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MindTalker: Navigating the Complexities of AI-Enhanced Social Engagement for People with Early-Stage Dementia

Xygkou, Anna, Chee Siang, Ang, Siriaraya, Panote, Kopecki, Jonasz, Covaci, Alexandra, Kanjo, Eiman, Wan-Jou, She (2024) MindTalker: Navigating the Complexities of AI-Enhanced Social Engagement for People with Early-Stage Dementia. In: 2024 ACM (Association of Computing Machinery) CHI conference on Human Factors in Computing Systems. . (In press) (doi:10.1145/3613904.3642538) (KAR id:105061)

Abstract

People living with dementia are at risk of social isolation, and conversational AI agents can potentially support such individuals by reducing their loneliness. In our study, a conversational AI agent, called MindTalker, co-designed with therapists and utilizing the GPT-4 Large Language Model (LLM), was developed to support people with early-stage dementia, allowing them to experience a new type of “social relationship” that could be extended to real life. Eight PwD engaged with MindTalker for one month or even longer, and data was collected from interviews. Our findings emphasized that participants valued the novelty of AI, but sought more consistent, deeper interactions. They desired a personal touch from AI, while stressing the irreplaceable value of human interactions. The findings underscore the complexities of AI engagement dynamics, where participants commented on the artificial nature of AI, highlighting important insights into the future design of conversational AI for this population.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1145/3613904.3642538
Uncontrolled keywords: GPT4, chatbots, dementia, conversational AI
Subjects: T Technology
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders: University of Kent (https://ror.org/00xkeyj56)
Depositing User: Anna Xygkou
Date Deposited: 21 Feb 2024 14:02 UTC
Last Modified: 21 Feb 2024 14:02 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/105061 (The current URI for this page, for reference purposes)

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