AI-CONECT: Designing Responsible-AI-based Conversational Chatbots for Dementia Intervention

作者：Junyuan Hong, Wenqing Zheng, Han Meng, Siqi Liang, Liu Chen, Hiroko H. Dodge, Jiayu Zhou, Zhangyang Wang · 发表于：Innovation in Aging · 年份：2025 · DOI：10.1093/geroni/igaf122.3537 · 被引用次数：1 · 研究领域：Digital Mental Health Interventions、Artificial Intelligence in Healthcare and Education、AI in Service Interactions

Abstract Social isolation is recognized as a major modifiable risk factor for dementia. The I-CONECT clinical trial demonstrated that semi-structured, cognitively stimulating conversations can be an effective strategy for combating social isolation and cognitive decline among older adults with mild cognitive impairment (MCI). However, scaling such interventions is limited by the availability of trained human interviewers. To address this challenge, we developed the AI-CONECT Chatbot, a Responsible-AI system designed to implement the I-CONECT conversational intervention protocol using large language models (LLMs). This approach enables broad, scalable deployment without the human resource limitations of the original intervention. Our approach combines a privacy-preserving learning framework to automatically generate and evaluate protocol-compliant instructional prompts for LLMs with a novel evaluation strategy using “virtual users”—AI-based replicas of participants with MCI and participants with normal cognition (NC) from the I-CONECT clinical trial. In simulated conversations between chatbots and the virtual users, we found that optimized prompts can improve the user’s word ratio significantly over ChatGPT (ChatGPT: mean=0.235, std=0.059; ours: mean=0.489, std=0.065, p = 0.0004 by Mann-Whitney U test). The high word ratio implies better users’ conversational engagement. These findings suggest that LLMs, when carefully prompted, can engage older adults in cognitively stimulati...