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Computational Learning Theories

By Gibson, David C.

Computational Learning Theories

By Gibson, David C.

0 - Default Title
Description
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 domains
The book will be of interest to educational professionals, researchers, and developers of educational technology that utilize artificial intelligence.
Product details
Binding:
Paperback
Number of Pages:
168
Release Date:
2025-07-18
Publication Date:
2025-07-18
Publisher:
Springer Nature Switzerland
Languages:
Original: English
ISBN10:
3031659007
ISBN13:
9783031659003
GPSR Manufacturer Reference:
[email protected]
Weight:
265 g
Height:
155 cm
Width:
235 cm
Thickness:
10 cm
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