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Understanding Student Engagement in AI-Powered Online Learning Platforms: A Narrative Review of Key Theories and Models
Author(s): Manuel B. Garcia (FEU Institute of Technology, Philippines), Chai Lee Goi (Curtin University, Sarawak, Malaysia), Kate Shively (Ball State University, USA), Damian Maher (University of Technology Sydney, Australia), Joanna Rosak-Szyrocka (Czestochowa University of Technology, Poland), Ari Happonen (LUT University, Finland), Aras Bozkurt (Anadolu University, Turkey)and Robertas Damaševičius (Vytautas Magnus University, Lithuania)
Copyright: 2025
Pages: 30
EISBN13: 9798337319223
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Abstract
Online learning has become fundamental to modern academic and professional development. Amidst its widespread adoption, there is increasing integration of artificial intelligence (AI) to enhance the learning experience. Understanding student engagement within these AI-powered digital platforms is crucial, as it directly influences learning outcomes and satisfaction. This chapter provides a narrative review of key theories and models essential for analyzing engagement in virtual learning contexts. Particularly, it focuses on constructivist learning theory, social learning theory, cognitive load theory, flow theory, technology acceptance model, self-determination theory, cognitive theory of multimedia learning, and feedback intervention theory. By examining these frameworks through an epistemological lens, the chapter explores how knowledge acquisition, cognitive processing, and social learning principles interact within AI-enhanced educational contexts. The insights reported here can serve as a guide for optimizing AI to maximize student involvement and educational efficacy.
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