Rapid learning tools for student

Yap, Li Hui (2025) Rapid learning tools for student. Final Year Project, UTAR.

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Abstract

Due to information overload, language difficulties, and fragmented technologies, students frequently struggle to navigate large volumes of academic content. The creation of a web-based Rapid Learning Tool (RLT) that combines document analysis, AI-powered summarization, translation, definition lookup and research support into a single platform is the project’s suggested solution to these problems. A variety of document formats (PDF, DOC, DOCX, PPT, PPTX, and TXT) can be uploaded to the system, which also automatically extracts and summarizes important information, translates or explains words in real time, and collects relevant research papers from reliable databases, for example Semantic Scholar. Additionally, the word cloud visualization and keyword extraction features enable students to rapidly understand the primary ideas of texts. Rapid Learning Tool (RLT) combines AI services like Cohere AI, Ollama, DeepL, and Google Translate and is based in Node.js and Express.js for the backend. To guarantee a customized and safe educational environment, it also makes use of secure JWT authentication, session monitoring, and user conversation history abilities. Rapid Learning Tool successfully lowers students’ cognitive load while improving their understanding and learning effectiveness by using these features. It overcomes the gap between disjointed learning processes and effective, intelligent learning support by giving students a reliable, adaptable and useful instructional tool.

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