Learning from Elders: Making an LLM-powered Chatbot for Retirement Communities more Accessible through User-centered Design
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908339701547008 |
|---|---|
| author | Li, Luna Xingyu Chung, Ray-yuan Chen, Feng Zeng, Wenyu Jeon, Yein Zaslavsky, Oleg |
| author_facet | Li, Luna Xingyu Chung, Ray-yuan Chen, Feng Zeng, Wenyu Jeon, Yein Zaslavsky, Oleg |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08985 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning from Elders: Making an LLM-powered Chatbot for Retirement Communities more Accessible through User-centered Design Li, Luna Xingyu Chung, Ray-yuan Chen, Feng Zeng, Wenyu Jeon, Yein Zaslavsky, Oleg Human-Computer Interaction Artificial Intelligence 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. |
| title | Learning from Elders: Making an LLM-powered Chatbot for Retirement Communities more Accessible through User-centered Design |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2504.08985 |