Investigating Explainable Artificial Intelligence to Enhance Equity and Inclusivity in Adaptive Learning: Empirical Insights from Under-Resourced Environments

This study investigates the integration of Explainable Artificial Intelligence (XAI) intoadaptive learning systems, drawing on Human–AI Complementarity Theory and EpistemicJustice Theory to examine how explainability supports equity and inclusivity in resourceconstrainedsettings. Using a mixed-methods design, the study combined pre- and post-test datawith qualitative insights to assess learner confidence in the system, engagement, andperformance. Findings show that learners using XAI-supported systems achieved significantlyhigher post-test scores (F₁,₁₁₁ = 24.78, p < .001, η2 = .08) and reported greater confidencein the system due to clearer, contextually relevant explanations. The results indicate that XAIreduces confusion, strengthens fairness perceptions, and enhances learner autonomy. The studyconcludes that explainability is essential for equitable AI-mediated learning, particularly whereresource constraints and diverse learner needs amplify the risks of opaque systems. Itrecommends the adoption of XAI-enhanced platforms in resource-constrained settings toimprove transparency, trust, and academic achievement.