This book offers an in-depth exploration of explainable learner models, presenting theoretical foundations and practical applications in the context of educational AI. It aims to provide readers with a comprehensive understanding of how these models can enhance adaptive learning systems.
Chapters cover a wide range of topics, including the development and optimization of explainable learner models, the integration of these models into adaptive learning systems, and their implications for educational equity. It also discusses the latest advancements in AI explainability techniques, such as pre-hoc and post-hoc explainability, and their application in intelligent tutoring systems. Lastly, the book provides practical examples and case studies to illustrate how explainable learner models can be implemented in real-world educational settings.
This book is an essential resource for researchers, educators, and practitioners interested in the intersection of AI and education. It offers valuable insights for those looking to integrate explainable AI into their educational practices, as well as for policymakers focused on promoting equitable and transparent learning environments.
This book offers an in-depth exploration of explainable learner models, presenting theoretical foundations and practical applications in the context of educational AI. It aims to provide readers with a comprehensive understanding of how these models can enhance adaptive learning systems.
Chapters cover a wide range of topics, including the development and optimization of explainable learner models, the integration of these models into adaptive learning systems, and their implications for educational equity. It also discusses the latest advancements in AI explainability techniques, such as pre-hoc and post-hoc explainability, and their application in intelligent tutoring systems. Lastly, the book provides practical examples and case studies to illustrate how explainable learner models can be implemented in real-world educational settings.
This book is an essential resource for researchers, educators, and practitioners interested in the intersection of AI and education. It offers valuable insights for those looking to integrate explainable AI into their educational practices, as well as for policymakers focused on promoting equitable and transparent learning environments.
This book offers an in-depth exploration of explainable learner models, presenting theoretical foundations and practical applications in the context of educational AI. It aims to provide readers with...
This present book provides valuable insights on the technical, societal and legal challenges posed by the use of artificial intelligent systems in a plethora of different applications, from embodied...
This book constitutes the proceedings of First International Workshop, TML4H 2023, held virtually, in May 2023.The 16 full papers included in this volume were carefully reviewed and selected from 30...
Acknowledgements PART 1: THEORETICAL PERSPECTIVES: NEW PARADIGMS OF WORK AND LEARNING 1 Introduction Marja-Leena Stenström & Päivi Tynjälä 2 Connectivity and Transformation in Work-Related...
Electoral management bodies (EMBs), the institutions designated to manage political change inclusively and peacefully, must be trusted for election results to be accepted. Trustworthy Elections: The...
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