AI Therapy Chatbots in Youth With Attention-Deficit/Hyperactivity Disorder: A Nurse-Led, Risk-Tiered Policy Framework

AI-powered therapy chatbots and virtual companions are beginning to enter routine mental health care, yet safeguards for children and adolescents remain underdeveloped. For youth with attention-deficit/hyperactivity disorder (ADHD), these tools may offer brief coaching, psychoeducation, and structured between-visit check-ins across clinic, school, and home settings, but they also raise concerns about inappropriate content, overreliance, quiet model drift, privacy across health and education systems, and missed crisis escalation. In this Viewpoint, we propose a nurse-led, risk-tiered policy and implementation framework for integrating AI therapy chatbots into youth ADHD care safely, effectively, and equitably. The framework has 3 linked layers: policy levers, implementation enablers, and measurable outcomes. We emphasize child-specific safeguards, predetermined change control for adaptive models, equity-oriented procurement and reimbursement, interoperability and safety reporting, and nurse-led human-in-the-loop oversight. We further clarify that nursing-generated data-such as structured assessments, triage actions, escalation records, and supervision workload-can function both as governance inputs and as outcome-relevant signals within monitoring and service evaluation. We also outline a phased implementation road map and a concise indicator set that schools and clinics can monitor without major new infrastructure. A nurse-led, risk-tiered approach, combined with age-appropriate safeguards, equity-by-design implementation, and privacy-preserving real-world learning, can help health systems and schools move from ad hoc adoption toward safer and more equitable use of AI therapy chatbots for youth with ADHD while the evidence base continues to mature.