通过双重拒绝对话机制，让AI视频更符合教学逻辑。
When Saying No Makes Better Videos: Designing Dual Gatekeeping for Pedagogically Grounded AI Content Creation
- 教育者用理论重构AI脚本，实现教学设计迭代。
- 自动检测教学连贯性与视听同步问题，提升内容质量。
- 适合教育科技研究者和数字内容创作者参考。
为防止采用美学精致但教学缺陷明显的AI生成内容，本文研究了一种包含两层结构化拒绝机制的视频创作流程。第一层允许教育者基于多媒体学习理论迭代优化AI脚本，第二层则利用自动化指标识别教学连贯性与叙事-视觉同步方面的违规。尽管每层均不全面，但其协同作用使有原则的延迟输出——即在不符合严格标准前不采纳AI结果——成为提升质量的催化剂。对23名教育者在3个主题上的实验，以及基于7个来自科学与哲学课程标准的数据集的自动化评估表明，两层机制均独立提升了相同的教学维度，说明深思熟虑的抵制与生成式AI并非对立，而是互补伙伴。
原文摘要 · Abstract (English)
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.
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