When Saying No Makes Better Videos: Designing Dual Gatekeeping for Pedagogically Grounded AI Content Creation

2026-08-20

AI Summary

为防止采用有教育缺陷的AI内容，研究设计了双层筛选机制，结合教育者迭代修改和自动指标检测以提升视频质量。

Abstract

To prevent the adoption of aesthetically polished but pedagogically flawed AI content, we study a video authoring pipeline featuring two layers of structured refusal. The first layer empowers educators to iteratively reshape AI scripts based on multimedia learning theory, while the second employs automated metrics to flag violations in instructional coherence and narrative-visual synchronization. While neither layer is exhaustive, their synergy ensures that principled resistance--the act of deferring AI output until it meets rigorous standards--becomes a catalyst for higher quality. Evaluation combining a study with 23 educators across 3 topics and automated metrics across 7 topics drawn from established science and philosophy curricula shows that both layers independently improve the same instructional dimensions, suggesting that thoughtful resistance and generative AI are not opposites but partners.

Problem

• AI content creation
• pedagogical quality
• gatekeeping

Innovation

• Dual Gatekeeping
• Pedagogically Grounded
• Automated Metrics
• Instructional Coherence