Robot-Mediated Interventions (RMIs) promise to help autism specialists support children with Autism Spectrum Conditions (ASC). This practice focuses on developing robot-enhanced support systems in which social robots act as assistants and mediators in autism therapy. Understanding the perspectives and experiences of autism specialists is key to evaluating the added value of robots in educational and therapeutic settings. For this purpose, our team collaborated with an autism center, where over 100 children with ASC participated in RMI sessions one to two times per week for one year. As part of the study, we conducted in-depth interviews with two practitioners, exploring their attitudes toward embedding robots into their daily work and practices. We analyzed data on four key themes-acceptance of RMI, evaluations of RMI effects, procedural outcomes and potential improvements. The results highlight the positive acceptance of robots in autism therapy, although practitioners raised concerns about functional and technical limitations. The findings suggest practical considerations for researchers, practitioners, and robot developers in the design and implementation of RMIs.

Children bring unique perspectives and valuable input into design processes, unburdened by the complexities of societal influences. Their direct involvement in the design process is essential for creating meaningful, engaging, and inclusive activities. Participatory design (PD) research is becoming increasingly important in involving end-users in co-designing robotic systems and software. This paper presents the preliminary findings of a PD workshop aimed at establishing an interactive and collaborative environment for children’s co-design of a learning activity for robot-assisted learning on the Furhat robot. To this end, we conducted four workshop sessions with children aged 2-8 years old to co-create a robot-assisted learning scenario surrounding the topic of animals. As a result, together with the children, we designed and tested the prototype of a learning scenario, which helped us identify the shortcomings and set the foundation for future robot-assisted learning scenarios and activities. By reflecting on the challenges and lessons learned through PD with young children, we contribute to enhancing the understanding of PD.

Children's early literacy skills tend to unfold given the specially designed environment and learning activities. In light of Kazakhstan's ongoing alphabet shift from Cyrillic to Latin, many challenges arise for early literacy development and acquisition of the new script. Human-robot interaction (HRI) research provides a unique venue for the integration of social robots into language learning, while the Montessori method with a century-long hisory offers autonomous, selfdirected, and collaborative learning space. Combining these areas, we propose a robotic system named Moveable Älıpbi, designed to help boost children's motivation in learning the new script in long-term interaction. We detail the system's interaction design life cycle from understanding users to designing and implementing robot behaviors followed by evaluating with pilot participants.

Prior research on sign language recognition has already demonstrated encouraging outcomes in achieving highly accurate and dependable automatic sign language recognition. The use of virtual characters as virtual assistants has significantly increased in the past decade. However, the progress in sign language generation and output that closely resembles physiologically believable human motions is still in its early stages. This assertion explains the lack of progress in virtual intelligent signing generative systems. Aside from the development of signing systems, scholarly research have revealed a significant deficiency in evaluating sign language generation systems by those who are deaf and use sign language. This paper presents the findings of a user study conducted with deaf signers. The study is aimed at comparing a state-of-the-art sign language generation system with a skilled sign language interpreter. The study focused on testing established metrics to gain insights into usability of such metrics for deaf signers and how deaf signers perceive signing agents.

Social Narratives (SNs) have shown promising benefits for helping children navigate diverse social situations. The use of SNs in Robot-Assisted Play (RAP) can offer creative ways to address social and communication challenges of children with functional needs. In this study, we evaluate the initial use of social stories in robot-assisted therapy to help 17 children with diverse conditions such as Down Syndrome (DS), Autism Spectrum Conditions (ASC), and speech disorders (SD) understand and act on real-world situations while interacting with the social robot Furhat. We developed educational stories based on six common professions to show how each professional works and interacts with people. We conducted a user study as part of their daily intervention in a rehabilitation center over two weeks. Overall results demonstrate that there were no significant differences in socio-emotional outcomes among children grouped by their diagnosis, age, and other characteristics. However, their communication skills played a significant role in their performance; in particular, children with verbal abilities had higher completion time in activities compared to their non-verbal counterparts. This result was supported by two therapists who reported higher engagement with the robot among verbal children. We suggest that the SN-inspired intervention could be used to support children with diverse special needs, without causing any negative impacts on their learning.

The development of data-driven behaviour generating systems has recently become the focus of considerable attention in the fields of human–agent interaction and human–robot interaction. Although rule-based approaches were dominant for years, these proved inflexible and expensive to develop. The difficulty of developing production rules, as well as the need for manual configuration to generate artificial behaviours, places a limit on how complex and diverse rule-based behaviours can be. In contrast, actual human–human interaction data collected using tracking and recording devices makes humanlike multimodal co-speech behaviour generation possible using machine learning and specifically, in recent years, deep learning. This survey provides an overview of the state of the art of deep learning-based co-speech behaviour generation models and offers an outlook for future research in this area.

The present study applies a novel Reinforcement Learning-based (RL) alphabet learning system named QWriter for the acquisition of the Kazakh Latin alphabet. We conducted a between-subject design experiment with 108 Kazakh children aged 6-8 years old in a public school and compared their learning rates across the two conditions: an RL-based QWriter robot and a human tutor (HT) as a baseline. The results show that children learned significantly more letters with the HT compared to the QWriter robot, showing that the RL-based robot is not effective for learning in the short term. Yet, we observe some interesting results by children’s age and gender. The results need further investigation comparing the QWriter with other robot baselines with different roles and across various learning tasks.

Sign Languages (SL) are a form of communication in the visual-gestural modality, and are full-fledged natural languages. Recent years have witnessed the increase in the use of virtual avatars as virtual assistants. Research into sign language recognition has demonstrated promising potential for robust automatic sign language recognition. However, the area of sign language synthesis is still in its infancy. This explains the underdevelopment of virtual intelligent signing systems. Additionally, existing models are often restricted to manually written rules and require expert knowledge, while data-driven approach could provide a better solution. Apart from the development of signing systems, research indicates a gap in the evaluation thereof by sign language users. In this paper, we propose a data-driven sign language interpreting avatar and its subjective evaluation. We present findings from a pilot study with the deaf evaluating two different avatars against a human sign language interpreter using the metrics that are believed to bring out important insights and narratives for the users in terms of their perceptions of the avatars.

The application of Reinforcement Learning (RL) as an emergent field of Machine Learning has shown positive results in interdisciplinary fields. Although research has proven its effectiveness in language education through various agents (e.g., chatbots, robots, talking avatars), its application in letter acquisition is relatively new. In light of the alphabet transition from Cyrillic to Latin for the Kazakh language, potential challenges might be associated with learning and memorizing the new alphabet. Specifically, students with extant alphabet knowledge might struggle in using the later-learnt new alphabet given no sufficient practice. In this paper, we present a chatbot based on Reinforcement Learning that is anticipated to assist university students in learning the Kazakh Latin alphabet during an interaction in a letter acquisition scenario. Thus, we attempt to identify whether the RL chatbot is efficient for this learning scenario through an online survey study involving pre-test, chatbot interaction, and post-test.

3D avatars are widely used in various fields of emerging technology, from augmented reality to social robots. To interact in a natural way with the user, they must be able to show at least some basic emotions. However, generating animation for these virtual avatars is a time-consuming task and a creative process. The main goal of this work is to facilitate a generation of facial animation of basic emotions on a 3D avatar. To this end, we developed and compared two approaches. The first method consists of the generation of animation using tuning Blendshape features of the 3D model, whereas the second method captures it from the real face and maps it on the model correspondingly. Additionally, the text, from which the emotion was estimated, was passed to lip synchronization software for generating realistic lip movements for the avatar. Then, animations of six basic emotions were shown in different variations in the survey and respondents were asked to guess the emotion shown in the video. Besides, such anthropomorphic features of the avatar as human-likeness, life-likeness and pleasantness were examined. In general, the analysis of the survey provided the following interesting findings: a) participants did not have significant differences in recognizing emotions based on the type of animation generation method; b) inclusion of voice significantly enhanced the recognition of emotion. In relation to participants’ accuracy of emotion recognition, Excitement and Happiness were mostly confused between each other more than any other two emotions, while Anger was the easiest emotion to recognize.