Who Do We Trust to Correct Health Misinformation? Credibility of AI-Generated Fact-Checking Messages

As artificial intelligence (AI)-generated messages are increasingly used to correct health misinformation, it remains unclear when such messages are perceived as credible and how credibility shapes subsequent information behaviors. This study examines how source labeling (AI vs. human scientists) influences the perceived credibility of health-related fact-checking messages and whether these effects depend on perceived AI objectivity and trust in science. Using a 2 × 2 between-subjects experiment with U.S. adults (N = 231), we assessed credibility perceptions and three information responses: accuracy-oriented information seeking, fact-checking message sharing, and issue-relevant information avoidance. Source labeling alone did not affect perceived credibility. Instead, credibility judgments were conditional. Perceived credibility was positively associated with message sharing and negatively associated with information avoidance but was not robustly related to accuracy-oriented information seeking. Independent of credibility, AI-labeled fact-checking messages elicited greater intentions to seek additional information. These findings highlight the conditional nature of AI credibility in health communication and underscore the importance of aligning AI-based fact-checking with audience trust orientations.