Computational Learning Theories: Models for Artificial Intelligence Promoting Learning Processes

Gibson, David C.; Ifenthaler, Dirk

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.

The book provides a road map for the development of AI that addresses the central problems of learning theory in the age of artificial intelligence including:

• optimizing human-machine collaboration
• promoting individual learning
• balancing personalization with privacy
• dealing with biases and promoting fairness
• explaining decisions and recommendations to build trust and accountability
• continuously balancing and adapting to individual, team and organizational goals
• generating and generalizing knowledge across fields and domains

The book will be of interest to educational professionals, researchers, and developers of educational technology that utilize artificial intelligence.

Details

Published by: Springer

Publication Date: 2025-07-18

Format: Paperback

ISBN-13: 9783031659003

DOI: 10.1007/978-3-031-65898-3

Dimensions: 235cm x155cm

Pages: 154