Integrating Recurrent Neural Networks in Virtual Reality-Based Learning Environments for Dynamic Educational Content Generation

The integration of Recurrent Neural Networks (RNNs) in virtual reality (VR)-based learning environments represents a cutting-edge approach to revolutionizing educational content generation The study begins by establishing the need for personalized and responsive educational content in the context of evolving learning paradigms. RNNs, known for their ability to capture sequential dependencies and patterns, are introduced as a powerful tool for modeling dynamic aspects of educational content. The integration of RNNs in VR environments is motivated by the desire to create interactive and evolving learning scenarios that adapt to individual learner preferences and progress. The research methodology involves the development of a prototype VR-based learning environment that incorporates RNNs for real-time content generation. The RNNs are trained on diverse datasets, allowing the system to learn and adapt to the varying needs and learning styles of users. The VR setting provides an immersive and engaging platform for learners, fostering a deeper understanding of complex concepts through interactive experiences. Key outcomes of the study include insights into the effectiveness of RNNs in dynamically generating educational content. The accuracy of the proposed model is 98.32% which is higher than existing methods like CNN (Convolutional Neural Network), CNN-LSTM (Long shirt Term Memory, GRU (Gated Recurrent Units).