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Related Experiment Video

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Integrating a Large Language Model Into a Socially Assistive Robot in a Hospital Geriatric Unit: Two-Wave Comparative

Lauriane Blavette1,2,3, Sébastien Dacunha1,2,3, Xavier Alameda-Pineda4

  • 1Institut National de la Santé et de la Recherche Médicale, Optimisation Thérapeutique en Pharmacologie OTEN Unité Mixte de Recherche-Santé 1144, Université Paris Cité, Paris, France.

JMIR Human Factors
|December 3, 2025
PubMed
Summary
This summary is machine-generated.

Integrating large language models (LLMs) into socially assistive robots (SARs) significantly improved interaction success and user experience in a geriatric care setting. This advancement enhances the potential of SARs for older adults, addressing key challenges in healthcare.

Keywords:
behavioral engagementgeriatric carehuman-robot interactionlarge language modelsmultimodal analysisolder adultssocially assistive robot

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Area of Science:

  • Geriatric Medicine
  • Human-Robot Interaction
  • Artificial Intelligence in Healthcare

Background:

  • Older adults in resource-limited settings face complex medical and psychosocial needs.
  • Socially assistive robots (SARs) offer practical support, with large language models (LLMs) enhancing their dialogue capabilities.
  • Acceptability of SARs depends on minimizing errors and adapting to user characteristics.

Purpose of the Study:

  • To evaluate the impact of integrating an LLM into a SAR dialogue system in a hospital geriatric unit.
  • To compare system performance and interaction success across two experimental waves.
  • To explore user characteristics' influence on performance, acceptability, and usability.

Main Methods:

  • 28 older adults participated in a single-session evaluation of a SAR over 8 months.
  • Interactions were recorded across two waves: basic dialogue system and LLM-based system.
  • System performance, user engagement, acceptability, and usability were quantitatively and qualitatively analyzed.

Main Results:

  • LLM integration significantly increased error-free interactions (27.8% to 70.2%) and interaction success (25% to 74.5%).
  • Acceptability and usability scores were significantly higher with the LLM-based system.
  • Emotional engagement correlated positively with interaction success; age negatively impacted physical engagement and acceptability.

Conclusions:

  • Dialogue quality improvements from LLM integration enhance interaction success and user experience in geriatric care SARs.
  • Behavioral engagement is influenced by both system performance and individual user traits.
  • Multimodal behavioral analysis and self-reported measures are crucial for user-centered robot design in clinical settings.