AI-Driven Multi-Modal Interactive Learning Environment Using Deep Learning and Chain-of-Thought Reasoning

In the context of rapid advancements in educational technology, Active Interactive Learning Environments (ILEs) have emerged as key tools for enhancing instructional outcomes and student engagement. This paper presents an innovative active interactive learning system based on multi-modal chain-of-thought (CoT) reasoning, aiming to optimize personalized support in the learning process by integrating multi-modal data (including text, images, and videos) with CoT techniques. The system utilizes advanced deep learning technologies such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Retrieval-Augmented Generation (RAG) to achieve dynamic, real-time personalized content generation and feedback mechanisms. Experimental results indicate that this system significantly improves student performance and engagement in the “Computer Networks” course, demonstrating its effectiveness in practical teaching settings. This study provides a solid theoretical foundation and practical guidance for further research and application of intelligent educational systems, highlighting the potential for driving future educational innovation.