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.
@article{arxiv.2504.08985,
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},
journal= {arXiv preprint arXiv:2504.08985},
year = {2025}
}
Comments
Accepted as Research talk for Considering Cultural and Linguistic Diversity in AI Applications workshop at CALD-AI@ASIS&T 2025