  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.
