Experiential Speculation in Vision-Based AI Design Education: Designing Conventional and Progressive AI Futures

Yi-Ching Janet Huang, Stephan Wensveen, Mathias Funk

Artificial Intelligence (AI) will change how designers design, what they design, and why they design. Recent efforts have extended design education with AI and related machine learning (ML) concepts and technologies. Often, these courses are grounded in other fields such as Computer Science, Electrical and Mechanical Engineering, and Social Sciences. However, few courses teach AI concepts and technologies in combination with creativity, aesthetics, and speculation to leverage the expertise of design. We explored this combination to allow students to creatively design AI exemplars. In a nine-week design activity, students envision and prototype AI exemplars that are based on their personal vision and aesthetic values. This paper reports on our vision, course design, educational context, and learning activities with a focus on developing and integrating different areas of design expertise with AI technology. We contribute insights into 1) what was designed: six design cases, resulting in design speculations, prototypes, and critical reflections as well as our design critiques of these AI exemplars. Then, 2) how to design with a vision-based approach to AI design education in comparison to other approaches, and 3) a futuring landscape with three horizons unpacking why to design with AI.