With the rapid proliferation of generative artificial intelligence (AI), research on AI-generated misinformation and disinformation has expanded significantly. However, systematic reviews remain limited, and most existing reviews focus on detection methods or single types of misinformation, such as deepfakes. To address this gap, this study reviews 24 journal articles published before 22 February 2025, identified through searches in Scopus and Web of Science. Using the PRISMA framework, and the Mixed Methods Appraisal Tool (MMAT), methodological rigor was ensured. Findings show that most research centers on AI-generated videos, particularly in political communication, and most studies employ quantitative experimental methods. Analyses of cross-cultural, cross-platform, algorithmic, and governance perspectives are rare. Much of the literature examines individual-level effects, such as credibility judgments and sharing intentions, while relatively little attention is given to platform algorithms, institutional interventions, or broader societal impacts. By integrating dispersed findings and mapping key methodological and thematic gaps, this review provides a more holistic understanding of the complex challenges posed by AI-generated misinformation and disinformation.
Yun Wang (Thu,) studied this question.