As social robots enter educational settings, how they are positioned as new social actors within existing teacher-student pedagogical relationships becomes an important yet underexplored question. They may be shaped by existing relationships, while also having the potential to reconfigure them. In this study, we explore the relational positioning of educational social robots through a drawing-based design activity with teachers. By analyzing the pedagogical configurations imagined by teachers, we reveal how teachers assign roles, responsibilities, and degrees of authority to robots, in relation to themselves and students. Building on these findings, we discuss the design implications for future educational social robots, highlighting the importance of considering how robots are added into and reshape pedagogical relationships.

As robots are increasingly integrated into everyday life, their ability to communicate emotions becomes crucial to foster positive human-robot interactions. However, the key factors for the recognition of artificial emotions are still poorly understood. This study explores whether children can better recognize emotions expressed by robots using gestures or by robots using facial expressions. We observed ten five-year-olds and fourteen six-year-olds during an in-the-wild workshop. The results revealed significant differences in emotion recognition based on robot type, age, and emotion. Children who interact with the gesture robot were 90% less likely to accurately recognize emotions than the robot with facial expressions. Six-year-olds demonstrated significantly higher accuracy, with 8.30 times greater chances of recognizing emotions than five-year-olds. Regarding emotion type, children were significantly more likely to identify sadness and happiness correctly than anger. These findings highlight the importance of incorporating facial expressions to aid in emotion recognition and make robots more approachable for young children.

As child-robot interactions become more and more common in daily life environment, it is important to examine how robot’s errors influence children’s behavior. We explored how a robot’s unexpected behaviors affect child-robot interactions during two workshops on active reading: one in a modern art museum and one in a school. We observed the behavior and attitudes of 42 children from three age groups: 6-7 years, 8-10 years, and 10-12 years. Through our observations, we identified six different types of surprising robot behaviors: personality, movement malfunctions, inconsistent behavior, mispronunciation, delays, and freezing. Using a qualitative analysis, we examined how children responded to each type of behavior, and we observed similarities and differences between the age groups. Based on our findings, we propose guidelines for designing age-appropriate learning interactions with social robots.

In child-robot interaction, the element of trust towards the robot is critical. This is particularly important the first time the child meets the robot, as the trust gained during this interaction can play a decisive role in future interactions. We present an in-the-wild study where Polish kindergartners interacted with a Pepper robot. The videos of this study were analyzed for the issues of trust, anthropomorphization, and reaction to malfunction, with the assumption that the last two factors influence the children's trust towards Pepper. Our results reveal children's interest in the robot performing tasks specific for humans, highlight the importance of the conversation scenario and the need for an extended library of answers provided by the robot about its abilities or origin and show how children tend to provoke the robot.