DIP into the Future: Building a Design Curriculum to Enable Design Students to Work with Machine Learning

Machine learning (ML) is used to enhance an increasing number of intelligent products, yet it is not a well-explored topic in user experience (UX) design education. ML is iteratively trained using collected data and its technical properties (e.g., accuracy rate) constantly change when it is used. It would be extremely difficult for UX design students to understand ML technology, not to mention identify design opportunities around ML, or implement an ML-enhanced design proposal. In particular, the growable nature of ML, such as its unpredictable, ever-changing, and data-driven nature, makes it difficult to apply ML in ideation and prototype. To prepare design students to work with ML as a growable computing/design material in UX design, we built a design course called the Design of Information Products (DIP). To this end, we designed a teaching infrastructure that enables design students to skilfully ideate and prototype with ML technology. We also proposed a cycling pedagogical method tailored to the background of design students and the lifecycle of ML. Finally, we presented the design projects of DIP and shared the experience of having design students work with ML. The outcomes of the DIP course and the feedback of students reveal that our work helped design students foster ML literacy to understand its growable nature and thus creatively ideate and practically prototype ML-enhanced products. Besides, we shared the lessons learned from building DIP course and highlighted the direction for developing a future ML-related curriculum.