Understanding Rapport over Multiple Sessions with a Social, Teachable Robot

Abstract

Social robots have been shown to be effective educational tools. Rapport, or interpersonal closeness, can lead to better human-robot interactions and positive learning outcomes. Prior research has investigated the effects of social robots on student rapport and learning in a single session, but little is known about how individuals build rapport with a robot over multiple sessions. We reported on a case study in which 7 middle school students explained mathematics concepts to an intelligent teachable robot named Emma for five sessions. We modeled learners’ rapport-building linguistic strategies to understand whether the ways middle school students build rapport with the robot over time follow the same trends as human conversation, and how individual differences might mediate the rapport between human and robot.