Development of an AI Driven Text Simplification and Analogy Generation Platform Using a Pre-Trained BART Model

Understanding complex information can be a challenge for most learners, especially when it is filled with technical terms, abstract ideas, or specialized language. Education, research, and technical communication often suffer when content is too difficult for the intended audience. Simplifying text can help, but simplification alone does not always create the mental connections needed for deeper understanding. This research proposes and develops an AI-driven platform that combines text simplification and analogy generation to make complex information clearer and more relatable. A pre-trained BART model is used to simplify text while preserving meaning, and a Retrieval-Augmented Generation (RAG) process is applied to generate analogies based on user-selected themes such as sports or classrooms. The system is built with Python for the backend and Flutter for the frontend, offering a user-friendly interface for real-time processing. Evaluation using ROUGE and BERTScore confirmed the system’s effectiveness. Summarization achieved a ROUGE-1 score of 0.8315, while text simplification reached a BERTScore F1 of 0.9279, indicating high semantic fidelity. Analogy generation maintained F1 scores above 0.7, demonstrating relevance and conceptual clarity. These results confirm the platform's ability to improve comprehension through high-quality simplification and relatable analogies, making it a practical tool for education and accessible communication across diverse domains.