Pediatric social robotics in ASD: navigating AI blind-spots toward clinical validation

Abstract As artificial intelligence becomes increasingly integrated into children’s therapeutic environments, social robots are emerging as one of the most clinically complex AI applications for pediatric autism spectrum disorder (ASD). This mini-review examines three under-validated “blind-spot” domains: robot–wearable sensor integration, immersive VR–robot hybrid systems, and generative AI conversational interfaces. Although these technologies offer promising opportunities for adaptive and personalized intervention, current evidence remains limited by small pilot studies, insufficient longitudinal validation, and unresolved developmental safety concerns. Key risks include pediatric sensor intolerance, cognitive overload during immersive exposure, privacy concerns related to continuous data collection, and unconstrained AI-generated dialogue that may reinforce maladaptive communicative patterns in vulnerable children. We propose a five-module clinical validation roadmap focused on pediatric calibration standards, standardized developmental outcome measures, multisite randomized controlled trials, longitudinal monitoring, and human-supported safety architectures. Conclusion : Adaptive social robotics offers a promising but currently unvalidated frontier in pediatric ASD care. These systems cannot yet be supported as primary therapeutic interventions, as pediatric-specific calibration standards, standardized developmental outcome measures, and adequately powered longitudinal trials remain absent. The proposed validation framework outlines a pathway for ensuring AI-supported interventions augment rather than replace authentic human-centered developmental care. What is Known: • Social robots can produce measurable short-term gains in social and communication skills in children with autism spectrum disorder. • Supporting evidence derives largely from small, short-duration pilot studies conducted in controlled settings, limiting clinical applicability. What is New: • Three under-validated blind-spot domains are identified: robot–wearable sensor integration, VR–robot hybrid systems, and generative AI conversational interfaces. • A five-module Clinical Validation Roadmap is proposed to move these domains from pilot demonstration toward pediatric standard of care.