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'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.

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.

This research designed and applied 24 multi-purposeful robot activities of varying social mediation levels in a multiple-session experiment with 34 children of diverse autistic characteristics in a rehabilitation setting. This paper explores what type of robot activities can meet individual needs to bring more socio-behavioral progress and juxtaposes child characteristics to identify behavioral outcomes in each activity. This knowledge would help us to respond to the question of what activity types suit specific subgroups of Autism Spectrum Disorder (ASD). Our data analysis included coding 48.5 hours of video data for a total of 14 measures to fully capture children's activity-based socio-emotional outcomes. Overall, the activities on varying social mediation levels brought more or less positive social outcomes to all children. However, children showed some different behavioral outcomes as mediated by core autism-related and age-specific characteristics. This study provides in-depth accounts of what might be helpful in designing and applying multi-purposeful activities responsive to the diverse needs of children.

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.

Research has recognized the importance of individ-ual differences of children with Autism Spectrum Disorder (ASD) that require interventions to meet their heterogeneous needs. This relatively large-scale study investigates a robot-assisted autism therapy (RAAT) with 34 children with diverse forms of ASD and Attention Deficit Hyperactivity Disorder (ADHD). We conducted a multi-session study with multi-purposeful activities targeting the socio-emotional abilities of children in a rehabilitation setting. We found a number of quantitative results suggesting various autism-related and demographic differences such as diverse forms of ASD, co-occurrence of ADHD, verbal skills, and age groups. The main findings are: 1) severity of ASD forms may not predict intervention outcomes but instead the co-occurrence of ADHD with LFA diagnosis may negatively impact social smiling; 2) verbal children were more generally engaged and less aggressive with the robot than non-verbal children whose curiosity rose over sessions; and 3) younger children (3.4 y.o.) showed more affection, while older children (7–12 y.o.) were better engaged through speaking more words and having longer engagement and eye contact with the robot.