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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
The Impact of AI-assisted Personalized Learning on Student Academic Achievement
Miling Chen
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DOI:10.17265/2161-623X/2025.06.008
University of Southampton, Southampton, United Kingdom
This exhaustive review reveals AI-assisted personalized learning systems’ transformational effects on student academic achievement among lactational levels. The study evaluates adaptive learning technologies concerning their ability to enhance student performance, engage students, and improve learning outcomes through a systematic review and analysis of research studies conducted from 2019-2024. Based on the meta-analysis, it can be concluded that for cognitive learning outcomes, students using an adaptive learning system had a medium-to-large positive effect size (g = 0.70) compared to students with a non-adaptive learning intervention. Another set of findings depicts an improvement of 0.36 standard deviations in students’ overall academic achievement and an improvement of 0.42 standard deviations in students’ mathematics achievement by students who benefited from adaptive instruction relative to those students who underwent traditional instruction, which can be translated into approximately around three to five months of additional learning compared to those students who underwent traditional instruction. This discovery witnesses the ability of AI-assisted personalized learning to efficiently address student learning differences, optimise educational resource allocations, and increase student retention rates. Nevertheless, the issues of data privacy, widening gaps among digital haves and have-nots, and strategic implementation must be addressed.
artificial intelligence, personalized learning, adaptive learning, student achievement, educational technology, learning outcomes, academic performance
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