Integrating a Large Language Model Into a Socially Assistive Robot in a Hospital Geriatric Unit: Two-Wave Comparative Study on Performance, Engagement, and User Perceptions

Behavioral engagement with a SAR in geriatric care is shaped by both system performance and individual user characteristics. Improvements in dialogue quality observed in wave 2, coinciding with the integration of the LLM, were associated with higher interaction success and enhanced user experience. Nevertheless, other contextual or group-related factors may also have contributed to this outcome. These findings highlight the importance of combining multimodal behavioral analysis with self-reported measures to inform the iterative, user-centered design of socially responsive robots in clinical contexts.