A Personal Social Robot for Structured Narrative Interaction in Narrative-Based Medicine

Abstract

Narrative-Based Medicine (NBM) integrates traditional care with conversational approaches that address the psychosocial and emotional dimensions of illness. In assisted living and long-term care, social robots represent a promising medium for operationalizing the principles of NBM through continuous monitoring and interaction. However, purely generative Large Language Model (LLM)-based systems lack the structured dialogue strategies required for effective narrative elicitation. This paper presents a framework for structured narrative interaction in social robots to support older adults with chronic conditions in collecting personal narratives, monitoring emotional states, and promoting therapeutic adherence. The system adopts a hybrid architecture combining a Finite State Machine (FSM) for structured dialogue progression, LLM-driven dynamic prompting for context-aware responses, and profile-aware retrieval based on long-term user memory. A within-subject study with twenty older adults compared the proposed system with a purely generative baseline. Results show that the structured approach significantly improves perceived sociability, enjoyment, and social presence, while maintaining comparable ease of use. Additionally, the system enables the extraction of health-related and emotional parameters to support integrated monitoring. These results suggest that structured human-robot dialogue provides an effective and scalable approach for embedding NBM principles into assistive technologies.