Explainable Artificial Intelligence in Education: A Comprehensive Review

Explainable artificial intelligence (AI) has drawn a lot of attention recently since AI systems are being employed more often across a variety of industries, including education. Building trust and increasing the efficacy of AI systems in educational settings requires the capacity to explain how they make decisions. This article provides a comprehensive review of the current level of explainable AI (XAI) research and its application to education. We begin with the challenges of XAI in education, the complexity of AI algorithms, and the necessity for transparency and interpretability. Furthermore, we discuss the obstacles involved with using AI in education, and explore several solutions, including human-AI collaboration, explainability techniques, and ethical and legal frameworks. Subsequently, we debate about the importance of developing new competencies and skills among students and educators to interact with AI effectively, as well as how XAI impacts politics and government. Finally, we provide recommendations for additional research in this field and suggest potential future directions for XAI in educational research and practice.