Evidence-Grounded, Role-Adaptive Conversational AI for Occupational Therapists and Caregivers in Neurodevelopmental Care: A Comprehensive Narrative Review and Sociotechnical Reference Architecture

Generative conversational AI can make health information easier to access, but its responses may still be unsupported, outdated, or poorly matched to the user. These risks are important in neurodevelopmental care, where occupational therapists and parents or caregivers require different levels of detail, language, and guidance. This narrative review used a structured multidisciplinary search to examine knowledge-base governance, retrieval-augmented generation, source verification, role adaptation, uncertainty communication, safety routing, privacy, human oversight, and lifecycle control. The evidence was synthesized into a sociotechnical reference architecture for adult-facing conversational AI serving occupational therapists and parents or caregivers. The architecture comprises five connected layers: intended use and request classification; knowledge governance and retrieval; claim-level verification; role-specific response design and safety review; and response delivery and lifecycle control. Occupational relevance, documentation, accountability, and continuous evaluation operate across the layers. Each layer is linked to operational requirements, responsible actors, and evaluation indicators. The framework is intended to guide future development and evaluation rather than function as a validated clinical tool. It excludes diagnosis, autonomous treatment planning, direct child-facing interaction, and replacement of occupational therapy assessment. The review provides a practical basis for developing conversational AI that is evidence-grounded, traceable, role-appropriate, and compatible with professional oversight.