Abstract
Artificial Intelligence (AI) and Machine Learning (ML) are technologies that impact the skills and practices of the next generation of designers, presenting the chance to reimagine 21st-century design education. Having a structured knowledge that stands as a multidisciplinary reference for design education is still a challenging aspect of this re-imagination. The paper presents a design-stage-oriented framework for introducing AI and ML into design education. The framework structures taxonomies of AI tools, data types performed by these systems, AI capabilities and the stages of a design process. It leads to an interactive user workflow, serving as a foundational component that enables teachers and students to explore the broad possibilities of co-design with AI tools. The paper discusses the potential impact and implications of the framework on design education and the initial validation in a workshop involving multidisciplinary teachers and students.
Keywords
design education; design-ai taxonomies; artificial intelligence; design process
DOI
https://doi.org/10.21606/drs.2024.535
Citation
Botta, M., Autuori, A., Terenghi, G., and Subet, M. (2024) A Design-Stage-Oriented Framework to Introduce Artificial Intelligence and Machine Learning in Design Education, in Gray, C., Ciliotta Chehade, E., Hekkert, P., Forlano, L., Ciuccarelli, P., Lloyd, P. (eds.), DRS2024: Boston, 23–28 June, Boston, USA. https://doi.org/10.21606/drs.2024.535
Creative Commons License
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Conference Track
Research Paper
Included in
A Design-Stage-Oriented Framework to Introduce Artificial Intelligence and Machine Learning in Design Education
Artificial Intelligence (AI) and Machine Learning (ML) are technologies that impact the skills and practices of the next generation of designers, presenting the chance to reimagine 21st-century design education. Having a structured knowledge that stands as a multidisciplinary reference for design education is still a challenging aspect of this re-imagination. The paper presents a design-stage-oriented framework for introducing AI and ML into design education. The framework structures taxonomies of AI tools, data types performed by these systems, AI capabilities and the stages of a design process. It leads to an interactive user workflow, serving as a foundational component that enables teachers and students to explore the broad possibilities of co-design with AI tools. The paper discusses the potential impact and implications of the framework on design education and the initial validation in a workshop involving multidisciplinary teachers and students.