PhyEduVideo: A Benchmark for Evaluating Text-to-Video Models for Physics Education

TL;DR

This paper introduces a benchmark for evaluating Text-to-Video models in physics education, assessing their ability to generate accurate, curriculum-aligned explanatory videos for core physics concepts.

Contribution

It presents the first dedicated benchmark for T2V models in physics education, including a dataset of prompts and an evaluation framework to measure conceptual accuracy and visual quality.

Findings

Models produce visually coherent videos with smooth motion.

Conceptual accuracy varies across physics topics, with struggles in electromagnetism and thermodynamics.

Performance is better in mechanics, fluids, and optics.

Abstract

Generative AI models, particularly Text-to-Video (T2V) systems, offer a promising avenue for transforming science education by automating the creation of engaging and intuitive visual explanations. In this work, we take a first step toward evaluating their potential in physics education by introducing a dedicated benchmark for explanatory video generation. The benchmark is designed to assess how well T2V models can convey core physics concepts through visual illustrations. Each physics concept in our benchmark is decomposed into granular teaching points, with each point accompanied by a carefully crafted prompt intended for visual explanation of the teaching point. T2V models are evaluated on their ability to generate accurate videos in response to these prompts. Our aim is to systematically explore the feasibility of using T2V models to generate high-quality, curriculum-aligned educational content-paving the way toward scalable, accessible, and personalized learning experiences powered by AI. Our evaluation reveals that current models produce visually coherent videos with smooth motion and minimal flickering, yet their conceptual accuracy is less reliable. Performance in areas such as mechanics, fluids, and optics is encouraging, but models struggle with electromagnetism and thermodynamics, where abstract interactions are harder to depict. These findings underscore the gap between visual quality and conceptual correctness in educational video generation. We hope this benchmark helps the community close that gap and move toward T2V systems that can deliver accurate, curriculum-aligned physics content at scale. The benchmark and accompanying codebase are publicly available at https://github.com/meghamariamkm/PhyEduVideo.