Tracing Two Decades of Artificial Intelligence in Education: A Bibliometric Analysis of Trends, Themes, and Future Directions (2000–2025)

Abstract

Despite the increasing integration of artificial intelligence (AI) into education, a comprehensive understanding of how scholarly discourse has evolved over time remains limited. Most existing studies focus on technical implementation or short-term pedagogical outcomes, often lacking longitudinal scope or thematic synthesis. This study addresses that gap by offering a 25-year bibliometric analysis of AI-related educational research, mapping its conceptual development, publication trends, and emerging priorities from 2000 to 2025. Using data sourced from Lens.org and processed through Biblioshiny (R-Studio) and VOSviewer, 350 peer-reviewed articles were analyzed based on their thematic focus, keyword evolution, authorship patterns, and citation networks. The novelty of this study lies in its integration of bibliometric mapping with temporal thematic evolution, enabling a detailed understanding of how foundational concepts, such as lifelong learning, AI literacy, and ethics, have transitioned from peripheral concerns to central research themes. Findings show a sharp increase in publication volume after 2018, reflecting the impact of cloud-based AI platforms and the pandemic-induced pivot to remote education. While “artificial intelligence” and “education” remain dominant keywords, emerging themes such as “student well-being,” “digital competency,” and “personalized learning” suggest a shift toward more human-centered and ethically conscious AI applications. The study concludes by identifying persistent gaps related to pedagogical effectiveness, global equity, and critical digital literacy, offering a roadmap for future interdisciplinary research and inclusive educational policy.

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