High-quality STEM animations can be useful for learning, but they are still not common in daily teaching, mostly because they take time and special skills to make. In this paper, we present a semi-automated, human-in-the-loop (HITL) pipeline that uses a large language model (LLM) to help convert math and physics concepts into narrated animations with the Python library Manim. The pipeline also tries to follow multimedia learning ideas like segmentation, signaling, and dual coding, so the narration and the visuals are more aligned. To keep the outputs stable, we use constrained prompt templates, a symbol ledger to keep symbols consistent, and we regenerate only the parts that have errors. We also include expert review before the final rendering, because sometimes the generated code or explanation is not fully correct. We tested the approach with 100 undergraduate students in a within-subject A-B study. Each student learned two similar STEM topics, one with the LLM-generated animations and one with PowerPoint slides. In general, the animation-based instruction gives slightly better post-test scores (83% vs.78%, p < .001), and students show higher learning gains (d=0.67). They also report higher engagement (d=0.94) and lower cognitive load (d=0.41). Students finished the tasks faster, and many of them said they prefer the animated format. Overall, these results suggest LLM-assisted animation can make STEM content creation easier, and it may be a practical option for more classrooms.

翻译：高质量的STEM动画对学习有益，但在日常教学中仍不常见，这主要是因为制作此类动画需要时间和专业技能。本文提出了一种半自动化的、人在回路中（HITL）的流水线，利用大型语言模型（LLM）将数学和物理概念转化为配有旁白的动画，并借助Python库Manim实现。该流水线还尝试遵循多媒体学习原则，如分段、信号提示和双重编码，以增强旁白与视觉内容的一致性。为了保持输出稳定，我们使用了约束性提示模板、用于保持符号一致性的符号簿，并仅对包含错误的部分进行重新生成。由于生成的代码或解释有时不完全正确，我们在最终渲染前加入了专家评审环节。我们通过一项面向100名本科生的被试内A-B研究测试了该方法。每位学生学习两个相似的STEM主题，一个使用LLM生成的动画，另一个使用PowerPoint幻灯片。总体而言，基于动画的教学方式在后测得分上略高（83%对比78%，p<.001），学生的学习增益也更高（d=0.67）。此外，学生报告了更高的参与度（d=0.94）和更低的认知负荷（d=0.41）。学生完成任务的速度更快，且许多人表示偏爱动画形式。总体而言，这些结果表明，LLM辅助的动画生成可以简化STEM内容的创作过程，并可能成为更多课堂的实用选择。