AI-Generated Instructional Videos: A Systematic Review of Learning Impacts, Applications, and Research Perspectives

Artificial Intelligence Generated Content (AIGC) refers to AI-driven content generation techniques based on Generative Adversarial Networks and large pre-trained models. It leverages learning and pattern recognition from existing data to generate relevant content with an appropriate level of generalization capability. For education, it offers new possibilities for developing teaching resources. AIGC efficiently generates instructional videos, significantly reducing the workload for teachers. This study conducts a systematic literature review to analyze the current state of generated instructional video applications and technological frameworks. Data was sourced from four databases: Web of Science, ERIC, IEEE Xplore, and Google Scholar. 143 articles were initially found in the four databases. In total, 12 articles were included in the final analysis. The results show that the quality of generated instructional videos is sufficient for educational use. However, they have certain limitations in boosting learning motivation and social presence, and cannot yet replace traditionally produced videos. Their applications span various educational stages, from early childhood to higher education. The technical framework includes several stages, including script creation, image generation, speech synthesis, animation production, and final video editing. Each stage relies on the collaboration of multiple AI tools. These findings not only provide insights into the current application landscape of generated instructional videos, but also offer research directions of such videos.