A Multimodal Approach to Adaptive Dialogue Interaction for Learning Companion Robots


Learning companion robots that support spoken natural language dialogue present exciting opportunities for adaptive and personalized interaction. We hypothesize that adaptive robot behaviors can have a positive effect on student motivation. Supporting these adaptive behaviors in real-time requires that learning companion robots construct and dynamically update models of student motivational state. Our project examines the utility of speech prosody in contributing to a dynamic model of student motivational state in human-robot peer tutoring interaction. The project involves collecting spoken dialogue data with a learning companion robot, measuring the motivational state of students, and modeling the relationship between speech prosody and student motivation.