Researchers, industry, and practitioners are increasingly interested in the potential of social robots in education for learners on the autism spectrum. In this study, we conducted semi-structured interviews and focus groups with educators in England to gain their perspectives on the potential use of humanoid robots with autistic pupils, eliciting ideas, and specific examples of potential use. Understanding educator views is essential, because they are key decision-makers for the adoption of robots and would directly facilitate future use with pupils. Educators were provided with several example images (e.g., NAO, KASPAR, Milo), but did not directly interact with robots or receive information on current technical capabilities. The goal was for educators to respond to the general concept of humanoid robots as an educational tool, rather than to focus on the existing uses or behaviour of a particular robot. Thirty-one autism education staff participated, representing a range of special education settings and age groups as well as multiple professional roles (e.g., teachers, teaching assistants, speech, and language therapists). Thematic analysis of the interview transcripts identified four themes: Engagingness of robots, Predictability and consistency, Roles of robots in autism education, and Need for children to interact with people, not robots. Although almost all interviewees were receptive toward using humanoid robots in the classroom, they were not uncritically approving. Rather, they perceived future robot use as likely posing a series of complex cost-benefit trade-offs over time. For example, they felt that a highly motivating, predictable social robot might increase children's readiness to learn in the classroom, but it could also prevent children from engaging fully with other people or activities. Educator views also assumed that skills learned with a robot would generalise, and that robots' predictability is beneficial for autistic children-claims that need further supporting evidence. These interview results offer many points of guidance to the HRI research community about how humanoid robots could meet the specific needs of autistic learners, as well as identifying issues that will need to be resolved for robots to be both acceptable and successfully deployed in special education contexts.

One of the major reasons behind traffic accidents is misinterpretation among road users. Self-driving vehicles are expected to reduce these accidents, given that they are designed with all road users in mind. Recently, research on the design of vehicle-pedestrian communication has emerged, but to our knowledge, there is no research published that investigates the design of interfaces for intent communication towards child pedestrians. This paper reports the initial steps towards the examination of children's views and understandings about the appearance and intention communication of self-driving vehicles. It adopts a design inclusive methodological approach for the development of a prototype for the communication of two basic intentions: "I am going to stop" and "I am going to proceed". The initial results indicate children's need to be aware about the autonomy of the vehicle and the use of their previous experience with traffic signs for the interpretation of communicative signs of the vehicle.

We present design strategies for Human Robot Interaction for schoolaged autistic children with limited receptive language. Applying these strategies to the DE-ENIGMA project (large EU project addressing emotion recognition in autistic children) supported development of a new activity for in facial expression imitation whereby the robot imitates the child's face to encourage the child to notice facial expressions in a play-based game. A usability case study with 15 typically-developing children aged 4-6 at an English-language school in the Netherlands was performed to observe the feasibility of the setup and make design revisions before exposing the robot to autistic children.

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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.