A novel context-aware intelligent learning framework for personalized education in developing countries
Bulus Bali · Next research. · 2026
The persistent educational divide in developing countries is worsened by infrastructural limitations, heterogeneous learner needs, limited teaching personnel, and the absence of adaptive Artificial Intelligence (AI) technologies. These challenges hinder equitable access to personalized learning and scalable instructional delivery. This study proposes a novel context-aware intelligent learning framework to deliver personalized instruction by dynamically adapting to learner cognitive load, engagement patterns, and modality preferences. This framework advances intelligent learning technologies and deepens understanding of how learners interact with adaptive systems, reflecting the intelligent, behavioral, and socio-cultural dimensions of human–computer interaction within developing contexts. The framework integrates RL, real-time learner modeling, and edge-compatible deployment strategies to support operation across diverse settings: rural offline, urban hybrid, and smart classrooms. A robust mathematical model incorporates user-specific data, contextual variables, and ethical constraints through multi-objective optimization and privacy-aware learning. Policy-informed decision support is embedded using reinforcement-based assessment strategies. Learner profiles were generated using probabilistic behavioral models representing cognitive load, engagement dynamics, and modality interaction patterns. Using 1000 synthetic learner profiles across three deployment scenarios, the framework achieved a 26.4% improvement in learning gains, 89.2% time-on-task efficiency, and reduced dropout risk (<4%) over baseline systems. Learning path accuracy surpassed 95% in offline evaluations. The framework was validated through synthetic learners for equitable and adaptive education in resource-constrained settings. Future work focuses on real-world deployment, multilingual accessibility, enhanced multimodal support, and reinforcement-based optimization, with potential applications in healthcare and workforce training.