Computational Learning Theories: Models for Artificial Intelligence Promoting Learning Processes (2024) (Advances in Analytics for Learning and Teaching)

By: David C. Gibson and Dirk Ifenthaler

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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. 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 collaborationpromoting individual learningbalancing personalization with privacydealing with biases and promoting fairnessexplaining decisions and recommendations to build trust and accountabilitycontinuously balancing and adapting to individual, team and organizational goalsgenerating and generalizing knowledge across fields and domainsThe book will be of interest to educational professionals, researchers, and developers of educational technology that utilize artificial intelligence.