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Comparative Study
. 2024 Sep 3;21(9):1170.
doi: 10.3390/ijerph21091170.

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

Affiliations
Comparative Study

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

Toshiharu Igarashi et al. Int J Environ Res Public Health. .

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; Alzheimer’s disease; cognitive function estimation; dementia; psychological burden.

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Conflict of interest statement

The authors declare no conflicts of interest. Tsukushikai Medical Corporation had no role in the design of this study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
(a) The appearance of the author who conducted the interpersonal conversation and (b) the appearance of the AI agent modeled based on the author’s appearance.
Figure 2
Figure 2
Protocol of daily conversation system for cognitive function estimation by AI agents.
Figure 3
Figure 3
Configuration diagram showing the entire system.
Figure 4
Figure 4
Comparison of high- and low-cognitive-function groups in terms of number of utterances. *: Significant differences were found at the 5% level.
Figure 5
Figure 5
Comparison of high- and low-cognitive-function groups in terms of seconds of speech.
Figure 6
Figure 6
Comparison of high- and low-cognitive-function groups in terms of silent seconds. **: Significant differences were found at the 1% level.

References

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