Comprehensive bibliometric analysis of artificial intelligence and E-Learning research trends (2014–2024)

This study conducts a comprehensive bibliometric analysis of research on AI in e-learning from 2014 to 2024, addressing the lack of a systematic overview of this rapidly evolving interdisciplinary field. Using the Scopus database and Biblioshiny package in R, we analyzed 282 documents from 127 sources to map research trends, key contributors, collaboration patterns, and emerging themes through co-citation networks, relational patterns, and keyword co-occurrence analysis. Results reveal 31.28% annual growth in publications, with China, the United States, and India as leading contributors. IEEE Access emerged as the most prolific journal (23 publications), while Intel Labs and Purdue University led institutional contributions. International collaborations comprised 24.47% of publications, highlighting the field's global nature. Key emerging technologies include generative AI, immersive technologies (VR/AR), and personalized learning systems, alongside growing attention to ethical considerations. The field has evolved from basic technological applications toward sophisticated, interdisciplinary implementations addressing complex educational challenges, with increasing focus on personalization, immersive experiences, and ethical frameworks. Findings guide educational practitioners in implementing evidence-based AI technologies, help policymakers address geographical disparities in research and implementation, and provide researchers with a roadmap of emerging subfields for future investigation, emphasizing contextual adaptation for effective implementation across diverse educational settings.