A Comprehensive AI-Driven Framework for Adaptive Learning: Integrating Multi-Dimensional Learner Modeling, Intelligent Content Recommendation, and Real-Time Personalization

Rejina P V1, Dr. Sandhya Dwivedi2

1Research scholar, Department of computer science and engineering,

Asian international university Imphal, West Manipur.

2Associate professor, Department of computer science and engineering,

Asian international university Imphal, West Manipur.

Abstract -

Background: Current adaptive learning systems typically focus on single dimensions of personalization and lack comprehensive integration of advanced AI techniques, limiting their effectiveness compared to human tutoring.

Objective: This study develops and evaluates a modular AI-driven framework that integrates multi-dimensional learner modeling, hybrid content recommendation, real-time adaptation, and explainable AI components to improve learning outcomes over both traditional computer-assisted instruction and existing adaptive systems.

Methods: We implemented a four-component framework using attention-enhanced LSTM networks for learner modeling, neural collaborative filtering with educational constraints for content recommendation, deep reinforcement learning for real-time adaptation, and causal reasoning for explainability. The framework was evaluated through a randomized controlled trial (N = 1,247 students) using the ASSISTments dataset, comparing against traditional CAI and a state-of-the-art adaptive baseline (DKT-based system). Primary outcomes included learning gains (pre-post assessments), knowledge retention (30-day follow-up), and engagement metrics, analyzed using mixed-effects models with Bonferroni correction for multiple comparisons.

Results: Compared to traditional CAI, the proposed framework showed moderate but significant improvements: learning effectiveness increased by 12.3% (d = 0.34, 95% CI [0.21, 0.47], p < 0.001), knowledge retention improved by 15.7% (d = 0.41, 95% CI [0.28, 0.54], p < 0.001), and engagement increased by 8.9% (d = 0.28, 95% CI [0.15, 0.41], p < 0.001). Compared to the adaptive baseline, improvements were smaller but significant: learning effectiveness (d = 0.22, p = 0.003), retention (d = 0.27, p < 0.001), and engagement (d = 0.19, p = 0.012). Ablation studies confirmed synergistic effects of integrated components.

Conclusions: The comprehensive framework demonstrates statistically significant but modest improvements over existing approaches. While promising, the practical significance requires further validation across diverse educational contexts.

Key Words:  Adaptive Learning, Deep Learning, Educational Data Mining, Multi-Objective Optimization, Explainable AI, Randomized Controlled Trial