This talk will lay out the challenges and point to some potential approaches for the user experience design of dynamic, adaptive, predictive devices (such as the Nest Thermostat, the Amazon Echo, the Edyn water monitor, etc.) that use machine learning to create predictive models of people and sensors. The Internet of Things promises that by analyzing data from many IoT devices our experience of the world becomes better and more efficient. The environment predicts our behavior, anticipates problems, and intercepts them before they occur. The notion is seductive: an espresso machine that starts a fresh latte as you’re thinking it’s a good time for coffee; office lights that dim when it’s sunny and power is cheap. However, we don’t have good examples for designing user experiences of predictive analytics. Attendees will see examples of several different systems and leave with a list of UX challenges to creating behavioral systems, along with potential approaches to addressing those challenges.
What You’ll Learn
- What is behavior change design and how is it different from traditional UX?
- How can we influence behavior in digital interventions?
- What is the process for behavior change design?
- How do we know that digital interventions work?