Assessment-Before-Intervention: A WHO iSupport-Grounded Conversational AI System for Dementia Family Caregiver Support

Abstract

Family caregivers of people with dementia face daily behavioral challenges—aggression, wandering, agitation—that require timely, contextual guidance. Existing chatbot systems typically respond with direct advice, bypassing assessment of behavioral context and caregiver emotional state. This paper presents a LINE-deployed conversational AI system grounded in the WHO iSupport for Dementia framework, built around an Assessment-Before-Intervention dialogue mechanism. The system applies a four-step reasoning process derived from the iSupport ABC behavioral cycle, governed by five safety principles, to determine when to clarify before advising. A dual-layer response model ensures emotional acknowledgment is never omitted; a hybrid keyword-semantic Retrieval-Augmented Generation (RAG) architecture bridges the lay-to-clinical vocabulary gap. We evaluated the system through a formative review with four domain reviewers in dementia care, covering 13 BPSD scenarios (52 evaluations across five quality dimensions). Mean scores (4.78–4.81 on a 5-point Likert scale) are interpreted as preliminary perceived-appropriateness data rather than clinical effectiveness evidence. The principal contribution lies in the qualitative findings: five recurrent failure modes and a structural pattern of cultural misalignment between the international iSupport framework and Taiwanese caregiving realities. Findings offer practical implications for the design of AI-assisted care systems in dementia and other emotionally sensitive healthcare contexts.