Artificial Intelligence is rapidly reshaping the educational landscape, transforming how institutions understand learners, predict academic outcomes, and deliver personalized learning experiences. From performance forecasting to early identification of at-risk students, AI-driven learning analytics now play a pivotal role in data-informed educational decision-making. Yet, alongside these advancements lies a critical challenge: the widespread use of opaque “black-box” models that compromise transparency, fairness, and ethical accountability. This book offers a timely and in-depth exploration of Explainable Artificial Intelligence (XAI) and Interpretable Machine Learning (IML) as essential foundations for building trustworthy AI systems in education. It bridges the gap between technical innovation and ethical responsibility by demonstrating how techniques such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) can reveal the reasoning behind model predictions. By making AI decisions understandable, educators and administrators are empowered to build trust, validate outcomes, and make informed, learner-centric decisions. Beyond methodology, the book critically examines pressing ethical concerns, including data privacy, algorithmic bias, and responsible data governance. Through real-world case studies and practical applications, it underscores the necessity of aligning AI systems with educational values, transparency standards, and robust policy frameworks. Designed for researchers, educators, and policymakers alike, this work advocates for AI in education that is not only powerful and accurate, but also explainable, ethical, and human-centered.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Anbieter: California Books, Miami, FL, USA
Zustand: New. Bestandsnummer des Verkäufers I-9789999336086
Anzahl: Mehr als 20 verfügbar
Anbieter: PBShop.store US, Wood Dale, IL, USA
PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Bestandsnummer des Verkäufers L0-9789999336086
Anzahl: Mehr als 20 verfügbar
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich
PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Bestandsnummer des Verkäufers L0-9789999336086
Anzahl: Mehr als 20 verfügbar
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
Paperback. Zustand: Brand New. 90 pages. 6.00x0.21x9.00 inches. In Stock. Bestandsnummer des Verkäufers x-9999336086
Anzahl: 2 verfügbar
Anbieter: Books Puddle, New York, NY, USA
Zustand: New. Bestandsnummer des Verkäufers 26406554035
Anzahl: 4 verfügbar
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
Zustand: New. Print on Demand. Bestandsnummer des Verkäufers 407648876
Anzahl: 4 verfügbar
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
Zustand: New. PRINT ON DEMAND. Bestandsnummer des Verkäufers 18406554041
Anzahl: 4 verfügbar
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Artificial Intelligence is rapidly reshaping the educational landscape, transforming how institutions understand learners, predict academic outcomes, and deliver personalized learning experiences. From performance forecasting to early identification of at-risk students, AI-driven learning analytics now play a pivotal role in data-informed educational decision-making. Yet, alongside these advancements lies a critical challenge: the widespread use of opaque 'black-box' models that compromise transparency, fairness, and ethical accountability.This book offers a timely and in-depth exploration of Explainable Artificial Intelligence (XAI) and Interpretable Machine Learning (IML) as essential foundations for building trustworthy AI systems in education. It bridges the gap between technical innovation and ethical responsibility by demonstrating how techniques such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) can reveal the reasoning behind model predictions. By making AI decisions understandable, educators and administrators are empowered to build trust, validate outcomes, and make informed, learner-centric decisions.Beyond methodology, the book critically examines pressing ethical concerns, including data privacy, algorithmic bias, and responsible data governance. Through real-world case studies and practical applications, it underscores the necessity of aligning AI systems with educational values, transparency standards, and robust policy frameworks. Designed for researchers, educators, and policymakers alike, this work advocates for AI in education that is not only powerful and accurate, but also explainable, ethical, and human-centered. Bestandsnummer des Verkäufers 9789999336086
Anzahl: 2 verfügbar
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Beyond the Black Box | Explainable and Ethical AI for Trustworthy Learning Analytics in Education: Interpretable ML, XAI Techniques & Responsible Decision- Making in Educational Systems | Rahul Jain | Taschenbuch | Englisch | 2026 | Eliva Press | EAN 9789999336086 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Bestandsnummer des Verkäufers 135116662
Anzahl: 5 verfügbar