PedaCo-Gen：在人机协作视频制作中构建教学代理
PedaCo-Gen: Scaffolding Pedagogical Agency in Human-AI Collaborative Video Authoring
AI总结：
PedaCo-Gen通过引入中间表示阶段，使教育者能与AI审查员互动审查和优化视频蓝图，提升视频质量与教学有效性。
摘要：
尽管文本到视频（T2V）生成AI的进步为内容创作民主化提供了前景，但当前模型往往优化于视觉保真度而非教学有效性。本研究引入PedaCo-Gen，一种基于 Mayer 的多媒体学习认知理论（CTML）的人机协作视频生成系统，旨在制作教学视频。与传统的
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
