用AI自动生成计算机课的类比动画，助教省时高效。
ANVIL: Analogies and Videos for Lecturers
- 输入概念定义，自动生成类比文本和可执行动画脚本。
- 教师评价显示多数动画内容达标，用户认可其可用性。
- 结合人工评估与自动化检测，确保教学质量和生成鲁棒性。
我们提出ANVIL，一个用于计算机科学教学的多模态生成系统，能基于概念定义自动生成类比性教学动画。系统先生成类比文本，将其结构化为视觉剧本，再生成可执行的manim代码以渲染动画，并具备自动修复机制提升鲁棒性。为实现大规模评估，我们首先开展教师评价以建立质量基准，并据此指导自动化筛选：针对文本类比，引入基于LLM的评估器进行可扩展质量筛查；针对视频，因主观判断难自动化，改用对剧本忠实度的自动化代理指标进行审计与错误分析。此外，我们还开展了面向教育工作者的用户研究，考察采纳需求与潜在风险。结果表明，ANVIL生成的内容常被评价为合格，且教育者对其价值与易用性反馈积极。
原文摘要 · Abstract (English)
We present ANVIL, a multimodal generative system that automates the production of analogy-based instructional animations for computer science topics. Given a concept definition, ANVIL generates a textual analogy, compiles it into a structured visual screenplay, and produces executable manim code to render an animation, with an automated repair mechanism to improve robustness. Evaluating such systems at scale requires balancing pedagogical validity with scalability. We begin with a teacher evaluation to ground the quality assessment and use its findings to guide automated screening. For textual analogies, we introduce an LLM-based evaluator for scalable quality screening; for videos, where subjective judgments are difficult to automate, we instead assess fidelity to the intended screenplay using an automated proxy for auditing and error analysis. We further conduct a user study with educators to examine adoption requirements and risks. Our findings suggest that ANVIL can produce materials that are frequently rated as adequate, and that educators respond positively to its perceived value and usability.
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