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Cited by 3Explainable Artificial Intelligence (XAI)Technology Use by Older AdultsSocial Robot Interaction and HRI

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Sometimes You Need Facts, and Sometimes a Hug: Understanding Older Adults' Preferences for Explanations in LLM-Based Conversational AI Systems

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Mathur, Niharika, Zubatiy, Tamara, Rozga, Agata, Forlizzi, Jodi, D Mynatt, Elizabeth·2026·Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3772318.3790812
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Abstract

Designing Conversational AI systems to support older adults requires these systems to explain their behavior in ways that align with older adults’ preferences and context. While prior work has emphasized the importance of AI explainability in building user trust, relatively little is known about older adults’ requirements and perceptions of AI-generated explanations. To address this gap, we conducted an exploratory Speed Dating study with 23 older adults to understand their responses to contextually grounded AI explanations. Our findings reveal the highly context-dependent nature of explanations, shaped by conversational cues such as the content, tone, and framing of explanation. We also found that explanations are often interpreted as interactive, multi-turn conversational exchanges with the AI, and can be helpful in calibrating urgency, guiding actionability, and providing insights into older adults’ daily lives for their family members. We conclude by discussing implications for designing context-sensitive and personalized explanations in Conversational AI systems.

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Metadata

Authors
Mathur, N., Zubatiy, T., Rozga, A., Forlizzi, J., & D Mynatt, E.
Affiliations
  • Georgia Institute of Technology (US)
  • Northeastern University (US)
  • Carnegie Mellon University (US)
  • Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts, USA
ORCID
Venue
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2026-04-13
Pages
1-21
DOI
10.1145/3772318.3790812
OpenAlex
W4415316642
Cited by
3
References
2
Citations by year
2026: 3
FWCI
25.31
Type
conference paper
Language
en
Open access
Licence
Locations
  • CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
Funders
National Science Foundation (2112633)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Health Sciences › Medicine › Geriatrics and Gerontology › Technology Use by Older Adults
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
Keywords
older adults, explainable AI, conversational AI, large language models, user preferences, multi-turn interaction, personalized explanations, user trust

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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