Building an AI-Driven Personalized Learning System to Enhance Training Effectiveness for Primary Education Students: A Comprehensive Framework and Empirical Evaluation

Building an AI-Driven Personalized Learning System to Enhance Training Effectiveness for Primary Education Students: A Comprehensive Framework and Empirical Evaluation

Article Number: e2026060 | Available Online: May 2026 | DOI: 10.22521/edupij.2026.23.60

Pham Quang Tiep , Ngo Thi Lien , Tran Van The , Vu Thu Hang , Nguyen Thi Huong

Full text PDF | 425 | 230

Abstract

Background/Purpose. This study aims to develop and evaluate the effectiveness of an AI-driven personalized learning system in improving learning outcomes, motivation, and pedagogical competencies of Vietnamese primary education students within the context of digital transformation in teacher education.

Methods. A mixed-methods cluster-randomized controlled trial was conducted over 18 weeks with 187 second- and third-year students from 12 intact classes at the University of Education, Vietnam National University Hanoi. The intervention comprised three components: an intelligent learning analytics system, an AI-adaptive learning environment, and a personalized competency assessment system. Data were collected through standardized achievement tests, learning motivation scales, pedagogical competency assessments, and semi-structured interviews with 60 participants (48 students, 8 instructors, 4 administrators) using systematic thematic analysis.

Results. Using multilevel modeling, the experimental group demonstrated significant improvements compared to traditional methods: a 12.3% increase in academic performance (Cohen's d = 0.62), a 18.7% increase in learning motivation (Cohen's d = 0.68), and a 15.2% enhancement in pedagogical competencies (Cohen's d = 0.56). The most significant improvements were observed in personalized lesson design capabilities (17.8%) and educational technology integration skills (16.4%).

Conclusion. The AI-driven personalized learning system significantly improved training outcomes with moderate effect sizes consistent with rigorous educational technology research. However, potential contamination effects and assessment bias represent important limitations. The study provides a scalable implementation framework emphasizing instructor digital competency development, institutional infrastructure support, and comprehensive stakeholder engagement for sustainable adoption in Vietnamese educational contexts.

Keywords: Artificial intelligence, personalized learning, teacher training students, educational technology, digital transformation

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