Personalization and Generative Dialogue in Social Robotics for Eldercare: A User Study

Service robots have the potential to support cognitive and social well-being in long-term care facilities, yet their widespread adoption depends on intuitive interaction modalities that minimize user learning effort and the need for a technical expert on-ground. Spoken dialogue is a natural interface, and recent advances in large language models (LLMs) promise more flexible and engaging exchanges than traditional scripted systems. In this study, we implemented a modular speech-based architecture combining automatic speech recognition, text-to-speech synthesis, and a conversational agent capable of switching between a fully scripted and LLM-driven dialogue. The implemented architecture was embodied in a TIAGo robot (PAL Robotics) and tested to compare three conversational strategies: (1) scripted, pre-defined dialogue, (2) LLM-based free-form conversation, and (3) LLM-based conversation augmented with personal information provided through the prompt. Eighteen younger adults and eighteen older adults engaged in a five-minute interaction with the robot under all three conditions in a within-subject design, and subsequently completed the Almere model questionnaire. Across all subscales and both participant groups, differences between dialogue strategies were small and statistically non-significant, despite informal comments from several older participants indicating a perceived increase in intelligence or naturalness for the LLM conditions. The findings suggest that generative dialogue and basic personalization alone do not meaningfully shift perceived acceptance in brief, task-neutral encounters, underscoring the importance of longer-term deployment and functionally meaningful robot roles in future evaluations.