In human–robot collaboration, people's perceptions of robot abilities can shape how they interact with it. Drawing on insights from a pilot study, we hypothesized that a robot's backstory and gaze patterns might strengthen perceptions of competence. To examine this, we conducted a 2×2 (backstory x gaze patterns) mixed-method study in a museum-based lab setting, where 66 participants completed two open-ended gift-box assembly tasks (makeup vs. pet-owner items) with a Misty robot. Contrary to our expectations and much of the HRI literature, we found no significant differences across experimental conditions. However, participants' interactions revealed important insights about collaboration dynamics. Two distinct collaboration levels emerged: while a quarter of participants maintained low levels of collaboration, the majority engaged in highly collaborative exchanges with the robot. These collaboration levels were associated with different perceptions of warmth and competence and different interaction patterns. A thematic analysis showed that in low-collaboration cases, participants' self-efficacy (i.e. the confidence they held in their own task competence) did not appear to influence the interaction. However, within high-collaboration cases, self-efficacy shaped how participants engaged with the robot. Those with high self-efficacy critically evaluated the robot's contributions against their own expertise, whereas those with low self-efficacy leaned more heavily on the robot's guidance. Across both groups, several factors seemed to enhance collaboration: the robot's answer justifications, occasional pushback from the robot, and participants' growing familiarity with the interaction. We discuss how future systems should account for users' confidence levels, balance responsiveness with constructive disagreement, and support adaptation to foster more balanced and effective collaboration.

Natural Language Interfaces that facilitate data exploration tasks are rapidly gaining in interest in the research community because they enable users to focus their attention on the task of inquiry rather than the mechanics of chart construction. Yet, current systems rely solely on processing the user’s explicit commands to generate the user’s intended chart. These commands can be ambiguous due to natural language tendencies such as speech disfluency and underspecification. In this paper, we developed and studied how an always listening interface can help contextualize imprecise queries. Our study revealed that an always listening interface is able to use an on-going conversation to fill in missing properties for imprecise commands, disambiguate inaccurate commands without asking the user for clarification, as well as generate charts without being explicitly asked.

The application of social robots in education is an emerging field that has the potential to transform the way we teach and learn. In this work, we compare the effects of a physically co-located social robot and a virtual social robot on learning experiences, as students reason about geometry conjectures. Our thematic analysis of interactions and interviews show that the co-located robot was treated more socially and improved learning experiences compared to a virtual robot. Specifically, it increased the perceived effectiveness of the interaction to lower anxiety, provide companionship, and support reasoning and comprehension. These results support the use of co-located, physically present robots to complement learning activities that benefit from social interaction, including reasoning about complex problems and use of gestures. This research contributes to the growing body of literature on the use of social robots in education and highlights the potential for further research on this subject.

Abari Bhattacharya, Abhinav Kumar, Barbara Di Eugenio, Roderick Tabalba, Jillian Aurisano, Veronica Grosso, Andrew Johnson, Jason Leigh, Moira Zellner. Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue. 2023.