AI literacy research topic mining and evolution analysis using the BERTopic model

With the rapid development of artificial intelligence (AI) technologies and their far-reaching impact on society, understanding the research topics of AI literacy and grasping their important topics and evolutionary trends are of great significance for promoting innovative development in AI literacy research. In this study, AI literacy research literature was selected from the Web of Science Core Collection, and the BERTopic was used for topic modeling to conduct an in-depth exploration of three aspects: topic content, intensity, and diffusion. The results show that AI literacy research includes six main topics: AI Literacy in Education, AI Literacy Influencing Factors and Measurement, Generative AI Applications and Assessment in Education, AI Literacy in Healthcare Professions, AI Governance, and AI Literacy in Libraries and Librarianship. Regarding topic intensity evolution, the research topics exhibited strong continuity and progression. Among them, Generative AI Applications and Assessment in Education emerged as an important topic after 2023, whereas AI Literacy Influencing Factors and Measurement is likely to become the most prominent research direction in AI literacy in the future. In terms of topic evolution paths, AI literacy research shows a dynamic evolutionary process of “emergence–differentiation–fusion,” reflecting the trend of interdisciplinary collaborative development. The findings of this study help reveal the knowledge structure and evolutionary logic of the AI literacy field, providing a systematic basis and directional guidance for future research topics, policy making, and educational practices.