LEADER 03631nam 22005775 450 001 9910874683003321 005 20240917005228.0 010 $a9783031658983 024 7 $a10.1007/978-3-031-65898-3 035 $a(CKB)32970587900041 035 $a(MiAaPQ)EBC31531377 035 $a(Au-PeEL)EBL31531377 035 $a(DE-He213)978-3-031-65898-3 035 $a(EXLCZ)9932970587900041 100 $a20240716d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Learning Theories $eModels for Artificial Intelligence Promoting Learning Processes /$fby David C. Gibson, Dirk Ifenthaler 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (164 pages) 225 1 $aAdvances in Analytics for Learning and Teaching,$x2662-2130 311 08$a9783031658976 320 $aIncludes bibliographical references. 327 $a1. 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. 330 $aThis book shows how artificial intelligence grounded in learning theories can promote individual learning, team productivity and multidisciplinary knowledge-building. It advances the learning sciences by integrating learning theory with computational biology and complexity, offering an updated mechanism of learning, which integrates previous theories, provides a basis for scaling from individuals to societies, and unifies models of psychology, sociology and cultural studies. The book provides a road map for the development of AI that addresses the central problems of learning theory in the age of artificial intelligence including: optimizing human-machine collaboration promoting individual learning balancing personalization with privacy dealing with biases and promoting fairness explaining decisions and recommendations to build trust and accountability continuously balancing and adapting to individual, team and organizational goals generating and generalizing knowledge across fields and domains The book will be of interest to educational professionals, researchers, and developers of educational technology that utilize artificial intelligence. 410 0$aAdvances in Analytics for Learning and Teaching,$x2662-2130 606 $aEducation$xResearch 606 $aEducational technology 606 $aEducational psychology 606 $aResearch Methods in Education 606 $aDigital Education and Educational Technology 606 $aEducational Psychology 615 0$aEducation$xResearch. 615 0$aEducational technology. 615 0$aEducational psychology. 615 14$aResearch Methods in Education. 615 24$aDigital Education and Educational Technology. 615 24$aEducational Psychology. 676 $a006.3 700 $aGibson$b David C.$0144859 702 $aIfenthaler$b Dirk 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910874683003321 996 $aComputational Learning Theories$94236939 997 $aUNINA