Beyond the Black Box

Artificial Intelligence is rapidly reshaping the educational landscape, transforming how institutions understand learners, predict academic outcomes, and deliver personalized learning experiences. From performance forecasting to early identification of at-risk students, AI-driven learning analytics now play a pivotal role in data-informed educational decision-making. Yet, alongside these advancements lies a critical challenge: the widespread use of opaque "black-box" models that compromise transparency, fairness, and ethical accountability. This book offers a timely and in-depth exploration of Explainable Artificial Intelligence (XAI) and Interpretable Machine Learning (IML) as essential foundations for building trustworthy AI systems in education. It bridges the gap between technical innovation and ethical responsibility by demonstrating how techniques such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) can reveal the reasoning behind model predictions. By making AI decisions understandable, educators and administrators are empowered to build trust, validate outcomes, and make informed, learner-centric decisions. Beyond methodology, the book critically examines pressing ethical concerns, including data privacy, algorithmic bias, and responsible data governance. Through real-world case studies and practical applications, it underscores the necessity of aligning AI systems with educational values, transparency standards, and robust policy frameworks. Designed for researchers, educators, and policymakers alike, this work advocates for AI in education that is not only powerful and accurate, but also explainable, ethical, and human-centered.