LLM2Manim: Pedagogy-Aware AI Generation of STEM Animations
Aastha Joshi, Hongyi Ke, Meet Gajjar, Aaron Christian, Qi Wang, Jun Chen

TL;DR
This paper introduces a semi-automated pipeline using large language models to generate STEM animations with Manim, improving learning engagement and efficiency in educational settings.
Contribution
It presents a novel human-in-the-loop approach combining LLMs, multimedia principles, and expert review for creating effective STEM animations.
Findings
Students learned better with animations (83% vs. 78%)
Animation increased engagement (d=0.94) and learning gains (d=0.67)
Students preferred animated content and completed tasks faster.
Abstract
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…
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