Acceptability and Usability of a Socially Assistive Robot Integrated With a Large Language Model for Enhanced Human-Robot Interaction in a Geriatric Care Institution: Mixed Methods Evaluation

Background: Socially assistive robots (SARs) hold promise for supporting older adults (OAs) in hospital settings by promoting social engagement, reducing loneliness, and enhancing emotional well-being. They may also assist health care professionals by delivering information, managing routines, and alleviating workload. However, their acceptability and usability remain major challenges, particularly in dynamic real-world care environments. Objective: This study aimed to evaluate the acceptability and usability of a SAR in a geriatric day care hospital (DCH) and to identify key factors influencing its adoption by OAs and their informal caregivers. Methods: Over the course of 1 year, 97 participants (n=65, 67%, OA patients and n=32, 33%, informal caregivers) took part in a mixed methods evaluation of ARI, a socially assistive humanoid robot developed by PAL Robotics. ARI was deployed in the waiting area of a geriatric day care robot in Paris (France), where it interacted with users through voice-based dialogue. After each session, participants completed 2 standardized assessments, the Acceptability E-scale (AES) and the System Usability Scale (SUS), administered orally to ensure accessibility. Open-ended qualitative feedback was also collected to capture subjective experiences and contextual perceptions. Results: Acceptability scores significantly increased across waves (wave 1: mean 15.4/30, SD 5.81; wave 2: mean 20.9/30, SD 5.25; wave 3: mean 22.5/30, SD 4.23; P<.001). Usability scores also improved (wave 1: mean 47.9/100, SD 24.18; wave 2: mean 57.4/100, SD 22.46; wave 3: mean 69.3/100, SD 16.03; P<.001). A strong positive correlation was observed between acceptability and usability scores (r=0.664, P<.001). Qualitative findings indicated improved ease of use, clarity, and user satisfaction over time, particularly following the integration of a large language model (LLM) in wave 2, leading to more coherent, natural, and context-aware interactions. Conclusions: Successive system enhancements, most notably the integration of an LLM, led to measurable gains in usability and acceptability among patients and informal caregivers. These findings underscore the importance of iterative, user-centered design in deploying SARs in geriatric care environments.