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Quantum Learning: Bridging Artificial Intelligence, Quantum Computing, and Data Science in Education

Editat de Pawan Whig, Pavika Sharma, Ahmad A. Elngar, Nuno Silva
en Limba Engleză Hardback – 8 mai 2026
Quantum Learning: Bridging Artificial Intelligence, Quantum Computing, and Data Science in Education explores the transformative intersection of three revolutionary technologies reshaping the future of learning. The concept of Quantum Learning provides a paradigm where quantum principles redefine machine learning models, enhance computational speed, and enable novel personalized education systems.
 
The book integrates AI, quantum algorithms, and data-driven pedagogy to reimagine classrooms and cognitive processes. Readers will discover how quantum-inspired neural networks, quantum data analysis, and intelligent tutoring systems can revolutionize educational delivery. Through interdisciplinary research, the work translates complex quantum and AI concepts into practical educational applications, featuring case studies and real-world insights that demonstrate how quantum-enhanced intelligence can personalize learning and improve outcomes. The text covers both theoretical frameworks and practical implementation strategies, offering a blueprint for adaptive, scalable learning ecosystems.
 
This wide-ranging book will appeal to a diverse audience of researchers, educators, technologists, and policymakers seeking to understand and shape the next generation of education innovation. By combining these domains into one book and using an accessible approach, it makes cutting-edge concepts comprehensible to both technical and non-technical readers, positioning it as an essential resource for anyone involved in educational technology, artificial intelligence research, or quantum computing applications in learning environments.
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Specificații

ISBN-13: 9781041037798
ISBN-10: 1041037791
Pagini: 216
Ilustrații: 82
Dimensiuni: 210 x 280 mm
Ediția:1
Editura: CRC Press
Colecția CRC Press

Public țintă

Academic, Postgraduate, and Professional Practice & Development

Cuprins

Chapter 1: Introduction1.1 The Evolution of Educational Technology1.2 Defining Quantum Learning1.3 The Need for a New Paradigm in Education1.4 Overview of Key Technologies: AI, Quantum Computing, and Data Science1.5 How to Use This BookPart I: Quantum Computing in EducationChapter 2: Understanding Quantum Computing2.1 The Basics of Quantum Computing: Qubits, Superposition, and Entanglement2.2 Classical vs. Quantum Computing: A Comparative Overview2.3 Potential Applications of Quantum Computing in Education2.4 Quantum Algorithms for Educational Data Processing2.5 Challenges and Opportunities in Quantum EducationChapter 3: Quantum Simulations in the Classroom3.1 Enhancing Science Education with Quantum Simulations3.2 Quantum Modeling for Complex Problem Solving3.3 Case Studies: Quantum Computing Projects in Schools and UniversitiesChapter 4: Preparing for the Quantum Future4.1 Curriculum Development for Quantum Computing Education4.2 Integrating Quantum Concepts into K-12 and Higher Education4.3 Teacher Training and Professional Development4.4 Quantum Literacy: Preparing Students for the Quantum AgePart II: Artificial Intelligence in EducationChapter 5: AI-Powered Personalized Learning5.1 The Role of AI in Personalizing Education5.2 Adaptive Learning Systems: Tailoring Education to Individual Needs5.3 AI in Learning Analytics: Monitoring and Enhancing Student Performance5.4 Implementing AI in Diverse Educational SettingsChapter 6: Intelligent Tutoring Systems and Virtual Classrooms6.1 The Rise of Intelligent Tutoring Systems6.2 AI in Virtual and Augmented Reality for Education6.3 Enhancing Student Engagement through AI-Powered Interactions6.4 Case Studies: AI-Driven Virtual Classrooms in PracticeChapter 7: Automation and Efficiency in Education7.1 Automating Administrative Tasks with AI7.2 AI in Grading and Assessment: Reducing Educator Workload7.3 Ethical Considerations in AI-Driven Educational Decisions7.4 The Future of AI in Educational ManagementPart III: Data Science and Educational TransformationChapter 8: Data-Driven Decision Making in Education8.1 The Power of Data Science in Education8.2 Collecting and Analyzing Educational Data8.3 Predictive Analytics: Identifying At-Risk Students8.4 Data-Driven Curriculum Design and EvaluationChapter 9: Enhancing Institutional Efficiency with Data Science9.1 Resource Optimization through Data Insights9.2 Data Science in Student Support Services9.3 Case Studies: Data-Driven Improvements in Educational Institutions9.4 Challenges in Implementing Data-Driven ApproachesChapter 10: Ethical Data Practices in Education10.1 Privacy Concerns and Data Security in Education10.2 Addressing Bias in Data-Driven Educational Tools10.3 Ensuring Transparency and Accountability in Educational Data Use10.4 Developing Ethical Guidelines for Data Science in EducationPart IV: Interdisciplinary Approaches and Future DirectionsChapter 11: Designing Interdisciplinary Curricula11.1 Integrating Quantum Computing, AI, and Data Science in Education11.2 Fostering Interdisciplinary Skills for the Future Workforce11.3 Collaborative Learning Platforms and Interdisciplinary Projects11.4 Preparing Students for Emerging Career PathsChapter 12: Case Studies and Practical Applications12.1 Real-World Examples of Quantum Learning in Action12.2 Success Stories from Educational Institutions12.3 Lessons Learned: Challenges and Triumphs12.4 Insights for Future ImplementationChapter 13: Ethical Considerations and the Role of Educators13.1 Navigating the Ethical Landscape of Emerging Technologies13.2 The Educator’s Role in Shaping Ethical Tech Use13.3 Preparing Students for Responsible Technology Use13.4 Building an Ethical Framework for Quantum LearningChapter 14: The Future of Education in the Quantum Age14.1 Visioning the Future: Quantum Learning in 2030 and Beyond14.2 Emerging Trends and Technologies14.3 Global Impacts of Quantum Learning14.4 Preparing for Continuous Change in EducationConclusion15.1 Summarizing the Quantum Learning Framework15.2 The Road Ahead: Continuous Innovation in Education15.3 Final Thoughts and Call to ActionAppendix

Notă biografică

Pawan Whig, a leading expert in artificial intelligence and machine learning, is dedicated to advancing sustainable development. Dr. Whig's groundbreaking research and publications inspire innovative solutions for a greener, more equitable future.
Pavika Sharma is a distinguished researcher in artificial intelligence and machine learning, focusing on sustainable development. Her innovative work explores the intersection of technology and environmental conservation, driving progress towards a sustainable future.
Ahmad A. Elngar is an Associate Professor and Head of the Computer Science Department at Beni-Suef University. His research focuses on artificial intelligence, machine learning, and data science, with a strong emphasis on developing innovative computational models for education and intelligent systems.
Nuno Silva is the Chief Scientific and Technology Officer at UnifAI Technology, leading innovation at the intersection of artificial intelligence, quantum computing, and data science. His work focuses on advancing intelligent systems that enhance learning, decision-making, and sustainable technological transformation.

Descriere

Quantum Learning: Bridging Artificial Intelligence, Quantum Computing, and Data Science in Education explores the transformative intersection of three of the most powerful technologies shaping the future of learning.