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

• URL: http://arxiv.org/abs/2504.08985v2
• Date: Mon, 28 Apr 2025 06:10:19 GMT
• Title: Learning from Elders: Making an LLM-powered Chatbot for Retirement Communities more Accessible through User-centered Design
• Authors: Luna Xingyu Li, Ray-yuan Chung, Feng Chen, Wenyu Zeng, Yein Jeon, Oleg Zaslavsky,
• Abstract summary: Low technology and eHealth literacy among older adults in retirement communities hinder engagement with digital tools.<n>A pilot trial with residents demonstrated high satisfaction and ease of use, but also identified areas for further improvement.<n>Next steps include enabling voice-to-text function and longitudinal intervention studies.
• Score: 2.8968722634502098
• License: http://creativecommons.org/licenses/by/4.0/
• 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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