Keywords
Personalized Learning
Virtual Learning Environments
Student Engagement
Learner Agency
Self-Regulated Learning
Human-Centered Pedagogy
Intelligent Personalization
How to Cite
Abstract
Background: Personalized learning using VLEs has evolved from pre-designed customizations of content, pace, and learning paths to smart, predictive, real-time personalization powered by learning analytics, adaptive techniques, conversational agents, and generative AI. However, technology adoption does not always translate into meaningful or sustained engagement.
Objective: This paper investigates the relationship between personalized learning, AI-enabled VLEs, learner agency, self-regulated learning, human-centred pedagogy, and multidimensional student engagement, and proposes an integrated conceptual framework for human-centred intelligent personalization.
Methods: A structured critical and thematic review approach was used in conjunction with the development of a conceptual framework. Relevant literature on personalized learning, VLEs, AI in education, learning analytics, adaptive learning, student engagement, learner agency, self-regulated learning, pedagogical functions, and the ethical and inclusive dimensions of intelligent educational technologies was conceptually tabulated. Common themes and relationships were identified and clustered thematically.
Results: Five interrelated categories emerged from the synthesis: technological intelligence; learner agency and self-regulation; human-centred pedagogy; multidimensional student engagement behaviourally, cognitively, emotionally, and agentically; and ethical and sustainable foundations. These categories informed the Human-Centered Intelligent Engagement Framework (HCIEF), which conceptualizes intelligent personalization as a human-technology-pedagogy ecosystem rather than a linear technological process. The review also revealed a technology-agency dilemma: automation can increase system responsiveness at the cost of possibly reducing learners’ ability to choose, reflect and self-regulate.
Conclusion: HCIEF poses meaningful intelligent personalization as the coalescence of technical, human, pedagogical, and situational factors. It is technically challenging and also requires learner agency, pedagogical judgement, educational participation, multidimensional engagement, as well as ethical, inclusive, and sustainable contextual frames. The framework offers a theoretically based foundation for the design, evaluation, and empirical investigation of next generation personalized VLEs.
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