Evaluating a retrieval-augmented pregnancy chatbot: a comprehensibility–accuracy-readability study of the DIAN AI assistant

Grounded in an authoritative national guide, DIAN achieved broadly comparable CAR perceptions across groups, with clinically relevant divergence limited to postpartum care. Women perceived higher comprehensibility and accuracy, while clinicians judged language more readable, suggesting a gap between experiential clarity and professional textual ease. Targeted postpartum refinement, lexical simplification, role-tailored summaries, and actionable checklists may align perceptions without compromising fidelity. More broadly, RAG-grounded chatbots can support equitable digital health education when content is vetted, updated, and evaluated with stakeholder-centered metrics. Future work should examine free-form interactions, longitudinal behavioral outcomes, and ethical safeguards (scope-of-use messaging, escalation pathways, and bias audits).