AI-Generated Video in Education: A Scoping Review of Technologies, Pedagogical Applications, Benefits, and Challenges

Rapid advances in generative artificial intelligence (AI) have accelerated the development and educational use of AI-generated video (AIGV). Despite growing interest, existing research remains fragmented, with a limited synthesis of how AIGV technologies are applied across educational contexts. This study addresses this gap through a scoping review that maps the AIGV landscape in education. Following PRISMA-ScR guidance, this scoping review identified 65 studies from Scopus, Web of Science, and Google Scholar and analyzed them using descriptive and thematic synthesis. The findings reveal four main AIGV technology categories, including text-to-video generation, AI avatar video generation, AI tools for partial video generation, and deepfake video generation. AIGV has been applied across diverse educational domains, with a strong concentration in higher education and increasing use in digital learning environments. Pedagogically, AIGV has been reported to support not only content delivery but also simulation, multimodal learning, and personalized instruction. While the reviewed literature reports potential benefits related to scalability, accessibility and inclusive learning, pedagogical flexibility, and learner engagement, it also identifies challenges related to technical quality, content accuracy, teaching presence, ethical concerns, and epistemic trust and content credibility. Overall, this review shifts attention from the general debate about whether AIGV is useful or risky to more precise questions about which forms of AIGV are being used, in which contexts, for what pedagogical purposes, and with what reported opportunities and concerns.