Artificial Intelligence-Driven Personalized Learning: Psychological Implications and Educational Outcomes

Junyao Wang, Yasmin Hussain and Chencheng Mao

International Journal of Education, Humanities and Social Sciences, 2025, vol. 2, issue 1, 24-39

Abstract: This paper explores the psychological implications and educational outcomes of artificial intelligence (AI)-driven personalized learning systems. The study delves into how AI facilitates customized learning experiences, adapting to individual student needs and learning styles. The research highlights the impact of AI on student motivation, cognitive load, and academic performance, as well as potential ethical concerns such as data privacy and algorithmic bias. Empirical findings from various case studies demonstrate how AI-driven platforms enhance engagement and retention rates. The study concludes with recommendations for optimizing AI use in education while addressing associated challenges.

Keywords: Artificial Intelligence; personalized learning; educational technology; cognitive psychology; student performance (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:axf:ijehss:v:2:y:2025:i:1:p:24-39

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