Ahmed, Md. SabbirAnan, Md. Ajmain AosafPrio, Sayad Md.Nabi, A. K. MahamudunIslam, Rifat AraIslam, Shamazda2025-06-292025-06-2920252025-02ID 20101388ID 20101356ID 24141282ID 18201091ID 19301225http://hdl.handle.net/10361/26419Cataloged from PDF version of thesis.Includes bibliographical references (pages 60-63).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.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.73 pagesenBRAC University theses reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Artificial Intelligence (AI)Physics educationAi-powered learning platformNatural Language Processing(NLP)Deep learningGenerative Adversarial Networks(GANs)3D VisualizationMotion video simulationComputer visionSTEM educationArtificial intelligenceMachine learning.Thesis