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.
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This content will become publicly available on August 28, 2025
Characterization of Human Trust in Robot through Multimodal Physical and Physiological Biometrics in Human-Robot Partnerships
- Award ID(s):
- 2104742
- PAR ID:
- 10548170
- Publisher / Repository:
- IEEE
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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