Desktop Application for a Virtual Chemistry Lab with Computer Vision-Based Interaction for High School Students

The integration of computer vision in educational environments opens new opportunities for interactive and immersive learning. This work proposes a desktop application for a virtual chemistry laboratory aimed at high school students, utilizing hand gesture recognition to simulate real laboratory interactions. Through the development of a proprietary dataset with multi-action gestures and the training of a convolutional neural network model, the system enables students to conduct virtual experiments safely, reinforcing theoretical concepts through practical experiences. This solution promotes active, accessible, and personalized learning in science education, especially in contexts with limited resources.