We introduce Augmented Physics, a machine learning-powered tool designed for creating interactive physics simulations from static textbook diagrams. Leveraging computer vision techniques, such as Segment Anything and OpenCV, our web-based system enables users to semi-automatically extract diagrams from physics textbooks and then generate interactive simulations based on the extracted content. These interactive diagrams are seamlessly integrated into scanned textbook pages, facilitating interactive and personalized learning experiences across various physics concepts, including gravity, optics, circuits, and kinematics. Drawing on an elicitation study with seven physics instructors, we explore four key augmentation techniques: 1) augmented experiments, 2) animated diagrams, 3) bi-directional manipulatives, and 4) parameter visualization. We evaluate our system through technical evaluation, a usability study (N=12), and expert interviews (N=12). The study findings suggest that our system can facilitate more engaging and personalized learning experiences in physics education.

翻译：本文介绍增强物理（Augmented Physics），一种基于机器学习的工具，旨在从静态教科书图表中创建交互式物理仿真。通过融合计算机视觉技术（如Segment Anything和OpenCV），我们的网页端系统支持用户从物理教材中半自动提取图表，并基于提取内容生成交互式仿真。这些交互式图表可无缝嵌入扫描版教材页面，为重力、光学、电路、运动学等多元物理概念提供交互式个性化学习体验。基于对七位物理教师的启发式研究，我们探索了四种核心增强技术：1）增强实验，2）动态图表，3）双向可操作模型，4）参数可视化。我们通过技术评估、可用性研究（N=12）和专家访谈（N=12）对系统进行综合评价。研究结果表明，该系统能够为物理教育创造更具吸引力与个性化的学习体验。