Learning from Elders: Making an LLM-powered Chatbot for Retirement Communities more Accessible through User-centered Design

Luna Xingyu Li, Ray-yuan Chung, Feng Chen, Wenyu Zeng, Yein Jeon +1 more
4/11/2025
cs.HCcs.AI

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Abstract

Low technology and eHealth literacy among older adults in retirement communities hinder engagement with digital tools. To address this, we designed an LLM-powered chatbot prototype using a human-centered approach for a local retirement community. Through interviews and persona development, we prioritized accessibility and dual functionality: simplifying internal information retrieval and improving technology and eHealth literacy. A pilot trial with residents demonstrated high satisfaction and ease of use, but also identified areas for further improvement. Based on the feedback, we refined the chatbot using GPT-3.5 Turbo and Streamlit. The chatbot employs tailored prompt engineering to deliver concise responses. Accessible features like adjustable font size, interface theme and personalized follow-up responses were implemented. Future steps include enabling voice-to-text function and longitudinal intervention studies. Together, our results highlight the potential of LLM-driven chatbots to empower older adults through accessible, personalized interactions, bridging literacy gaps in retirement communities.

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@article{li2025learning,
  title  = {Learning from Elders: Making an LLM-powered Chatbot for Retirement Communities more Accessible through User-centered Design},
  author = {Luna Xingyu Li and Ray-yuan Chung and Feng Chen and Wenyu Zeng and Yein Jeon and Oleg Zaslavsky},
  year   = {2025},
  eprint = {2504.08985},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.08985v2}
}

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