Explainable Artificial Intelligence and Interpretable Machine Learning in Education : A Researcher’s Guide to Data Science

Explainable AI (XAI) and interpretable machine learning are becoming essential in educational research, offering transparency and practical insight. This book provides a comprehensive guide to applying these technologies to analyze data, assess learning, and design adaptive environments while maintaining ethical and interpretable AI use.

In a rapidly evolving landscape of educational research, explainable artificial intelligence (XAI) and interpretable machine learning (IML) are emerging as pivotal tools that enhance transparency, efficiency, and innovation. This book serves as a comprehensive guide to understanding and leveraging these technologies to transform teaching, learning, and research practices. It aims to bridge the gap between complex technological advancements and practical educational applications. It delves into how XAI and IML can be harnessed to analyze vast educational datasets, assess student performance, and design adaptive learning environments, all while ensuring the interpretability and ethical deployment of AI systems. Through a blend of theoretical insights and real-world examples, the book explores topics such as the foundations of XAI, the development of IML algorithms for education, and the ethical implications of data-driven decision-making. A unique feature of this volume is its interdisciplinary approach, combining perspectives from educators, researchers, and data scientists. It emphasizes collaboration and encourages contributors to address emerging trends, challenges, and opportunities in the application of XAI and IML. Case studies from diverse educational contexts provide practical insights and inspire innovative solutions to pressing educational issues. The book serves as a comprehensive and definitive guide for practitioners and researchers dedicated to enhancing educational processes.