Better together: designing for child-robot collaboration

The goal of this research is to design a system that encourages a child and robot to collaborate and learn together. My research has two key hypotheses: 1) The expressive motion of an "error-prone," non-humanoid robot can be algorithmically characterized to motivate a child to regularly assist it; 2) An "error prone" robot will provide social motivation for a child to learn and collaborate, which will demonstrably improve the learning outcomes of the child. These two hypotheses will be evaluated via user studies, child-robot interaction analysis, and child learning outcome assessments. Through these analyses, I intend to purposely cultivate the abilities of the robot, such that it can capitalize on the prosocial behavior of a child and minimize its own computational expenses. In so doing, robots can become better collaborators that are more accessible, educational, and cost effective.