Engaging All Elderly Residents in Community Renewal: Designer Spotlight Interview Tool for LLM Building
Open Access
Article
Conference Proceedings
Authors: Ruchen Hu, Zihui Chen, Mengshi Yang, Qiong Wu
Abstract: The natural language processing capabilities of Large Language Models (LLMs) can significantly enhance designers' ability to quantify unstructured information and improve communication with users, which is particularly important in rapidly aging societies. As elderly individuals engage in community renewal projects, they often face comprehension and expression barriers due to differences in cultural backgrounds and cognitive abilities, which complicates the acquisition of tacit knowledge for designers. To address this, we developed an AI community renewal toolkit, CommUnity AI, utilizing the fine-tuned ChatGPT-4o model. This toolkit provides easy-to-understand feedback to older adults through the creation of visual and textual information cards, and its effectiveness was evaluated in our study. The experiment involved 24 older adults and 6 designers, divided into experimental and control groups, and three separate focus group interviews were conducted. Using the SERVQUAL model to analyze the results, we found that the elderly participants showed greater trust and acceptance of the toolkit compared to traditional interview methods.CommUnity AI provides high-quality feedback through language comprehension, data collection, and visual and textual feedback, effectively reducing communication time while considering the needs and comprehension abilities of the elderly. This study underscores the potential of LLMs in community co-design, offering theoretical and practical insights into how designers can collaboratively engage with elderly individuals, ultimately fostering more inclusive and friendly community environments.
Keywords: LLMS, Human-computer Interaction, Artificial Intelligence, Collaborative Design, Age-appropriate tools, User Experience Evaluation
DOI: 10.54941/ahfe1005570
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