Orchestrating Optimal Blended Learning in Theoreticalcourses: A Framework for Enriching Education

Blended learning, which integrates traditional classroom teaching with online components, has dramatically transformed education by increasing flexibility and improving pedagogical outcomes. This paper focuses on optimizing blended learning in theoretical courses, particularly operating systems (OS). As part of a broader initiative to enhance teaching and learning and improve Desire2Learn (D2L) accessibility using AI tools, we propose a flexible and readily implementable framework for instructors. The framework addresses three key dimensions of learning-content, context, and community-while promoting active online engagement to guide educators in fostering better student interactions. Its effectiveness was demonstrated using synthetic data across three scenarios. A comprehensive feasibility analysis reveals the framework's potential to identify gaps, address challenges, and improve outcomes by integrating various online learning strategies and AI tools. By doing so, it provides a more tailored, student-centered approach to blended learning, adapting to diverse learning needs. Additionally, the framework leverages AI to streamline course delivery, offering personalized learning paths and improving student performance metrics. The framework has important implications for educators and institutions by systematically addressing these challenges effectively. It offers a scalable and adaptable solution that not only improves engagement but also enhances the overall learning experience in both online and blended environments. Ultimately, this framework can play a crucial role in shaping the future of education by making digital learning more accessible and effective for all students.