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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.
| Autorius: | Dirk Ifenthaler, David C. Gibson, |
| Serija: | Advances in Analytics for Learning and Teaching |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2024 |
| Knygos puslapių skaičius: | 168 |
| ISBN-10: | 3031658973 |
| ISBN-13: | 9783031658976 |
| Formatas: | 241 x 160 x 15 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Computational Learning Theories: Models for Artificial Intelligence Promoting Learning Processes“
