AI Research Partner: An AI-Powered Web Platform for Research Paper Analysis, Summarization, and Ideation
TL;DR
The AI Research Partner is presented, a full-stack MERN web platform that unifies the research-reading workflow — comprehension, synthesis, and ideation — into a single authenticated system, demonstrating that a single, prompt-engineered platform can reasonably reproduce the core stages of expert research reading within one coherent, collaborative interface.
Abstract
The rapid growth of published research literature has made manual, unaided reading a bottleneck for students and early-stage researchers, who must extract structured understanding from unstructured PDF documents while operating at varying levels of comprehension. This paper presents the AI Research Partner, a full-stack MERN (MongoDB, Express.js, React, Node.js) web platform that unifies the research-reading workflow — comprehension, synthesis, and ideation — into a single authenticated system. The platform ingests a PDF, extracts its text, and uses the Google Gemini large language model to generate multi-level (basic, medium, technical) section summaries, an interactive D3.js concept knowledge graph, novelty-rated research ideas, citation recommendations, auto-generated quizzes, and abstract/slide drafts, while a Socket.io-based real-time layer enables collaborative annotation among multiple users. The system was implemented end-to-end, evaluated through functional testing across eight modules, and benchmarked for AI feature response latency and concurrent-user scalability. Results indicate pass rates above 87% across all modules, typical AI response times of 3-17 seconds depending on feature complexity, and stable real-time note-broadcast latency under load, demonstrating that a single, prompt-engineered platform can reasonably reproduce the core stages of expert research reading within one coherent, collaborative interface.