AI and mixed reality in child and adolescent psychiatry: where we were, where we are, and where we are headed
Year: 2026
Type: article
Abstract: Children and adolescents are the first generation to grow up in the era of integration of artificial intelligence and augmented reality (AR) into daily life, yet they are largely absent from the evidence base for the immersive technologies most likely to help them with their mental health challenges. This Perspective explores history of the field from early laboratory AR systems for simple phobias, through randomized trials of augmented reality exposure therapy on commercial headsets, to current platforms in with generative AI agents that animate spatially mapped characters for patients’ experience of in vivo interactions. Augmented reality is potentially better matched to youth than virtual reality: the child remains in their own environment, on their own feet, in continuous visual contact with clinician and the caregiver, while feared social and physical stimuli are introduced under graded control. Because these platforms are transdiagnostic and function based, targeting impaired or avoided human interaction rather than a single diagnosis, they extend across anxiety disorders, post-traumatic stress disorder, autism spectrum disorder, early psychosis, and school refusal, and offer an opportunity for standardized, behavioral assessment. There also risks specific to minors: AI-facilitated avoidance, engagement-optimized models that might validate distorted threat appraisal, cultural and developmental bias in AI-generated social behavior, and assent and data governance requirements. The pediatric evidence base is empty because the studies have not been attempted, and filling it requires immersive-technology infrastructure for both research and clinical care in child and adolescent psychiatry, and a generation of clinicians and investigators trained to use it.
Author: Arash Javanbakht
Institution: Wayne State University
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,353
OpenAlex ID: W7225643797