Explainable Artificial Intelligence and Interpretable Machine Learning in Educat

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Explainable Artificial Intelligence and Interpretable Machine Learning in Education by Myint Swe Khine

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.

FORMAT
Hardcover CONDITION
Brand New Publisher Description

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.

Table of Contents

Preface. PART I: INTRODUCTION. 1. Explaining Explainability in Education Integrating Data Science, Interpretation, and Human Understanding. PART II: CONCEPTUAL AND HUMAN-CENTERED FOUNDATIONS OF EXPLAINABLE AI IN EDUCATION. 2. C-XplainEd: A Conceptual Framework for Trustworthy XAI Educational Applications. 3. The Relation between Fairness and Explainability in Predictive Modeling of Student Performance: A Study on the OULAD. 4. Human-Centered Explainable AI in Education: Opportunities and Challenges of Large Language Models. 5. When the Model Won't Explain Itself: EPICC as a Framework for Human-Centered Explainability in Educational AI Use. 6. Human-Centred Approaches for Non-Expert Users in Explainable AI. 7. Evaluating Explainability in Educational AI: A Dual-Perspective Framework with Case Application. PART III: APPLIED AND COMPUTATIONAL INNOVATIONS IN EDUCATIONAL XAI. 8. A Framework for Explainable AI in Automated Grading Systems in Engineering Education. 9. Explaining Grit: Leveraging XAI on Sentiment Analysis of Student-Generated Text. 10. From Local Explanations to Collective Explanations: An XAI Approach Using LIME and Clustering in Education. 11. Beyond the Black Box: XAI Techniques to Interpret Complex Machine Learning Models. 12. A Knowledge-based Neural Network to Interpret Mars Habitat Building Assessment in Minecraft.

Details ISBN1041149018 Author Myint Swe Khine Publisher Taylor & Francis Ltd Year 2026 ISBN-13 9781041149019 Format Hardcover Imprint CRC Press Subtitle A Researcher's Guide to Data Science Place of Publication London Country of Publication United Kingdom Edited by Myint Swe Khine Illustrations 26 Tables, black and white; 1 Line drawings, color; 43 Line drawings, black and white; 4 Halftones, color; 5 Halftones, black and white; 5 Illustrations, color; 48 Illustrations, black and white Audience Tertiary & Higher Education Country of Origin GB Product Class Description Computing: Professional & Programming ISBN-10 1041149018 Pages 250 DEWEY 371.33463 Publication Date 2026-08-17 UK Release Date 2026-08-18
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