Designing Intelligent Learning Ecosystems: The Role of Artificial Intelligence and Blended Learning in Enhancing Digital Education Quality
Keywords:
intelligent learning ecosystems, artificial intelligence, blended learning, digital education quality, e-learning, higher educationAbstract
This study presents a model for designing intelligent learning ecosystems that enhance the quality of digital education through the integration of artificial intelligence and blended learning, with Islamic Azad University as the empirical context. The research addresses persistent challenges in e-learning, including limited interaction, unequal access, and the need to respond to diverse learner profiles through technology-enhanced educational design. A mixed-methods approach was employed. In the qualitative phase, semi-structured interviews were conducted with 15 experts in education and educational technology selected through purposive sampling. In the quantitative phase, data were collected from 384 faculty members and university staff using stratified random sampling across regions and academic fields. Qualitative data were analyzed through thematic analysis, while quantitative data were examined using Partial Least Squares Structural Equation Modeling (PLS-SEM), Artificial Neural Networks (ANN), and the MABAC multi-criteria decision-making method. Findings revealed that the proposed ecosystem is built around three core dimensions: blended learning, artificial intelligence capabilities, and digital education quality. Blended learning was defined through flexibility, interaction, personalization, and infrastructure, while AI capabilities included educational data analysis, intelligent recommendation, intelligent support, and automated assessment. The quality of digital education was reflected in learner satisfaction, learning effectiveness, and educational interaction. The model demonstrated strong explanatory power (R² = 0.712). ANN results identified learner satisfaction and learning effectiveness as the most influential indicators, and MABAC ranked intelligent support as the highest-priority AI capability. The study concludes that integrating AI-driven support into blended learning environments can provide a practical pathway for strengthening digital education quality and informing future policy and implementation in higher education.
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