ExamForge: An Intelligent Exam Paper Generation System Based on Large Language Models

ExamForge is an open-source AI-driven exam generation system that can automatically convert uploaded learning materials into customized exam papers, supporting multiple question types and difficulty levels. This project is maintained by guna2004-hash, hosted on GitHub, original link: https://github.com/guna2004-hash/ExamForge, released on 2026-06-07. Its core goal is to help educators and learners quickly create high-quality exam papers, solving the efficiency and quality issues of traditional manual question setting.

In the field of educational technology, traditional manual question setting is time-consuming and labor-intensive, making it difficult to ensure comprehensive coverage of topics and balanced difficulty. With the maturity of Large Language Model (LLM) technology, using AI to automatically generate exam papers has become possible. As an open-source AI-driven system, ExamForge aims to combine modern web technology with LLM capabilities to intelligently extract knowledge points from learning materials and generate questions, addressing the above pain points.

Technical Stack

• Backend: FastAPI (high-performance Python web framework with asynchronous API support), LLM integration, multi-format document parsing
• Frontend: React (modern UI framework), responsive design (adapts to multiple devices)

Core Features

1. Intelligent content parsing: Automatically analyze uploaded documents/PDFs/texts and extract key knowledge points
2. Diverse question types: Multiple-choice, fill-in-the-blank, short-answer, etc.
3. Difficulty control: Adjustable question difficulty levels
4. Customized output: Specify number of questions and distribution of question types
5. Exam paper export: Support export in multiple formats

ExamForge is suitable for various scenarios:

• School education: Teachers quickly generate unit tests, mid-term/final exam papers
• Online education platforms: Generate practice questions and quizzes for supporting courses
• Corporate training: Generate assessment questions based on training materials to evaluate employees' learning outcomes
• Self-study: Learners convert notes into self-assessment questions to strengthen knowledge mastery

1. Application of RAG Architecture: May adopt Retrieval-Augmented Generation technology to ensure high relevance between generated questions and source materials
2. Local Deployment Support: Protect data privacy through local LLM inference solutions like Ollama
3. Modular Design: Separated architecture of FastAPI and React, easy to extend and maintain

ExamForge is a typical application combining educational technology and LLMs, demonstrating the practical value of AI in the education field and providing technical references for similar applications. In the future, with the development of multimodal models and Agent technology, the system may support video/audio input and more intelligent interaction methods. For developers exploring AI educational applications, this open-source project is worth paying attention to.