The rapid expansion of generative artificial intelligence across educational, professional, and societal domains has intensified the need for a clear understanding of generative artificial intelligence literacy. Although scholarly interest in this topic has grown substantially in recent years, existing research remains dispersed across disciplines, limiting both theoretical consolidation and practical guidance. This study maps the scientific literature on generative artificial intelligence literacy by identifying its underlying thematic structure. A systematic literature review was conducted following PRISMA 2020 guidelines. We retrieved 40 peer-reviewed journal articles published between 2023 and 2025 from the Web of Science and Scopus databases. Topic modeling using Latent Dirichlet Allocation was applied to the full texts, with inter-rater reliability validation achieving substantial agreement (Cohen’s kappa = 0.78). The analysis revealed four interrelated thematic areas: ethical foundations (40%), educational use (32.5%), adoption and interaction (12.5%), and evaluation (15%). Geographic analysis showed notable concentration in Asia (50%) and educational settings (47.5%), with limited representation in healthcare, government, and industry sectors. Two critical gaps emerged: the scarcity of validated measurement instruments and a persistent disconnect between expert ethical frameworks and users’ ethical awareness. These findings provide a structured foundation for researchers, educators, and policymakers to develop evidence-based interventions and support the sustainable adoption of generative artificial intelligence technologies.
Ramírez-Correa et al. (Sat,) studied this question.