An Application of the Self-Determination Theory Model for AI-Enhanced, Digital Psychosocial Interventions for Pediatric Disabilities

BACKGROUND: Youth with developmental disabilities (DDs) experience elevated psychosocial challenges, including difficulties with emotion regulation, self-concept, and access to timely mental health support. Pediatric nurses are well-positioned to address these needs; however, time constraints, limited training, and restricted access to specialty services often impede the provision of consistent psychosocial care. Digitally delivered psychosocial interventions enhanced by artificial intelligence (AI) offer new opportunities for individualized, accessible support, but these approaches frequently lack a clear theoretical foundation. PURPOSE: This paper applied Self-Determination Theory (SDT), specifically Basic Psychological Needs Theory, to guide pediatric nurses' use of AI-enhanced, digital psychosocial interventions for DDs in practice. METHODS: A theory-driven synthesis mapped SDT needs (autonomy, competence, and relatedness) to AI-enabled intervention features: conversational agents, neuro/adaptive feedback, and emotion-recognition tailoring. RESULTS: The framework specifies how these features can promote volitional engagement, optimal challenge with timely feedback, and supportive interactions for youth with DDs. It also notes evidence gaps (small, heterogeneous samples; limited comparisons and follow-up) and barriers involving payment/workload, equity and access, and data protection, transparency, and bias. CONCLUSIONS/IMPLICATIONS: SDT offers a coherent, testable foundation for designing, evaluating, and integrating AI-enhanced psychosocial supports for pediatric DDs. Nurses can screen for support, coach families, monitor risks, and advocate for ethical deployment and rigorous research.