Children are interacting with robots in sophisticated ways to the extent in which they may be establishing a relationship. Forming appropriate levels of connection between children and robots could have significant impacts on the future of robot design, particularly in education. Despite this, little is known about the underlying mechanisms of such formation. In this work, we take an initial step by exploring what robot behaviors build a child-robot connection in a single interaction. Specifically, we investigated whether 6-10-year-old children feel more connected to a robot that responds to an issue by asking the child for help or simply disclosing the issue, and whether this is dependent on the valence of the response to the issue (emotional or mechanical). In a 2 x 2 between-subjects study (N=100), we found that children of all ages trusted a robot that asked for help more than a robot that simply disclosed the issue. Furthermore, children felt closer to an emotional robot that asked for help than an emotional robot that did not ask for help. Together, these findings suggest that asking for help builds trust between a robot and child and expressing relatable vulnerability, via emotional help requests, creates further feelings of connection.

As human-robot interactions become more social, a robot's personality plays an increasingly vital role in shaping user experience and its overall effectiveness. In this study, we examine the impact of three distinct robot personalities on user experiences during well-being exercises: a Baseline Personality that aligns with user expectations, a High Extraversion Personality, and a High Neuroticism Personality. These personalities were manifested through the robot's dialogue, which were generated using a large language model (LLM) guided by key behavioral characteristics from the Big 5 personality traits. In a between-subjects user study (N = 66), where each participant interacted with one distinct robot personality, we found that both the High Extraversion and High Neuroticism Robot Personalities significantly enhanced participants' emotional states (arousal, control, and valence). The High Extraversion Robot Personality was also rated as the most enjoyable to interact with. Additionally, evidence suggested that participants' personality traits moderated the effectiveness of specific robot personalities in eliciting positive outcomes from well-being exercises. Our findings highlight the potential benefits of designing robot personalities that deviate from users' expectations, thereby enriching human-robot interactions.