The Information Age for Education via Artificial Intelligence and Machine Learning: A Bibliometric and Systematic Literature Analysis

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in education is a rapidly evolving field, yet the long-term implications and actual impacts on student learning outcomes require more in-depth study. Address this gap, our study offers a novel approach combining bibliometric analysis and a Systematic Literature Review (SLR), guided by the PRISMA methodology. The first phase, a comprehensive bibliometric analysis, identified key nations, educational institutions, journals, keywords, and influential authors in the realm of AI/ML in educational settings. This phase provided a macro-level understanding of the field’s landscape, showcasing the global and interdisciplinary nature of AI/ML research in education. The subsequent phase involved a meticulous SLR of 22 select scholarly articles. This in-depth review sheds light on the current applications, emerging trends, challenges, and future directions of AI and ML in education. The findings from this dual-method approach offer a comprehensive roadmap for educators, researchers, and policymakers, underscoring the transformative potential of AI and ML in the educational sector. The review’s extensive article collection provides a deep dive into the diverse and significant impact of AI in education, highlighting its role in areas such as predicting academic success, enhancing e-learning experiences, and preparing future generations for AI’s integration in various fields like healthcare. This study not only underscores the revolutionary potential of AI in reshaping educational landscapes but also serves as a guiding framework for effectively deploying AI and ML technologies in education.

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