Comparison of Lexical Alignment with a Teachable Robot in Human-Robot and Human-Human-Robot Interactions
This work addresses limitations in measuring and studying lexical alignment for educational robots, offering incremental insights into human-robot interaction dynamics.
The study compared lexical alignment with a teachable robot in human-robot versus human-human-robot interactions, finding that students aligned more in one-on-one settings and that the alignment-rapport relationship is more complex than previously thought.
Speakers build rapport in the process of aligning conversational behaviors with each other. Rapport engendered with a teachable agent while instructing domain material has been shown to promote learning. Past work on lexical alignment in the field of education suffers from limitations in both the measures used to quantify alignment and the types of interactions in which alignment with agents has been studied. In this paper, we apply alignment measures based on a data-driven notion of shared expressions (possibly composed of multiple words) and compare alignment in one-on-one human-robot (H-R) interactions with the H-R portions of collaborative human-human-robot (H-H-R) interactions. We find that students in the H-R setting align with a teachable robot more than in the H-H-R setting and that the relationship between lexical alignment and rapport is more complex than what is predicted by previous theoretical and empirical work.