Systems for recommending courses are essential for pointing students in the direction of appropriate academic paths. But conventional recommendation models frequently act as opaque black boxes, which reduces interpretability and user confidence. Explainable AI (XAI) tackles this issue by offering recommendations that are clear and easy to grasp, allowing teachers and students to comprehend the rationale behind suggested courses. This study examines how XAI might be included into course recommendation systems, classifying various explanation strategies and their contribution to increased system transparency. The study also emphasizes the primary benefits of XAI, such as enhanced user trust, equity, and customized learning pathways. Notwithstanding these benefits, a number of obstacles prevent XAI from being widely used. Improvements in interactive explanations, adaptive explainability strategies, and fairness-aware AI models are necessary to meet these problems. This study fosters ethical and successful AI-driven education by offering a thorough examination of XAI in course recommendation systems. This helps to design more dependable, transparent, and student-centric recommendation frameworks.