Context-aware explainable adaptive learning framework for large classroom management in resource-constrained African universities

Rising enrolment in higher-education institutions across developing countries has intensified persistent challenges in classroom management, learner engagement, and personalized instruction. Most existing adaptive learning systems are designed for stable, well-resourced environments, rendering them largely ineffective in resource-constrained African universities. This study proposes a Context-Aware Explainable Adaptive Learning Framework (CAELF) developed through design-based conceptual research. The framework synthesizes adaptive learning theory, explainable artificial intelligence (XAI) principles, context-aware computing, and human-centred instructional design into a six-layer architecture. Its defining feature is the treatment of environmental variables such as internet instability, limited device access, and participation irregularities as core inputs to adaptive decision-making. Embedded explainability mechanisms provide learners and lecturers with transparent, actionable reasoning behind system outputs, strengthening pedagogical trust and accountability. The study contributes to educational technology scholarship by unifying contextual responsiveness, transparency, scalability, and human-centred oversight within a single model tailored to large, under-resourced African classroom environments.