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https://doi.org/10.62836/jissh.v3i1.0004
Zhu, X., & Sabijon, Jr., A. C. (2026). Personalized Learning Through AI in Primary Education. Journal of Integrated Social Sciences and Humanities, 3(1), 0004. https://doi.org/10.62836/jissh.v3i1.0004
Volume 3, Issue 1, 2026

Personalized Learning Through AI in Primary Education

This study examines the role and implications of AI-driven personalized learning platforms in primary education. This paper synthesizes findings regarding the academic effectiveness of such platforms, their impact on teacher roles, and the ethical challenges they pose, particularly concerning equity and data privacy. To investigate these questions, this study employed a systematic literature review methodology, synthesizing peer-reviewed research from the past decade to critically examine the academic effectiveness, evolving teacher roles, and ethical implications of AI-driven personalized learning platforms in primary education. While AI platforms are shown to enhance individualized learning and provide real-time adaptive support, significant concerns emerge regarding algorithmic bias, the potential widening of achievement gaps, and threats to student data security. This investigation into the real-world impact of AI-powered personalized learning in primary education conclusive verdict of significant but conditional promise.

artificial intelligence personalized learning primary education educational equity teacher role data ethics

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Supporting Agencies

  1. Funding: This research received no external funding.