Peer-reviewed article
Pediatric social robotics in ASD: navigating AI blind-spots toward clinical validation.
Grewal S, Roy R, Sidhu K, Bascon K, Elahresh F et al. ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This review examines the use of social robots combined with artificial intelligence technologies to support children with autism spectrum disorder. While robots show promise for improving social and communication skills, the authors identify significant gaps in validation, including limited evidence from small studies, unknown safety risks, and concerns about privacy and AI-generated content. The review proposes a five-step framework to ensure these technologies are properly tested before being used as primary treatments in clinical settings.
Key points
- Social robots can produce measurable short-term gains in social and communication skills, but evidence comes mainly from small pilot studies in controlled settings
- Three under-validated domains pose risks: robot-wearable sensor integration, immersive VR-robot hybrid systems, and generative AI conversational interfaces
- Key safety concerns include sensor intolerance in children, cognitive overload during immersive exposure, privacy risks from continuous data collection, and unconstrained AI dialogue that may reinforce maladaptive communication patterns
- Current systems lack pediatric-specific calibration standards, standardized developmental outcome measures, and adequately powered longitudinal trials
- A proposed five-module validation roadmap includes pediatric calibration standards, standardized measures, multisite randomized controlled trials, longitudinal monitoring, and human-supported safety architectures
Limitations
This is a narrative review rather than a systematic synthesis of evidence, so it does not quantify how much evidence exists or conduct meta-analysis. The review does not evaluate any single intervention directly, so it cannot specify which specific robot systems or AI features have been tested or their exact outcomes.
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.