AI-Enhanced Computer-Based Flipped Classroom: Toward Personalized, Interactive, and Scalable Learning

The flipped classroom has gained prominence in higher education as a model that shifts learning from passive lectures to active, student-centered engagement. By moving lectures and foundational content outside the classroom, valuable in-class time can be devoted to collaboration, discussion, and problem-solving. The model enhances critical thinking, teamwork, and deeper understanding. Recent advances in artificial intelligence (AI) have opened new opportunities to enrich this approach. AI tools such as adaptive learning platforms, chatbots, generative assistants, and data analytics can extend the flipped model by providing personalized learning pathways and immediate feedback. Emerging research between 2021 and 2025 highlights these benefits: increased motivation, greater learner autonomy, stronger preparedness for class, and improved skills such as speaking and collaboration. At the same time, challenges remain. Barriers include limited access to reliable technology, ethical concerns about data privacy and bias, the steep learning curve for teachers, and the risk of AI tools being used without clear alignment to pedagogical goals. This paper synthesizes current findings on AI-enhanced flipped learning, outlines the benefits and limitations of this evolving model, and proposes a refined framework for implementation. It concludes with recommendations for practice and directions for future research to ensure responsible and effective integration of AI in flipped classroom environments.