Artificial Intelligence in Elementary Education: A Bibliometric Analysis and Systematic Literature Review
DOI:
10.29303/jppipa.v11i11.12752Published:
2025-11-25Downloads
Abstract
This study presents a bibliometric analysis and systematic literature review of AI in primary education by searching the Scopus database in August 2025. The initial search identified 619 articles, and after screening, 57 articles published between 2021 and 2025 were analyzed. The analysis was conducted using bibliometric analysis and the TCCM framework, which was applied to answer research questions and map research gaps. The findings show an increasing trend in publications each year, with the most articles published in 2025 (n = 27), while the peak in annual citations occurred in 2024 (with 561 citations). Keyword co-occurrence analysis formed five clusters, namely AI literacy and pedagogy; GenAI and personalization; student perceptions; AI literacy in elementary schools; and student behavior. The TCCM analysis identified various theories used, with TAM/UTAUT being the most frequently applied. In terms of context, the most frequent participants were elementary school students (≈50%; 26 studies), followed by teachers, prospective teachers/students, a combination of students and teachers, and school management. In terms of characteristics, 15 main categories of factors were mapped, namely personal, intention, attitude, usage, usefulness, ease, pedagogical, and knowledge, which were the dominant categories. In terms of methods, survey-based quantitative designs were the most dominant (n = 25; 44.6%), with SEM-PLS as the analysis technique. Furthermore, this study synthesizes the latest evidence to describe research gaps and offers a future research agenda for advancing AI in the context of primary education.
Keywords:
Artificial intelligence PRISMA framework TCCM (Theory Context Characteristics Methods framework Technology for educationReferences
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