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Computational Learning Theories
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398 b
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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.
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