Optimizing blended learning through AI-powered analytics in digital education platforms

This study proposes an empirical framework for enhancing blended learning through Artificial Intelligence (AI)-powered analytics in digital education platforms. The research employs a mixed-methods approach, examining 250 undergraduate business students engaged in blended learning courses over one semester. Quantitative data from platform analytics, academic performance metrics, and structured questionnaires are analyzed using descriptive statistics, regression analysis, and machine learning algorithms. Results demonstrate significant improvements in learning outcomes, with overall academic performance increasing from 72.4% to 81.7% (p < 0.001). Critical thinking skills improve by 24.3%, collaborative abilities by 31.2%, and digital literacy by 28.7%. Cluster analysis reveals three distinct learner profiles, with engagement patterns serving as strong predictors of academic success (R² = 0.584). AI-powered predictive models achieve 83.7% accuracy in identifying at-risk students by week four, enabling targeted interventions that improve outcomes by 67%. Platform engagement frequency emerges as the strongest predictor (β = 0.42, p < 0.001). Critical engagement periods occur during weeks 3-5 and 10-12. The framework integrates multiple learning theories within AI-enhanced contexts and provides practical guidance for platform optimization, instructional design, and policy development. Findings emphasize that successful blended learning requires purposeful technology integration with pedagogical principles, continuous engagement monitoring, and personalized support mechanisms.