Understanding Older Adults’ Evaluation of AI-Generated Health Information

When older adults cannot distinguish accurate health information from misinformation within AI-generated content, they may misjudge its credibility and follow incorrect advice. To investigate how information characteristics shape older adults’ assessment of AI-generated health content, this study employed a 2 × 2 × 2 within-subject design manipulating emotional framing (fear vs. neutral), authority cues (high vs. low), and information veracity (true vs. false), with individual differences examined as exploratory variables. Seven older adults rated the credibility and adoption intention of AI-generated information and chose whether to verify it. Those verifying viewed corrections and re-rated initial information. Results showed that emotional framing and authority cues impaired participants’ ability to identify misinformation. Individuals with higher digital literacy and cognitive ability relied less on emotional framing. Findings suggest that such information characteristics make misinformation harder to identify, whereas digital literacy and cognitive ability may buffer this effect by reducing reliance on them.

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Publication Details

Journal
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Published
2026-10-29
DOI
https://doi.org/10.1177/10711813261498726
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
0.00
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article

Understanding Older Adults’ Evaluation of AI-Generated Health Information

Calvin Kalun Or, Yu Jiang
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Misinformation and Its Impacts
article

Understanding Older Adults’ Evaluation of AI-Generated Health Information

Calvin Kalun Or, Yu Jiang
article en

Abstract

When older adults cannot distinguish accurate health information from misinformation within AI-generated content, they may misjudge its credibility and follow incorrect advice. To investigate how information characteristics shape older adults’ assessment of AI-generated health content, this study employed a 2 × 2 × 2 within-subject design manipulating emotional framing (fear vs. neutral), authority cues (high vs. low), and information veracity (true vs. false), with individual differences examined as exploratory variables. Seven older adults rated the credibility and adoption intention of AI-generated information and chose whether to verify it. Those verifying viewed corrections and re-rated initial information. Results showed that emotional framing and authority cues impaired participants’ ability to identify misinformation. Individuals with higher digital literacy and cognitive ability relied less on emotional framing. Findings suggest that such information characteristics make misinformation harder to identify, whereas digital literacy and cognitive ability may buffer this effect by reducing reliance on them.

Proceedings of the Human Factors and Ergonomics Society Annual Meeting
University of Hong Kong (HK)
Openalex Percentile: Top 5%
Misinformation and Its Impacts
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