LLM Quiz Engine: An Automated Quiz Generation System Based on Large Language Models

Introducing an AI system that uses large language models to automatically generate quizzes, supporting question generation from text/PDF files, with features like Pydantic validation, quality checks, and quiz history records.

• Original Authors/Maintainers: intelligent-quiz-systems team
  • Artur Arturstrag (Unit testing, quiz history)
  • Joanna Czarnocka (LLM calls, Pydantic validation, Streamlit UI)
  • Krystian (LLM integration, quality checks, guardrails)
  • Magdalena (Prompt management)
• Source Platform: GitHub
• Original Title: llm-quiz-engine: AI-powered system for automated quiz generation
• Original Link: https://github.com/intelligent-quiz-systems/llm-quiz-engine
• Publication Date: May 23, 2026

The education and training field has long faced a challenge: creating high-quality quiz questions is both time-consuming and labor-intensive. Teachers, trainers, and content creators need to invest a lot of time in designing questions, writing options, and ensuring answer accuracy.

With the rise of large language models (LLMs), automated content generation has become possible. The LLM Quiz Engine project leverages this technology to allow AI to automatically generate quiz questions based on provided text materials, significantly improving content creation efficiency.

The project adopts a modular design with clear responsibilities for each component:

Prompt Manager Developed by Magdalena, it manages and optimizes prompt templates sent to LLMs to ensure high-quality quiz question generation.

LLM Integration Layer Led by Krystian, it handles communication with large language models, including:

• Model calling and response processing
• Fallback mechanism to ensure service availability
• Result reduction to optimize output quality
• Quality checks to ensure the accuracy of generated content
• Guardrails to prevent inappropriate content generation

Pydantic Validation Layer Implemented by Joanna Czarnocka, it uses Pydantic models to perform structured validation on LLM outputs, ensuring correct data format and complete fields.

User Interface (Streamlit UI) Also developed by Joanna, it provides three main interfaces:

• Configuration interface: Set model parameters and API keys
• Quiz interface: Interactive interface for taking quizzes
• Summary interface: Display quiz results and statistics

File Processing Module Supports loading content from text files (.txt) and PDF files as context sources for quiz generation.

• Python: Core development language
• Streamlit: Web interface for quickly building data applications
• Groq API: Provides access to LLM services
• Pydantic: Data validation and serialization
• python-dotenv: Environment variable management

The system can automatically generate various types of quiz questions based on input text or PDF materials:

• Multiple-choice questions: Automatically generate questions and options
• True/false questions: Generate true/false judgments based on material content
• Fill-in-the-blank questions: Extract key information to create fill-in-the-blank questions
• Short-answer questions: Generate open-ended questions

Unlike simple random question generation, LLM Quiz Engine can:

• Understand the core content of input materials
• Extract key concepts and knowledge points
• Generate questions closely related to the materials
• Ensure the accuracy of answers

The project has built-in multi-layer quality checks:

1. Pydantic Validation: Ensure output format meets expectations
2. Content Quality Check: Verify the rationality of questions and answers
3. Guardrail Mechanism: Filter inappropriate or sensitive content
4. Fallback Strategy: Switch to alternative solutions when the main model fails