Search

As research on robotic tutors increases, it becomes more relevant to understand whether and how robots will be able to keep students engaged over time. In this paper, we propose an algorithm to monitor engagement in small groups of children and trigger disengagement repair interventions when necessary. We implemented this algorithm in a scenario where two robot actors play out interactive narratives around emotional words and conducted a field study where 72 children interacted with the robots three times in one of the following conditions: control (no disengagement repair), targeted (interventions addressing the child with the highest disengagement level) and general (interventions addressing the whole group). Surprisingly, children in the control condition had higher narrative recall than in the two experimental conditions, but no significant differences were found in the emotional interpretation of the narratives. When comparing the two different types of disengagement repair strategies, participants who received targeted interventions had higher story recall and emotional understanding, and their valence after disengagement repair interventions increased over time.

Physiological sensors can be instrumental in facilitating complex behavioral and social interactions between humans and robots. However, there is a limited understanding of the psychological implications of incorporating such sensors in the context of human-robot interaction. We show that perception of sensors' role can act as an individual difference factor that can implicitly influence trust and reliance on a robot. We argue that some people may believe in the ability of physiological sensors to facilitate mentalization (or understanding) of humans. We refer to this tendency as mentalizing propensity. Such implicit differences in mentalizing propensity may influence participants' trust and reliance on a robot, as found in the current study. We also found that trust can mediate the influence of mentalizing propensity on reliance. Furthermore, we show that social interactions with a robot influenced participants' mentalizing propensity. The current work shows that implicit understanding of sensors' role in human-robot interaction context is an important attribute to assess in future studies on trust and reliance.