Scalable Observed Correction With Generative AI: How Motivationally Relevant Message Features and Design Choices Boost Communicative Engagement
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.
Authors
- Michael W. Wagner (ORCID: https://orcid.org/0000-0003-4590-5033)
- Porismita Borah (ORCID: https://orcid.org/0000-0002-1140-4233)
- Xiaohui Cao
- Yibing Sun (ORCID: https://orcid.org/0000-0003-1754-9895)
- Liwei Shen
- Dhavan V. Shah
- Sijia Yang (ORCID: https://orcid.org/0000-0003-4209-9881)
- Ji Soo Choi
Institutions
- University of Wisconsin–Madison (US)
- Washington State University (US)
Publication Details
- Journal
- Science Communication
- Published
- 2026-05-11
- DOI
- https://doi.org/10.1177/10755470261438828
- Primary Topic
- Misinformation and Its Impacts
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Wisconsin Alumni Research Foundation
- John S. and James L. Knight Foundation
- Directorate for Technology, Innovation and Partnerships