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

arXiv: https://arxiv.org/abs/2504.08985 · HTML: https://mlsanity.com/paper/2504.08985/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Builds on (most cited first, 1 of 1)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)

## Abstract (arXiv)

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.