Artificial intelligence in personalized learning: A global systematic review of current advancements and shaping future opportunities

Abstract

This study investigates the role of Artificial Intelligence (AI) in personalized learning within tertiary and higher education contexts worldwide. Guided by the PRISMA 2020 framework (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a systematic literature review of 25 Scopus-indexed articles published between 2019 and 2024 was conducted to map publication trends, identify dominant AI technologies, and synthesize the benefits, challenges, and future directions of AI-enabled personalized learning systems. The findings reveal a rapid shift from early rule-based systems to sophisticated models that integrate machine learning, natural language processing, intelligent tutoring systems, and large language models such as ChatGPT. AI has been shown to enhance student engagement, motivation, and performance by providing adaptive learning pathways, real-time feedback, and tailored content. However, several challenges persist most notably data privacy and ethical concerns, technological infrastructure constraints, educator readiness, and limited scalability across diverse educational contexts. While AI-driven personalization improves learning effectiveness compared to traditional methods, long-term impacts and issues of equity and inclusion remain underexplored. This review highlights the need for ethical frameworks, robust teacher training, and integrating emerging multimodal AI technologies to support more inclusive, sustainable, and human-centered personalized learning ecosystems. The study provides strategic insights for researchers, educators, and policymakers to guide future deployment of AI in education.

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