Dual Gatekeeping Improves AI-Generated Educational Video Quality

The proliferation of AI-generated content, particularly videos, raises concerns about quality, especially in educational contexts where aesthetic polish might mask pedagogical flaws. Researchers have explored a novel video authoring pipeline designed to ensure high-quality, pedagogically sound AI-created content through a "dual gatekeeping" mechanism. This system incorporates two distinct layers of structured refusal. The first layer empowers educators to actively and iteratively refine AI-generated scripts, guiding the AI to align with established multimedia learning theories. This ensures that the foundational content is instructionally sound from an expert perspective. The second layer introduces automated metrics that systematically flag issues such as instructional coherence and the synchronization between narrative and visuals. While neither gatekeeping layer is exhaustive on its own, their combined synergy fosters a process of "principled resistance," where AI output is deferred and refined until it meets rigorous quality standards. Evaluations with educators and automated metrics across various topics confirm that both layers independently enhance instructional dimensions, demonstrating that thoughtful human-AI collaboration leads to superior educational content.