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Abstract

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.

Keywords

Blended learning AI-powered analytics Digital education platforms Learning effectiveness Predictive modelling Student engagement

Article Details

Author Biographies

Luh Putu Artini, Universitas Pendidikan Ganesha, Jl. Udayana No.11, Banjar Tegal, Singaraja, Kabupaten Buleleng, Bali 81116, Indonesia

Luh Putu Artini pursued her undergraduate education at the Faculty of Teacher Training and Education (FKIP) of Udayana University, majoring in English Language Education (1982-1986); her master's degree in 'Applied Linguistics' at La Trobe University, Australia (1992-1994); and her doctoral degree in English Language Education at Newcastle University, Australia (2002-2006). Her academic and research focus is English language pedagogy, which includes curriculum, methods, and strategies, and materials development.

Dessy Seri Wahyuni, Universitas Pendidikan Ganesha, Jl. Udayana No.11, Banjar Tegal, Singaraja, Kabupaten Buleleng, Bali 81116, Indonesia

Dessy Seri Wahyuni teaches at the Informatics Engineering Study Program, Engineering and Vocational Faculty, Universitas Pendidikan Ganesha, Bali, Indonesia. Her main duties are teaching, researching, and providing community services. Dessy accomplished her Doctoral at Vocational Education Program via Joint Degree Program in Yogyakarta State University and Technische Universität Dresden Germany. She teaches in the undergraduate and postgraduate programs in the university, in which several of the courses is in related with Praxis Instructional Design and Strategy in Vocational High School, Link and Match Curriculum Design in Vocational High School and The Learner Development by Cognitive and Practical Skills. The courses reflect her expertise as well as her research areas of interest. She supervised students research in TUD Germany and Universitas Pendidikan Ganesha.

How to Cite
Zhang, F., Wayan Subagia, I., Putu Artini, L., & Seri Wahyuni, D. (2025). Optimizing blended learning through AI-powered analytics in digital education platforms: an empirical framework. Future Technology, 4(4), 173–184. Retrieved from https://fupubco.com/futech/article/view/481
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