Towards a Flipped Learning Ecosystem: A Generative Artificial Intelligence-Enabled Framework

The potential of generative artificial intelligence (GenAI) to transform education has become a key area of focus, particularly in its integration with pedagogical strategies such as flipped learning. Flipped learning, which encourages students to engage with content prior to class, has been shown to promote deeper learning. However, its implementation often presents challenges. This paper proposes a novel framework that combines flipped learning with GenAI, offering a comprehensive approach that spans micro, meso, and macro levels. At the micro level, the framework focuses on optimizing in-class experiences by leveraging GenAI to facilitate real-time feedback, collaborative learning, and personalized support. On the meso level, it examines how GenAI tools can assist in workload management and facilitate personalized pre-class preparation, ensuring alignment with diverse learning styles and needs. At the macro level, the paper addresses the paucity of theoretical guidance regarding the flipped approach and discusses how the framework can guide curriculum redesign by employing theoretical frameworks such as constructivism and connectivism theory to inform the structure of in-class activities and foster more effective knowledge construction and critical thinking. By addressing systemic challenges at multiple levels, the proposed GenAI-enabled flipped learning framework aims to enhance the overall learning experience, providing a more engaging and efficient approach to flipped classrooms. The paper concludes by suggesting areas for future research and calling for empirical studies to assess the impact of this integrated approach across various educational contexts.