Examining AI Use in Educational Contexts: A Scoping Meta-Review and Bibliometric Analysis

With growing research attention in artificial intelligence in education (AIED), there is a profusion of systematic reviews that have investigated AI integration in a wide variety of educational contexts, including PreK-12 schools and higher education. Even though existing systematic reviews have explored effects of AI on education, few studies have synthesized the results of those reviews. To fill this gap, we conducted a scoping meta-review and bibliometric analysis to map the broad field of AIED and identify research gaps based on our analysis. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, we searched Scopus and Web of Science and collected 126 review articles from 2014 to Mid-August of 2023 that satisfied our inclusion criteria. Specifically, we used four criteria for the screening process: (1) the article’s full text was available in English; (2) the article was published in peer-reviewed journals; (3) the article was a systematic review; and (4) the article was focused on AI use in one or multiple educational context(s). Our findings revealed the key characteristics of the included reviews, such as keyword co-occurrence network, co-authorship network, geographic distribution of authorship, educational levels, and subject areas. Three major themes related to AIED were generated, encompassing AI’s impact on teaching, learning, and administration. We also identified commonalities and nuanced differences between PreK-12 education and higher education along with research areas that have been underexplored. Our study not only expanded understanding of studies on AIED, but also provided suggestions on how to strengthen future research.

Duke Scholars

Author Zhenjie Weng DKU Faculty