03928nam a22005535i 4500 978-3-031-65898-3 DE-He213 20251030112632.0 cr nn 008mamaa 240716s2024 sz | s |||| 0|eng d 9783031658983 978-3-031-65898-3 10.1007/978-3-031-65898-3 doi LB1028-1028.25 JN bicssc EDU037000 bisacsh JN thema Gibson, David C. author. (orcid)0000-0003-1053-4690 https://orcid.org/0000-0003-1053-4690 aut http://id.loc.gov/vocabulary/relators/aut 80703 Computational Learning Theories [electronic resource] : Models for Artificial Intelligence Promoting Learning Processes / by David C. Gibson, Dirk Ifenthaler. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XIII, 154 p. 20 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Advances in Analytics for Learning and Teaching, 2662-2130 1. Why ‘Computational’ Learning Theories? -- 2. AI and Learning Processes -- 3. A Complex Hierarchical Framework of Learning -- 4. Piaget and the Ontogeny of Intelligence -- 5. Keller and the ARCS Model of Motivation -- 6. Complexity Theory and Learning -- 7. AI Roles for Enhancing Individual Learning -- 8. Informal Social Learning -- 9. How People Learn -- 10. AI Assisting Individuals as Team Members -- 11. AI Roles for the Team or Organization -- 12. A Network Theory of Culture -- 13. AI Roles in Cultural Learning -- 14. Open Questions. 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. Education Research. 59832 Educational technology. 57244 Educational psychology. 57583 Research Methods in Education. 69694 Digital Education and Educational Technology. 68557 Educational Psychology. 57583 Ifenthaler, Dirk. author. aut http://id.loc.gov/vocabulary/relators/aut 73981 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031658976 Printed edition: 9783031658990 Printed edition: 9783031659003 Advances in Analytics for Learning and Teaching, 2662-2130 76488 https://doi.org/10.1007/978-3-031-65898-3 ZDB-2-EDA ZDB-2-SXED 61662 61662