Digital Inclusion Through Explainable AI: Transforming Educational Support for Diverse Learners

Abstract

The integration of Artificial Intelligence (AI) in educational contexts has raised critical questions about accessibility, transparency, and equity. Explainable AI (XAI) offers a promising framework for addressing these concerns by making algorithmic decision-making processes interpretable, particularly for diverse learners including students with disabilities, English language learners (ELLs), and those from underrepresented socioeconomic backgrounds. This paper presents a comprehensive review of XAI applications in K–12 and higher education, examines existing barriers to digital inclusion, and proposes a conceptual framework — the Inclusive Explainable AI in Education (IXAIED) framework — for deploying XAI-based educational tools that prioritize transparency, usability, and equity. Findings indicate that XAI not only enhances learner trust and agency but also enables educators and administrators to identify and mitigate algorithmic biases that disproportionately affect marginalized populations. Implications for policy, pedagogy, and future research are discussed.