This paper presents a study that compares a humanoid robotic tutor to a human tutor when instructing school children to build a LEGO house. A total of 27 students, between the ages of 11-15, divided into two groups, participated in the study and data were collected to investigate the participants' success rate, requests for help, engagement, and attitude change toward robots following the experiment. The results reveal that both groups are equally successful in executing the task. However, students ask the human tutor more often for help, while students working with the robotic tutor are more eager to perform well on the task. Finally, all students get a more positive attitude toward a robotic tutor following the experiment, but those in the robot condition change their attitude somewhat more for certain questions, illustrating the importance of real interaction experiences prior to eliciting students' attitudes toward robots. The paper concludes that students do follow instructions from a robotic tutor but that more long-term interaction is necessary to study lasting effects.

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As increasingly more research efforts are geared towards creating robots that can teach and interact with children in educational contexts, it has been speculated that endowing robots with artificial empathy may facilitate learning. In this paper, we provide a background to the concept of empathy, and how it factors into learning. We then present our approach to equipping a robotic tutor with several empathic qualities, describing the technical architecture and its components, a map-reading learning scenario developed for an interactive multitouch table, as well as the pedagogical and empathic strategies devised for the robot. We also describe the results of a pilot study comparing the robotic tutor with these empathic qualities against a version of the tutor without them. The pilot study was performed with 26 school children aged 10–11 at their school. Results revealed that children in the test condition indeed rated the robot as more empathic than children in the control condition. Moreover, we explored several related measures, such as relational status and learning effect, yet no other significant differences were found. We further discuss these results and provide insights into future directions.

Engagement in task orientated social robotics is a complex phenomenon, consisting of both task and social elements. Previous work in this area tends to focus on these aspects in isolation without consideration for the positive or negative effects one might cause the other. We explore both, in an attempt to understand how engagement with the task might effect the social relationship with the robot, and vice versa. In this paper, we describe the analysis of participant self-report data collected during an exploratory pilot study used to evaluate users' "perception of engagement". We discuss how the results of our analysis suggest that ultimately, it was the users' own perception of the robots' characteristics such as friendliness, helpfulness and attentiveness which led to sustained engagement with both the task and robot.

In our work we explore the development of a computational model capable of automatically detecting engagement in social human-robot interactions from real-time sensory and contextual input. However, to train the model we need to establish ground truths of engagement from a large corpus of data collected from a study involving task and social-task engagement. Here, we intend to advance the current state-ofthe-art by reducing the need for unreliable post-experiment questionnaires and costly time-consuming annotation with the novel introduction of implicit probes. A non-intrusive, pervasive and embedded method of collecting informative data at different stages of an interaction.

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