Computational Learning Theories : Models for Artificial Intelligence Promoting Learning Processes

✍ Scribed by David C. Gibson; Dirk Ifenthaler

• Publisher: Springer Nature Switzerland
• Year: 2024
• Tongue: English
• Leaves: 278
• Category: Library

✦ Synopsis

This book shows how artificial intelligence grounded in learning theories can promote individual learning, team productivity and multidisciplinary knowledge-building. It advances the learning sciences by integrating learning theory with computational biology and complexity, offering an updated mechanism of learning, which integrates previous theories, provides a basis for scaling from individuals to societies, and unifies models of psychology, sociology and cultural studies.

✦ Table of Contents

Cover
Front Matter
1. Why ‘Computational’ Learning Theories?
2. AI and Learning Processes
3. A Complex Hierarchical Framework of Learning
4. Piaget and the Ontogeny of Intelligence
5. Keller and the ARCS Model of Motivation
6. Complexity Theory and Learning
7. AI Roles for Enhancing Individual Learning
8. Informal Social Learning
9. How People Learn
10. AI Assisting Individuals as Team Members
11. AI Roles for the Team or Organization
12. A Network Theory of Culture
13. AI Roles in Cultural Learning
14. Open Questions