Bibliometric Analysis of RCT Studies on AI Educational Tools' Usage Frequency and Self-directed Learning Ability

Liu Baotong^*

Department of Education, University of Durham, UK

Abstract：

This study undertakes a bibliometric analysis of 52 eligible randomized controlled trial (RCT) papers published during the period 2000–2024, with literature retrieved from two authoritative databases: Google Scholar (primary source) and CNKI (supplementary source). Utilizing three core analytical approaches—time series analysis, keyword co-occurrence analysis, and cluster analysis—this research systematically explores the temporal evolution and thematic landscape of the field. The findings indicate that the research on AI educational tools and self-directed learning ability has entered a phase of rapid growth since 2018, with core hotspots concentrated on AI educational tools, self-directed learning ability, and RCT methodology, as well as three well-defined thematic clusters. This work clarifies the research status and development trends of the domain, thereby providing valuable insights and directions for future empirical research.

Key Words：

AI educational tools; self-directed learning ability; RCT; bibliometric analysis