction arising from disciplinary perspectives, and reliance on heuristics. Based on these insights, the study identifies opportunities for GAI—particularly in large language models (LLMs)—to support data integration and interpretation, keyword clustering, and the expansion of brainstorming. By doing so, GAI could contribute to making data-informed collaboration more contextual and inclusive. This research provides empirical evidence that data-informed design is a practical approach to improving the qualitative outcomes of multidisciplinary collaboration. It further suggests that GAI holds promise as a complementary tool to augment this process. Future work will focus on developing GAI-supported scenarios, especially for the analysis phase, defining functional requirements, and designing tools that facilitate more effective data-informed design.
Keywords
Data-informed; Co-creation; Multidisciplinary collaboration; Generative AI; GAI Assistant
DOI
https://doi.org/10.21606/iasdr.2025.794
Citation
Lee, M.,and Lee, Y.(2025) Data-Informed Design in Multidisciplinary Collaboration: Empirical Findings and Directions for Generative AI, in Chang, C.-Y., and Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.794
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 6 - Co-creation
Data-Informed Design in Multidisciplinary Collaboration: Empirical Findings and Directions for Generative AI
This study empirically investigates how data can strengthen multidisciplinary collaboration and explores the potential role of generative AI (GAI) as a complementary enabler. Eight teams, composed of designers and business professionals, participated in co-creation workshops where the availability of Big Data and Thick Data was systematically manipulated. The resulting ideas were evaluated across three dimensions: ‘Novelty’, ‘Usefulness’, and ‘Commercial Appeal’. Additionally, follow-up interviews were conducted to capture participants’ reflections on the collaborative idea generation process, utilizing the collected data. The findings show that teams with access to both Big Data and Thick Data produced qualitatively superior ideas compared to those that did not. Data was shown to function not merely as a validation tool but as a catalyst that refines problem definition, clarifies goal setting, and fosters shared understanding among participants, thereby enhancing idea quality. At the same time, participant experiences revealed structural challenges, including difficulties in data interpretation, friction arising from disciplinary perspectives, and reliance on heuristics. Based on these insights, the study identifies opportunities for GAI—particularly in large language models (LLMs)—to support data integration and interpretation, keyword clustering, and the expansion of brainstorming. By doing so, GAI could contribute to making data-informed collaboration more contextual and inclusive. This research provides empirical evidence that data-informed design is a practical approach to improving the qualitative outcomes of multidisciplinary collaboration. It further suggests that GAI holds promise as a complementary tool to augment this process. Future work will focus on developing GAI-supported scenarios, especially for the analysis phase, defining functional requirements, and designing tools that facilitate more effective data-informed design.