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.

翻译：尽管文本到视频（T2V）生成式人工智能的进步为内容创作的民主化提供了一条充满希望的道路，但现有模型通常更侧重于视觉保真度而非教学有效性。本研究介绍了PedaCo-Gen，这是一个基于梅耶多媒体学习认知理论（CTML）、融入教学理念的人机协作视频生成系统，用于创作教学视频。PedaCo-Gen摒弃了传统的“一次性”生成模式，引入了一个中间表示（IR）阶段，使教育工作者能够与AI评审员互动，共同审查和优化包含脚本和视觉描述的“视频蓝图”。我们与23位教育专家进行的研究表明，与基线方法相比，PedaCo-Gen在不同主题和CTML原则下均显著提升了视频质量。参与者认为AI驱动的指导不仅仅是一套指令，更是一种增强其教学设计专业知识的元认知支架，并报告了较高的制作效率（M=4.26）和指导有效性（M=4.04）。这些发现凸显了通过有原则的协同创作来重获教学能动性的重要性，为未来协调生成能力与人类专业知识的AI创作工具奠定了基础。