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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Figure 1: Overview of ANVIL ’s generation pipeline. Given a target CS concept, ANVIL (1) synthesizes an analogy, (2) compiles it into visual elements and a structured screenplay, (3) generates an executable manim program and applies a bounded agentic repair loop to fix compilation/runtime errors, and (4) renders the animation.
Figure 2: Model–View–Controller Pattern as a Restaurant. The View captures the order . The Controller ( waiter ) forwards it to the Model ( kitchen ). The Model processes the order ( dish prepared ).
Figure 3: Heatmaps visualizing the expert evaluation results on the artifact set.