A LINE-deployed conversational AI system grounded in the WHO iSupport for Dementia framework, built around an Assessment-Before-Intervention dialogue mechanism, which offers practical implications for the design of AI-assisted care systems in dementia and other emotionally sensitive healthcare contexts.
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.
Results show CBT knowledge alone does not ensure effective application, giving the affective-computing community instrumentation to measure where LLMs fall short.
Vaishnavi Sinha, Pooja Guttal, Pranay Deep Reddy Katike et al.· 0 citations
A multimodal emotion-aware architecture, which pays attention to memory-enhanced personalization and emotion-specific reinforcement learning, is introduced and hybrid human-AI approaches, which focus on safety and empathetic conversation to improve current mental health systems are recommended.
HEART, a healthcare-specific evaluative architecture comprising Human-Centred Communication, Ethical and Trustworthy Deployment, Ethical and Trustworthy Deployment, Adaptive and Embodied Intelligence, Relationship Continuity, and Translational Healthcare Value is proposed.
Conversational artificial intelligence (AI) has shown potential to support knowledge translation, personalized education, and access to health-related information. However, applications in neurodevelopmental care remain largely focused on screening and assessment, while conversational systems tailored to occupational therapy are scarce. Moreover, general-purpose generative AI may produce inaccurate, insufficiently contextualized, or clinically inappropriate responses, highlighting the need for evidence-based, domain-specific systems with robust safety mechanisms. This study describes the protocol for the co-design, development, and preliminary evaluation of a conversational AI system designed to support occupational therapists and parents of children aged 5–12 years with neurodevelopmental disorders in Greece. The system is intended as an educational and decision-support resource and will not replace diagnosis, clinical judgment, or individualized intervention. A co-designed, multi-phase, mixed-methods proof-of-concept design will be adopted, informed by the Medical Research Council framework, Design Science Research, user-centered design principles, and the CeHRes Roadmap 2.0. Development will include evidence synthesis, stakeholder needs assessment, knowledge base construction, iterative prototype development, and expert, technical, safety, and user evaluation. The system will integrate a curated occupational therapy knowledge base, retrieval-augmented generation, role-specific prompting, source verification, and layered safety guardrails. Expected outputs include a stakeholder-informed Greek-language minimum viable product and a transparent framework linking evidence, user requirements, technical design, and evaluation criteria. Preliminary evaluation will assess factual accuracy, evidence concordance, occupational therapy relevance, clinical appropriateness, safety, usability, acceptability, and perceived usefulness. This protocol provides a reproducible foundation for developing clinically relevant conversational AI in occupational therapy and for future feasibility and effectiveness studies.
Pantelis Pergantis, N. Bardis, Charalabos Skianis et al.· Brazilian Journal of Science· 0 citations
Developmental dyslexia involves persistent difficulties in word-level reading and decoding, requiring sustained linguistic practice that is difficult to maintain without supervision. Although Generative AI offers personalized support, standard Large Language Models (LLMs) often lack the pedagogical and therapeutic knowledge required for linguistic intervention. We present Foxy, a proactive LLM-driven Conversational Agent designed to assist Italian children aged 8–11 with dyslexia during morphological training. Through a modular prompt-orchestration framework and an event-driven architecture, Foxy acts as a specialized tutor that provides scaffolding, limits topic drift, and reduces hallucinations via a verified lexical knowledge base. The system was refined through expert-led co-design and evaluated in a pilot study with 18 educators, and therapists. Results indicate positive perceptions of usability, usefulness, and appropriateness, with recognition of Foxy’s motivational benefits. These findings suggest Foxy is a promising tool for dyslexia intervention, pending wider empirical validation.
Giulia Valcamonica, Giovanni Caleffi, Francesco Piferi et al.· International Conference on...· 0 citations
Analysis of interaction patterns from robot-delivered individual CST (iCST) sessions conducted with people living with dementia in home settings provides empirical insights into the factors that shape conversational engagement in robot-delivered iCST, which inform the design of adaptive conversational robots for dementia therapy.