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Abstract

This study conducted a systematic review of scholarly research that addressed the use of artificial intelligence (AI) technologies in supporting decision- making in education, with the purpose of identifying recent trends, applications, challenges, and research gaps in this field. The study adopted systematic literature review methodology, analyzing a total of 157 studies published between January 2020 and March 2025, which were retrieved from accredited databases, including Al Mundhoma, Google Scholar, Springer, ProQuest, and Scopus. The analysis revealed a diverse range of tools and techniques used to apply AI in educational decision-making, most notably machine learning algorithms, predictive models, and intelligent decision support systems. The findings indicated that most studies focused more on supporting educational decision-making than administrative decision-making. Prominent areas of application included academic performance analysis, content personalization, and educational assessment and evaluation. In contrast, the use of AI to support administrative decision-making within educational institutions was found to be limited. The study revealed that AI applications enhance decision quality, accelerate processes, and reduce bias, while also facing challenges such as inadequate digital infrastructure, privacy concerns, and limited human capacity. It recommends improving infrastructure, building staff capacity, and fostering technical partnerships. The study emphasizes that the effective use of AI in educational decision-making requires integration between technology and human expertise within frameworks that uphold equity and sustainability.

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