From Transparency to Trust: A Literature Review on Explainable AI in Educational Systems

Abstract

As artificial intelligence (AI) systems increasingly shape educational experiences, the demand for transparency and trust in algorithmic decision-making has led to a growing interest in Explainable AI (XAI). This literature review examines how XAI is being implemented in educational contexts across different countries, highlighting both its pedagogical promises and ethical tensions. Drawing from real-world applications and student reflections, the study explores how explainability enhances feedback systems, supports learner autonomy, and fosters instructional alignment. However, it also identifies persistent challenges, including cognitive over-reliance, educator deskilling, and the illusion of fairness. Through a synthesis of global case studies, this review offers a comprehensive analysis of design considerations, policy frameworks, and user-centered practices for XAI in education. The study concludes with a detailed checklist for ethical deployment and a set of forward-looking recommendations aimed at ensuring that XAI contributes meaningfully to equitable, human-centered learning environments.

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