In this project, we explore how robotic products might autonomously adapt to the changes in their users’ behavioral, cognitive, and task states. We follow a transdisciplinary design process that draws on robotics, educational psychology, and neuroergonomics to design and evaluate the effectiveness of an adaptive educational robot. For instance, we have designed an adaptive educational robot that monitored its user’s levels of attention based on real-time signals it obtained from an EEG headset the user wore and adapted its behaviors in order to regain lost attention. We have also extended this work to MOOCs, creating an adaptive review system that recommended students what material to review based on how much attention they paid to different modules.

Student Lead: Daniel Szafir | Sponsor: Google, University of Wisconsin-Madison Graduate School

Papers: CHI 2012 Paper, CHI 2013 Paper

Patents: U.S. Patent # 20130260361