Enhancing Linguistic and Communicative Skills in Students with Autism Syndrome Through Social Robots and Digital Interaction: A Multilayered Data Logging Framework Integrating Multimodal Behavioral and Physiological Signals

Identifying support needs in the context of diversity is a growing and highly relevant area of research within public and social inclusion policies for individuals with autism spectrum disorder (ASD). This study examines how different interaction agents and the design of activity types influence the development of joint activities with children with ASD and compares these outcomes with those of a neurotypical control group. Data collection followed a multilevel HCI-informed analysis approach that integrated attentional response flow, behavioral observations, cognitive responses, and biometric measures to evaluate user engagement and intervention effectiveness across a sequential 20-session program, in exploration single-case research. The findings support the added value of social robots as interactive mediating agents that can scaffold the transition toward natural human communicative and linguistic development. These results open promising pathways for psychoeducational and clinical support through inclusive, interaction-centered technological designs aimed at fostering the development of individuals with autism.