Abstract Summary/Description
In this work, we introduce Text2Net, a text-based computer network simulation engine, that introduces a transformative approach to computer network education. Text2Net leverages state-of-the-art large language models (LLMs) and artificial intelligence (AI) text generation tools to interpret and transform plain-text descriptions of network topologies, configurations and functionalities, into dynamic and interactive simulations. This tool utilizes natural language processing (NLP) and large language models, specifically OpenAI’s ChatGPT-4T, to facilitate an intuitive interface that allows users (e.g. students, educators, and trainer/trainee engineers) to configure and interact with network simulations without the need for learning multiple simulation tools thus simplifying hands-on learning of networking. Text2Net addresses significant educational barriers by automating complex setup processes and making net- work learning faster, more accessible and engaging. This paper explores Text2Net’s development, focusing on its innovative use of AI, the system’s architecture, and its impact on network education. We provide a comprehensive evaluation of the system’s usability, performance, and educational value through qualitative analyses to demonstrate its effectiveness. Text2Net is a stepping stone to potentially revolutionize computer network education.
