ArticleScientific reports2026
Conditional effects of source expertise and pre-existing attitudes on objective knowledge in AI-assisted health information verification.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Generative AI systems are increasingly integrated into health communication, yet it remains unclear under what conditions source-related cues shape objective knowledge outcomes when individuals verify health information with AI assistance. This study examines the roles of source expertise, pre-existing attitudes, and knowledge states in shaping objective knowledge during AI-assisted health information verification. In an experiment with 103 participants, individuals viewed a mixed-accuracy Facebook post about gluten-free diets attributed to either an expert-labeled or a non-expert-labeled source, then used ChatGPT to verify the information. Source expertise alone did not enhance objective knowledge, but its effect emerged among participants with favorable pre-existing attitudes. The predicted moderating role of knowledge state (uncertain vs. misinformed) was not supported, although exploratory patterns indicated greater responsiveness among uncertain users. The findings suggest that dual-process perspectives are informative for understanding AI-assisted information processing in contexts where verification is supported by generative AI.
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