In intelligent tutoring systems, one fundamental problem that limits learning gains is the unproductive use of on-demand help features, namely overuse or aversion, resulting in students misusing the system rather than engaging in active learning. Social robots as tutoring agents have the potential to mitigate those behaviors by actively shaping productive help-seeking behaviors. We hypothesize that effectual help-seeking behavior is a critical contributor to learning gains in a robot-child tutoring interaction. We conduct a between-subjects study where children interacted with a social robot solving fractions problems over multiple sessions (29 children; 4 sessions per child) in one of two groups. Results showed that participants in our experimental group, who received adaptive shaping strategies from the robot targeting suboptimal help requests, reduced their suboptimal behaviors over time significantly more than a control group, as well as improved their scores from pretest to posttest significantly more than a control group.

Short et al. found that in a game between a human participant and a humanoid robot, the participant will perceive the robot as being more agentic and as having more intentionality if it cheats than if it plays without cheating. However, in that design, the robot that actively cheated also generated more motion than the other conditions. In this paper, we investigate whether the additional movement of the cheating gesture is responsible for the increased agency and intentionality or whether the act of cheating itself triggers this response. In a between-participant design with 83 participants, we disambiguate between these causes by testing (1) the cases of the robot cheating to win, (2) cheating to lose, (3) cheating to tie from a winning position, and (4) cheating to tie from a losing position. Despite the fact that the robot changes its gesture to cheat in all four conditions, we find that participants are more likely to report the gesture change when the robot cheated to win from a losing position, compared with the other conditions. Participants in that same condition are also far more likely to protest in the form of an utterance following the cheat and report that the robot is less fair and honest. It is therefore the adversarial cheat itself that causes the effect and not the change in gesture, providing evidence for a cheating detector that can be triggered by robots.

This paper describes an extended (6-session) interaction between an ethnically and geographically diverse group of 26 first-grade children and the DragonBot robot in the context of learning about healthy food choices. We find that children demonstrate a high level of enjoyment when interacting with the robot, and a statistically significant increase in engagement with the system over the duration of the interaction. We also find evidence of relationship-building between the child and robot, and encouraging trends towards child learning. These results are promising for the use of socially assistive robotic technologies for long-term one-on-one educational interventions for younger children.