Towards Trustworthy and Explainable AI Educational Systems

This study explores the impact of trustworthy and Explainable Artificial Intelligence (XAI) on promoting Trustworthy AI educational systems, outlining its importance, obstacles, and future directions. The introduction provides an overview of the significant role of AI in education and presents an overview of the paper's organization. The study defines XAI in educational contexts and explores its crucial function in clarifying the decision-making processes of AI for both educators and learners. It further explores the fundamental principles necessary for establishing trust in AI, with a focus on transparency, fairness, privacy, and ongoing evaluation as vital elements. The study examines and evaluates obstacles to applying XAI in educational settings, specifically addressing the inherent complications that impede full interpretability and transparency. Moreover, it examines the current patterns and possibilities in XAI in the field of education. It presents prospective avenues for future research and progress in this area. Finally, it summarizes important observations from the discussion, providing thoughtful analysis of the knowledge gained from implementing XAI in education, as well as identifying areas that require additional investigation and improvement. This study provides a thorough analysis that enhances our understanding of the crucial role of XAI in influencing reliable educational AI systems. It identifies the obstacles and outlines the path for future progress in this important field.