Using Artificial Intelligence to Support Scientometric Analysis of Scholarly Literature: A Case Example of Research on Mainland China’s Left-Behind Children

The rapid growth of scholarly literature poses significant challenges in effectively organizing, managing, and synthesizing research. This issue is particularly pronounced when the relevant literature is dispersed across numerous journals and languages. In this methodological article, we demonstrate how artificial intelligence (AI) technologies can support research designs that aim to comprehensively understand a specific area of study through scientometric and topical analysis. As a working example, we focus on the case of left-behind children (LBC) in mainland China. Our approach revealed significant growth and dispersion in LBC research, indicating a need for more integrated studies to address various aspects of LBC experiences. The findings also demonstrate that using AI technologies offers significant opportunities for managing a large and highly distributed body of research.