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Jonasz Piotr Kopecki

dblp:374/8588 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0006-2934-8491ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Health and well-being technologies · 39% Human-AI interaction · 30% Collaborative and social computing · 30%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-AI interaction
conversational agents
0.812024
MindTalker: Navigating the Complexities of AI-Enhanced Social Engagement for People with Early-Stage Dementia · CHI 2024
Health and well-being technologies › dementia care
dementia support
0.812024
MindTalker: Navigating the Complexities of AI-Enhanced Social Engagement for People with Early-Stage Dementia · CHI 2024
Collaborative and social computing › social interaction
social engagement
0.812024
MindTalker: Navigating the Complexities of AI-Enhanced Social Engagement for People with Early-Stage Dementia · CHI 2024
Health and well-being technologies
social isolation
0.212024
MindTalker: Navigating the Complexities of AI-Enhanced Social Engagement for People with Early-Stage Dementia · CHI 2024

Methods — techniques the papers use, named apart from their topics

interview study · 0.8co-design · 0.8GPT-4 · 0.8
YearPublicationVenuePosition
2024 MindTalker: Navigating the Complexities of AI-Enhanced Social Engagement for People with Early-Stage Dementia
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.
Anna Xygkou, Chee Siang Ang, Panote Siriaraya, Jonasz Piotr Kopecki, Alexandra Covaci, Eiman Kanjo, Wan-Jou She
CHI4