Scalable Observed Correction With Generative AI: How Motivationally Relevant Message Features and Design Choices Boost Communicative Engagement

Abstract

To address the challenge of scalability in fact-checking, we report two parallel experiments examining which message features may boost communicative engagement with AI-facilitated observed correction of misinformation related to human papillomavirus (HPV) vaccination. Study 1 (N = 1,789) found that expert endorsement enhanced AI-generated visual exemplars’ effects on engagement. Study 2 (N = 1,765) showed that the advantage of adopting an empathetic tone for AI (vs. human) fact-checkers was dependent on participants’ baseline perceived anthropomorphism in a nonlinear pattern. These findings highlight the importance of strategically selecting motivationally relevant messages and design features to improve the scalability of AI-powered observed correction.

Keywords

observed correction misinformation scalable fact-checking generative AI social diffusion audience engagement human–AI communication

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