Personalized AI-driven education: An AI framework for enhanced learning outcomes among school-age children

Atonte, Brandon Fangmbeng (2025) Personalized AI-driven education: An AI framework for enhanced learning outcomes among school-age children. World Journal of Advanced Engineering Technology and Sciences, 15 (1). pp. 2326-2327. ISSN 2582-8266

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Abstract

This paper presents Tutor AI, an educational platform that integrates AI-driven personalization with evidence-based pedagogical approaches for learners aged 3-17. The system features customizable 3D pedagogical agents, adaptive content delivery, and dynamic educational visualizations. Our implementation targets documented academic challenges in mathematics, reading, and science while addressing educational equity concerns. The paper outlines the theoretical framework, technical architecture, and projected impacts of the system. Based on meta-analytic evidence from comparable interventions, we project potential effect sizes ranging from 0.15-0.30 SD across core academic domains.

Item Type: Article
Official URL: https://doi.org/10.30574/wjaets.2025.15.1.0453
Uncontrolled Keywords: Artificial Intelligence in Education; Personalized Learning; Pedagogical Agents; Multimedia Learning; Educational Technology
Depositing User: Editor Engineering Section
Date Deposited: 04 Aug 2025 16:21
Related URLs:
URI: https://eprint.scholarsrepository.com/id/eprint/3267