Detailed Analysis of Responses from Older Adults through Natural Speech: Comparison of Questions by AI Agents and Humans

Toshiharu Igarashi (), Katsuya Iijima, Kunio Nitta and Yu Chen
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Toshiharu Igarashi: Simulation of Complex Systems Lab, Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo 277-8563, Japan
Katsuya Iijima: Institute of Gerontology (IOG), The University of Tokyo, Tokyo 113-8656, Japan
Kunio Nitta: Tsukushikai Medical Corporation, Tokyo 186-0005, Japan
Yu Chen: Simulation of Complex Systems Lab, Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo 277-8563, Japan

IJERPH, 2024, vol. 21, issue 9, 1-15

Abstract: In recent years, an increasing number of studies have begun to use conversational data in spontaneous speech to estimate cognitive function in older people. The providers of spontaneous speech with older people used to be physicians and licensed psychologists, but it is now possible to have conversations with fully automatic AI agents. However, it has not yet been clarified what differences exist in conversational communication with older people when the examiner is either a human or an AI agent. In this study, elderly people living in the community and attending a silver human resource center and a day service center were the subjects. Dialogues were conducted using generic interview items for estimating cognitive function through daily conversation, which were developed through research on estimation methods for cognitive function. From the data obtained from the dialogues, we compared the effects of human–AI interaction on the number of utterances, speaking time, and silence time. This study was conducted at a facility in Japan and included 32 subjects (12 males and 20 females). The results showed significant differences between human and AI dialogue in the number of utterances and silent time. This study suggests the effectiveness of AI in communication with older people and explores the possibility of using AI in social welfare.

Keywords: AI agents; cognitive function estimation; Alzheimer’s disease; dementia; psychological burden (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2024
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