Elevating education with AI: augmenting the understanding of physics through topic prediction, three-dimensional visualisation, and dynamic video aids

Abstract

In an era defined by the rapid evolution of science and technology, the integration of Artificial Intelligence (AI) into education has emerged as a transformative force. This study explores the revolutionary potential of AI in reshaping physics education by developing an AI-powered learning platform tailored specifically to the physics domain. The proposed platform combines advanced AI methodologies, including Natural Language Processing (NLP), Deep Learning, Generative Adversarial Networks (GANs), Computer Vision, and Machine Learning algorithms, to enhance the learning and problem-solving experience for both students and teachers. The platform offers a dynamic and interactive environment where users can efficiently solve complex physics problems while visualizing them in an intuitive manner. By leveraging user- generated data, the model creates personalized 3D visualizations and motion videos that simulate the given scenarios, enabling users to grasp abstract concepts and problem-solving strategies more effectively. Furthermore, the study delves into the rationale behind the selection of specific AI models and algorithms, the type and significance of the collected data, the comparative analysis with existing AI-based education tools, and the potential impact on the target user base. This research not only bridges the gap between theoretical physics and practical understanding but also provides an alternative approach to traditional learning methods. By facilitating the visualization of complex problems and offering innovative solutions, the proposed model aims to empower educators and learners alike. Ultimately, this study underscores the transformative potential of AI in fostering deeper comprehension, engagement, and creativity in physics education.