A Robot That Listens: Enhancing Self-Disclosure and Engagement Through Sentiment-based Backchannels and Active Listening

Abstract

As social robots get more deeply integrated into our everyday lives, they will be expected to engage in meaningful conversations and exhibit socio-emotionally intelligent listening behaviors when interacting with people. Active listening and backchanneling could be one way to enhance robots’ communicative capabilities and enhance their effectiveness in eliciting deeper self-disclosure, providing a sense of empathy, and forming positive rapport and relationships with people. Thus, we developed an LLM-powered social robot that can exhibit contextually appropriate sentiment-based backchanneling and active listening behaviors (active listening+backchanneling) and compared its efficacy in eliciting people’s self-disclosure in comparison to robots that do not exhibit any of these listening behaviors (control) and a robot that only exhibits backchanneling behavior (backchanneling-only). Through our experimental study with sixty-five participants, we found the participants who conversed with the active listening robot perceived the interactions more positively, in which they exhibited the highest self-disclosures, and reported the strongest sense of being listened to. The results of our study suggest that the implementation of active listening behaviors in social robots has the potential to improve human-robot communication and could further contribute to the building of deeper human-robot relationships and rapport.