Bibliometric Analysis Of Artificial Intelligence In Education: Study From ScienceDirect Database

Abstract

Phenomenon/Issue: The rapid development of technology, particularly artificial intelligence (AI), has transformed various sectors, including education. The increasing interest in AI in education has prompted further research to understand its impact and applications.

Purpose: This study aims to analyze the publication landscape of AI in education using bibliometric analysis, identifying key trends, influential authors, and frequently used keywords in the field.

Novelty: The study offers a comprehensive bibliometric analysis of publications on AI in education, providing a detailed mapping of authorship networks and keyword usage patterns from 2020 to 2024, a period marked by significant AI advancements.

Research Methods: The study employs bibliometric analysis using VOSviewer 1.6.20 software. Data were sourced from 50 publications on ScienceDirect, focusing on co-authorship and co-occurrence techniques. The analysis generated network, overlay, and density visualizations to illustrate key findings.

Results: The analysis reveals interconnected authors and identifies Hwan and Gwo Jen as the most cited contributors. The co-occurrence analysis highlights “artificial intelligence” as the most frequently used keyword, underscoring its centrality in discussions about AI in education.

Research Contributions: This study contributes to understanding the publication trends and scholarly networks in AI in education, offering valuable insights for researchers and educators. The bibliometric mapping serves as a resource for identifying influential works and emerging themes, guiding future research and development in the field.