Abstract

Due to its interdisciplinary nature, research in design, more so than other disciplines, has to develop self-awareness to adapt to the inherent complexity of the contemporary world. This requires the use of big data as comprehensive self-descriptors, along with tools borrowed from the field of Artificial Intelligence (AI) to generate knowledge that researchers in this field can integrate with their own expertise to guide their research activities. We consider a large-scale set of about 170000 design-related scientific publications and leverage natural language processing, machine learning, and data visualization to explore and capture the evolution of the design community. We identify and visualize recurring themes and discussions that helped shape the field. Our findings suggest that research in design is becoming increasingly interdisciplinary and interconnected and that AI-driven approaches can shed light on the future of the discipline and provide valuable insights for researchers and practitioners in the field

Keywords

design evolution; design education; data visualization; artificial intelligence

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

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Jun 23rd, 9:00 AM Jun 28th, 5:00 PM

Mapping the evolution of design research: a data-driven analysis of interdisciplinary trends and intellectual landscape

Due to its interdisciplinary nature, research in design, more so than other disciplines, has to develop self-awareness to adapt to the inherent complexity of the contemporary world. This requires the use of big data as comprehensive self-descriptors, along with tools borrowed from the field of Artificial Intelligence (AI) to generate knowledge that researchers in this field can integrate with their own expertise to guide their research activities. We consider a large-scale set of about 170000 design-related scientific publications and leverage natural language processing, machine learning, and data visualization to explore and capture the evolution of the design community. We identify and visualize recurring themes and discussions that helped shape the field. Our findings suggest that research in design is becoming increasingly interdisciplinary and interconnected and that AI-driven approaches can shed light on the future of the discipline and provide valuable insights for researchers and practitioners in the field

 

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