A rapid review of using AI-generated instructional videos in higher education - Hai, Tran Trieu; Mai, Duong Thi Thuy; Hanh, Nguyen Van (Frontiers in Computer Science, 2026)
IntroductionGenerative artificial intelligence (AI) has enabled the rapid emergence of AI-generated instructional videos (AIGIVs) as a new form of learning material in higher education. However, evidence on how they are produced, applied, and the reported benefits and risks remains fragmented, highlighting the need for a systematic synthesis.MethodsThis study conducted a rapid review following PRISMA principles. Studies published from 2023 onward were searched on the Web of Science, Scopus, IEEE Xplore, and Google Scholar. Fifteen eligible studies were analyzed using qualitative content analysis and thematic synthesis.ResultsTwo production modes were identified: fully AI-based video generation (e.g., Sora, HeyGen, Veo) and AI-assisted human-made production (e.g., DALL·E, ChatGPT). Pedagogical applications included using AIGIVs as instructional alternatives and as tools for reflective pedagogy, particularly ethical and critical reflection. Benefits included efficiency and scalability, improved accessibility and personalization, and enhanced emotional engagement and memory. Risks involved ethical concerns, technical limitations, and inauthentic or unreliable content.DiscussionAIGIVs show strong potential for higher education, but their value depends on instructional design, human oversight, and responsible governance.