Intellectual Humility as a Cognitive Filter for AI-Generated Health Misinformation. An Evolutionary Perspective on Epistemic Vigilance
2026-06-02 • Human-Computer Interaction
Human-Computer InteractionComputers and Society
AI summaryⓘ
The authors studied how people with intellectual humility (knowing the limits of what they know) judge health conversations made by AI that differ in how accurate they are. They found that people with more humility were better at spotting and doubting false or pseudoscientific information but didn't think more or less about the really accurate stuff. This humility didn't help people figure out if AI made the conversation, only if the content was believable or not. The authors suggest this humility is an old skill humans use to handle tricky information, which still works even with new AI content.
intellectual humilityepistemic vigilancepseudoscienceAI-generated contenthealth communicationmetacognitionsource attributionevolutionary adaptationscientific rigorepistemic limitations
Authors
Marcin Rządeczka, Maciej Wodziński, Kacper Zacharski, Marcin Moskalewicz
Abstract
We present experimental findings from a study (N=99) examining how intellectual humility (IH), i.e., the metacognitive awareness of epistemic limitations, affects the evaluation of AI-generated health dialogues varying in scientific rigor. Participants were randomly assigned to evaluate one of three dialogues about exercise and mental health: scientifically accurate, moderately pseudoscientific, or strongly pseudoscientific. Results reveal that IH functions as a selective cognitive filter. Individuals with higher humility scores rated pseudoscientific content as significantly less credible, while showing no correlation with credibility assessments of accurate content. Crucially, humility did not predict the ability to identify AI as the source of dialogues, suggesting that epistemic vigilance operates on content quality rather than source attribution. We interpret these findings through an evolutionary lens, proposing that IH represents an ancestral adaptation for navigating informationally uncertain environments. It remains effective at detecting exploitation attempts in AI-generated content, despite humans lacking evolved mechanisms for detecting AI sources. The study contributes to understanding how foundation models might improve or undermine human epistemic defenses, especially in health communication contexts.