Exploring a Blended Teaching Model with AI-Generated Short Videos: Evidence from Higher Education

Abstract

The increasing integration of digital technologies into higher education has contributed to a wider use of video-based instructional resources, particularly in blended learning settings. In recent years, developments in generative artificial intelligence have made it possible to produce short-form educational videos more efficiently, offering new ways to support flexible teaching and learning. Despite growing interest in these tools, their role within structured teaching models remains insufficiently examined. This study explores how AI-generated short videos can be incorporated into a blended teaching approach in an undergraduate digital media course. A mixed-method design was adopted, combining questionnaire data with semi-structured interviews to better understand students’ learning behaviors, participation patterns, and their experiences across online and face-to-face learning contexts. In addition, the study considers how video-based resources are organized within the overall course structure and how they support different stages of learning. The findings indicate that short, clearly structured videos can help students manage their learning more flexibly and maintain engagement beyond the classroom. Many students showed a preference for concise content that was closely aligned with specific learning tasks. The use of video materials also allowed instructors to shift part of the content delivery outside class time, creating more opportunities for discussion and interaction during in-person sessions. However, the results suggest that poorly designed video content may lead to fragmented understanding or increased cognitive load. Overall, the study presents a practice-oriented approach to integrating AI-generated short videos into blended teaching and highlights their potential to support student-centered learning in higher education.