Date
Publisher
arXiv
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.
Why it matters
AI content can be aesthetically polished but pedagogically flawed. Preventing the adoption of such content requires ensuring that AI output meets rigorous standards for instructional coherence and narrative-visual synchronization.
Key findings
- In science and philosophy topics involving 23 educators, both teacher review and automated checks independently improved video instructional quality.
- Deferring AI output until it meets rigorous standards served as a catalyst for higher content quality.
- Thoughtful resistance to imperfect drafts and generative AI may act as partners rather than opposites in educational content creation.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Why use AI?
Study design
