Artificial Intelligence in Education: A Bibliometric Analysis of Research Trends and Future Directions

This study aims to explore the research trends, intellectual structure, and emerging themes in Artificial Intelligence (AI) applications in education through a bibliometric approach. As AI adoption in education accelerates, understanding these patterns is essential for guiding future research and practical implementation. To achieve this, a bibliometric analysis was conducted on 3,441 documents published between 2010 and 2025, retrieved from major academic databases. Using VOSviewer, the study applied network, density, and overlay visualization to identify co-authorship networks, keyword co-occurrence, and thematic clusters. The findings reveal that machine learning and deep learning dominate the field, forming the core of research themes, while clusters highlight topics such as personalized learning, decision-making, ethical issues, and emerging technologies like generative AI. Early studies focused on foundational methods such as expert systems and fuzzy logic, whereas recent research emphasizes human-centered concerns, including privacy, fairness, and responsible AI use. The integration of AI with big data, IoT, and advanced analytics for educational management also shows significant growth. These insights provide valuable guidance for educators, policymakers, and researchers in promoting the ethical, inclusive, and effective application of AI in education.