Bibliometric analysis reveals geographic disparities in AI personalized learning research, highlighting the need for targeted infrastructure in developing regions.
Artificial intelligence (AI) technologies are increasingly recognised for their potential to enable personalised learning; however, their application and research visibility in developing-country contexts remain limited. This study addresses this gap by conducting a bibliometric analysis of global research on AI in education, with specific attention to its implications for the Global South. A total of 1,583 scholarly articles published between 2020 and 2024 were analysed using data sourced from Scopus, Web of Science, ScienceDirect, and Google Scholar. VOSviewer was used to map keyword co-occurrence patterns, research clusters, and thematic relationships. The findings reveal that AI constitutes a central node in the literature, strongly interconnected with personalised learning, adaptive systems, and learning analytics. Dominant research clusters highlight key technologies such as machine learning, deep learning, and natural language processing, while emerging themes emphasise ethical considerations, data governance, and scalability. Notably, the analysis reveals a significant imbalance in research output, with the Global North dominating contributions, underscoring a limited empirical focus on developing-country contexts. This study makes a novel contribution by synthesising global research trends while critically foregrounding the implications, opportunities, and contextual challenges of deploying AI-enabled personalised learning in resource-constrained environments. It advances existing bibliometric work by identifying underexplored areas, including infrastructure limitations, digital inequality, and the contextual adaptation of AI solutions in the Global South. The study concludes that while AI holds significant promise for enhancing personalised learning, its effective implementation in developing countries requires targeted investment in digital infrastructure, context-sensitive policy frameworks, and ethical governance mechanisms.
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Nzama et al. (2026) studied this question.
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