让AI当教学设计助手，帮教师高效生成更有效的知识视频
PedaCo-Gen: Scaffolding Pedagogical Agency in Human-AI Collaborative Video Authoring
- 用中间蓝图交互修改，让教师和AI共同设计视频结构
- 23位教育专家测试显示，视频质量显著优于传统方法
- 适合教育工作者、在线课程开发者使用
尽管文本生成视频（T2V）技术为内容创作民主化提供了可能，但当前模型多侧重视觉质量而非教学效果。本研究提出PedaCo-Gen，一个基于梅耶多媒体学习认知理论（CTML）的师生协同视频创作系统。不同于传统一次性生成，PedaCo-Gen引入中间表示（IR）阶段，允许教育者与AI评审员互动式审查和优化包含脚本与视觉描述的视频蓝图。对23位教育专家的研究表明，该系统在多个主题和CTML原则下均显著提升视频质量。参与者认为AI指导不仅是指令，更是促进自我反思的认知支架，报告生产效率（M=4.26）和指导有效性（M=4.04）均较高。研究强调通过有原则的共创重获教学主导权，为融合生成能力与专业经验的未来创作工具奠定基础。
原文摘要 · Abstract (English)
While advancements in Text-to-Video (T2V) generative AI offer a promising path toward democratizing content creation, current models are often optimized for visual fidelity rather than instructional efficacy. This study introduces PedaCo-Gen, a pedagogically-informed human-AI collaborative video generating system for authoring instructional videos based on Mayer's Cognitive Theory of Multimedia Learning (CTML). Moving away from traditional "one-shot" generation, PedaCo-Gen introduces an Intermediate Representation (IR) phase, enabling educators to interactively review and refine video blueprints-comprising scripts and visual descriptions-with an AI reviewer. Our study with 23 education experts demonstrates that PedaCo-Gen significantly enhances video quality across various topics and CTML principles compared to baselines. Participants perceived the AI-driven guidance not merely as a set of instructions but as a metacognitive scaffold that augmented their instructional design expertise, reporting high production efficiency (M=4.26) and guide validity (M=4.04). These findings highlight the importance of reclaiming pedagogical agency through principled co-creation, providing a foundation for future AI authoring tools that harmonize generative power with human professional expertise.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。