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Daisy M. Kiyemba

dblp:371/1582 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0004-6314-4340ORCID · reported

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 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
Human-AI interaction · 77% Health and well-being technologies · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Health and well-being technologies
mild cognitive impairment
0.212024
Evaluation Tools for Human-AI Interactions Involving Older Adults with Mild Cognitive Impairments · HRI 2024

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

measurement tool categorization · 0.8
YearPublicationVenuePosition
2024 Evaluation Tools for Human-AI Interactions Involving Older Adults with Mild Cognitive Impairments
abstract
As artificial intelligence (AI) systems have already proven useful in human lives generally, there is an opportunity for specialized human-AI interaction (HAI) systems to support and provide care for older adults with mild cognitive impairment (MCI). However, the integration of this technology in this population must be thoughtfully designed to accommodate specific needs and limitations. This includes careful measurement of both humans and systems. We developed an evolving dataset categorizing relevant measurement tools into five groups: cognitive ability, demographics & personality, activity level, state of mind, and perceptions of the AI system. Each instance of the tool being used in the literature cataloged in the dataset is qualified in terms of how likely we would recommend using it in the domain of HAI for older adults with MCI based on contextual factors and internal reliability measures. This dataset will serve as a valuable resource for future research, aiding in the identification of promising areas and trends in AI systems for older adults with MCI as well as providing essential tools for future studies.
Daisy M. Kiyemba, Jasmin Marward, Elizabeth J. Carter, Adam Norton
HRI1