Predictability is important to autistic individuals, and robots have been suggested to meet this need as they can be programmed to be predictable, as well as elicit social interaction. The effectiveness of robot-assisted interventions designed for social skill learning presumably depends on the interplay between robot predictability, engagement in learning, and the individual differences between different autistic children. To better understand this interplay, we report on a study where 24 autistic children participated in a robot-assisted intervention. We manipulated the variance in the robot's behaviour as a way to vary predictability, and measured the children's behavioural engagement, visual attention, as well as their individual factors. We found that the children will continue engaging in the activity behaviourally, but may start to pay less visual attention over time to activity-relevant locations when the robot is less predictable. Instead, they increasingly start to look away from the activity. Ultimately, this could negatively influence learning, in particular for tasks with a visual component. Furthermore, severity of autistic features and expressive language ability had a significant impact on behavioural engagement. We consider our results as preliminary evidence that robot predictability is an important factor for keeping children in a state where learning can occur.

The development of applications with intelligent virtual agents (IVA) often comes with integration of multiple complex components. In this article we present the Agents United Platform: an open source platform that researchers and developers can use as a starting point to setup their own multi-IVA applications. The new platform provides developers with a set of integrated components in a sense-remember-think-act architecture. Integrated components are a sensor framework, memory component, Topic Selection Engine, interaction manager (Flipper), two dialogue execution engines, and two behaviour realisers (ASAP and GRETA) of which the agents can seamlessly interact with each other. This article discusses the platform and its individual components. It also highlights some of the novelties that arise from the integration of components and elaborates on directions for future work.

The development of applications with intelligent virtual agents (IVA) often comes with integration of multiple complex components. In this article we present the Agents United Platform: an open source platform that researchers and developers can use as a starting point to setup their own multi-IVA applications.

This paper describes a longitudinal study in which children could interact unsupervised and at their own initiative with a fully autonomous computer aided learning (CAL) system situated in their classroom. The focus of this study was to investigate how the mindset of children is affected when delivering effort-related praise through a social robot. We deployed two versions: a CAL system that delivered praise through headphones only, and an otherwise identical CAL system that was extended with a social robot to deliver the praise. A total of 44 children interacted repeatedly with the CAL system in two consecutive learning tasks over the course of approximately four months. Overall, the results show that the participating children experienced a significant change in mindset. The effort-related praise that was delivered by a social robot seemed to have had a positive effect on children’s mindset, compared to the regular CAL system where we did not see a significant effect.

With the ageing of the population, more (chronic) diseases arise, putting ressure on the healthcare system. This requires a shift from treatment towards prevention of age related diseases by enabling the aging generation to stimulate them to take care of their own health and condition. The Council of Coaches project is developing a team of virtual coaches that can help older adults to achieve their health goals. Each coach offers insights and advice based on their expertise. The Council of Coaches enables multiparty interaction between multiple coaches and the users by leading user-coach, but also coach-coach conversations. Currently the Council of Coaches interacts with the coaches by selecting one of several predefined multiple choice options. Although this a robust method to capture user input, it is not ideal for elderly for several reasons, such as physical function loss. Spoken dialogues can offer a better user experience, but also come with many complexities. In this research topics, the state-of-art in this topic is investigated by answering multiple research (sub) questions. An extensive literature research has been conducted to gain knowledge about: Conversational Interfaces, Speech Recognizers, Virtual Coaching Systems and the Council of Coaches. This research topics serves as an approach for my thesis that attempts to answer the main question: To what extent can spoken interaction offer a valuable addition to the multiparty virtual Council of Coaches application?. All gained background knowledge will help with the adaption of the Council of Coach system to support spoken interactions.

Autistic children with limited language ability are an important but overlooked community. We develop a teacher-teleoperated robot and tablet system, as well as learning activities, to help teach facial emotions to minimally verbal autistic children. We then conduct user studies with 31 UK and Serbia minimally verbal autistic children to evaluate the system's accessibility. Results showed minimally verbal autistic children could use the tablet interface to control or respond to a humanoid robot and could understand the face learning activities. We found that a flexible and powerful wizard-of-oz tablet interface respected the needs of the children and their teachers. Our work suggests that a non-participatory, user-centered design process can create a robot and tablet system that is accessible to many autistic children.

We investigated how voice and motion from a small humanoid robot affect an autistic child's re-engagement of attention. Results suggest that a robot can use motion to re-engage the attention of an autistic child and that two adjoining multimodal cues are more effective than a single unimodal cue.

Interactive learning technologies, such as robots, increasingly find their way into schools. However, more research is needed to see how children might work with such systems in the future. This paper presents the unsupervised, four month deployment of a Robot-Extended Computer Assisted Learning (RECAL) system with 61 children working in their own classroom. Using automatically collected quantitative data we discuss how their usage patterns and self-regulated learning process developed throughout the study.

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.

Robot-assisted therapy is an emerging form of therapy for autistic children, although designing effective robot behaviors is a challenge for effective implementation of such therapy. A series of usability tests assessed trends in the effectiveness of modelling a robot's facial expressions on realistic facial expressions and of adding peripherals enabling child-led control of emotion learning activities with autistic children. Nineteen autistic children interacted with a small humanoid robot and an adult therapist in several emotion-learning activities that featured realistic facial expressions modelled on either a pre-existing database or live facial mirroring, and that used peripherals (tablets or tangible 'squishies') to enable child-led activities. Both types of realistic facial expressions by the robot were less effective than exaggerated expressions, with the mirroring being unintuitive for children. The tablet was usable but required more feedback and lower latency, while the tactile tangibles were engaging aids.

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 forwhenandhowa 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.

We present a new dialogue engine called Flipper 2.0 (Flipper) which aims to help developers of embodied conversational agents (ECAs) to quickly and flexibly create dialogues. Flipper provides a technically stable and robust dialogue management system to integrate with other components of ECAs such as behaviour realisers. We compare Flipper with state-of-the-art dialogue design systems. We describe the details of our dialogue engine, how it handles dialogue management and how it supports the authoring of dialogues. We demonstrate the use of the dialogue engine with examples of design patterns and discuss practical applications. Finally we give recommendations on the cases in which it is beneficial to use Flipper.

This paper presents the design and validation of a measurement instrument for children’s perceptions of robots’ social competence. The need for a standardized validated instrument has emerged as a requisite for meta-analyses and comparisons among various studies in the field of child-robot interaction. We report on the development of the instrument and its validation, which adopted a design-based method with two iterations. We used construct validity, which was formed by divergent and convergent validity. Children’s perceptions of three different robotic platforms were examined in two empirical studies with 78 children aged 7–9 years, which was based on semi-structured interviews with qualitative thematic content analysis. The results indicated that children differentiate their perception of social competence depending on the perceived intentionality of the robot and they ascribe discrete categorizations to the robot such as a machine, social artifact and social agent. The findings are discussed in relation to existing literature.

Not getting enough sleep is detrimental to our health and productivity, yet we have difficulty to maintain consistent bedtimes. Technological solutions to this problem mostly focus on detecting sleep patterns and providing feedback on them. We felt there was an opportunity for a perspective that concentrates on one's subjective experience. We propose Snoozle, an actuated pillow that supports consistent bedtimes by inviting users to bed, and improves the sleeping experience by enhancing the feeling of co-presence. In this proposal, we present how the concept of Snoozle developed from structured brainstorms, storyboards and sketches. We discuss the actuated pillow behavior and the envisioned interaction, and we detail our next steps.

This paper aims to present the main challenges that emerged during the process of the research design of a longitudinal study on child-robot interaction for science education and to discuss relevant suggestions in the context. The theoretical rationale is based on aspects of the theory of social constructivism and we use the collaborative inquiry as a framework to examine children's learning process who interact with a robotic learning companion. We identify two main challenges; (i) the development of robust on-demand systems for long-term interaction; and (ii) the design of developmentally appropriate scaffolding in embodied, semi-structured learning tasks. To address these challenges, we suggest (i) the development of a system for the detection of child's intention for interaction in the context of a classroom and (ii) the design of sensorized learning materials for the support of developmentally appropriate embodied learning experience.

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.

This review examines recent methodological approaches for the evaluation of child-robot interaction in learning settings. The main aims are to map existing work from a user-centered perspective, to identify possible trends related to evaluation methods for child-robot interaction, and to discuss potential future directions. We present a systematic review of existing studies, which have been thematically organized based on their research objectives. We then examine the evaluation methods that were used in these studies and we propose a conceptual framework based on the one hand on the themes that emerged, namely the social interaction between the child and the robot, the social acceptance, possible emotional interactions, the learning process and the learning outcome, and on the other hand on the corresponding measures. These methods have been considered in relation with the age ranges of the children, because of the relationship of their cognitive level to the choice of a developmentally appropriate evaluation method. We use this framework to highlight current trends and needs for the field and to contextualize the methodological directions for child-robot interaction. Finally, we discuss the challenges and limitations of the current methodological approaches as well as possible future directions for the evaluation methods of child-robot interaction in learning settings.

In an exploratory study on educational child-robot interaction we investigate the effect of alternating a learning activity with an additional shared activity. Our aim is to enhance and enrich the relationship between child and robot by introducing ``physical extracurricular activities''. This enriched relationship might ultimately influence the way the child and robot interact with the learning material. We use qualitative measurement techniques to evaluate the effect of the additional activity on the child-robot relationship. We also explore how these metrics can be integrated in a highly exploratory cumulative score for the relationship between child and robot. This cumulative score suggests a difference in the overall child-robot relationship between children who engage in a physical extracurricular activity with the robot, and children who only engage in the learning activity with the robot.

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.