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Abstract. This paper presents the EU EASEL project, which explores the potential impact and relevance of a robot in educational settings. We present the project objectives and the theorectical background on which the project builds, briefly introduce the EASEL technological developments, and end with a summary of what we have learned from the evaluation studies carried out in the project so far.

Abstract. Robots are gradually but steadily being introduced in our daily lives. A paramount application is that of education, where robots can assume the role of a tutor, a peer or simply a tool to help learners in a specific knowledge domain. Such endeavor posits specific challenges: affective social behavior, proper modelling of the learner's progress, discrimination of the learner's utterances, expressions and mental states, which, in turn, require an integrated architecture combining perception, cognition and action. In this paper we present an attempt to improve the current state of robots in the educational domain by introducing the EASEL EU project. Specifically, we introduce the EASEL's unified robot architecture, an innovative Synthetic Tutor Assistant (STA) whose goal is to interactively guide learners in a science-based learning paradigm, allowing us to achieve such rich multimodal interactions.

Designers of educational interventions are always looking for methods to improve the learning experience of children. More and more, designers look towards robots and other social agents as viable educational tools. To gain inspiration for the design of meaningful behaviours for such educational social robots we conducted a contextual analysis. We observed a total of 22 primary school children working in pairs on a collaborative inquiry learning assignment in a real world situation at school. During content analysis we identified a rich repertoire of social interactions and behaviours, which we aligned along three types of interaction: (1) Educational, (2) Collaborational, and (3) Relational. From the results of our contextual analysis we derived four generic high-level recommendations and fourteen concrete design guidelines for when and how a social robot may have a meaningful contribution to the learning process. Finally, we present four variants of our Computer Aided Learning system in which we translated our design guidelines into concrete robot behaviours.

This article presents a study in which we explored the effect of a social robot on the explanatory behavior of children (aged 6-10) while working on an inquiry learning task. In a comparative experiment, we offered children either a baseline Computer Aided Learning (CAL) system or the same CAL system that was supplemented with a social robot to verbally explain their thoughts to. Results indicate that when children made observations in an inquiry learning context, the robot was better able to trigger elaborate explanatory behavior. First, this is shown by a longer duration of explanatory utterances by children who worked with the robot compared to the baseline CAL system. Second, a content analysis of the explanations indicated that children who worked with the robot included more relevant utterances about the task in their explanation. Third, the content analysis shows that children made more logical associations between relevant facets in their explanations when they explained to a robot compared to a baseline CAL system. These results show that social robots that are used as extensions to CAL systems may be beneficial for triggering explanatory behavior in children, which is associated with deeper learning. 5:2 F. M. Wijnen et al.computer-supported learning and technology-supported education. Technology can often provide assignments and give feedback (right or wrong answer), and, in some situations, the technology is able to adapt the assignment's difficulty level to the learner. In recent years we have seen an emerging role for socially capable interactive learning technologies. Technologies such as virtual agents and robots are capable of interacting and engaging with the learner on a social level. Engaging the learner in social interaction opens up possibilities for providing richer task-related support and scaffolding.In this article, we are specifically interested in a robot's influence on the child's explanatory behavior in the context of inquiry learning.