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/ wequiz Public
forked from Team-WeQuiz/wequiz

Shall We Quiz?๐Ÿ˜† Play and solve quizzes generated by AI โ€” together with your friends!

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๐ŸŽฒ Shall we quiz?... Click here!

*The service is currently not running due to server costs.

๐ŸŒˆ Introduction Page

๐Ÿ“ Send Feedback



๐Ÿค” Overview

A multiplayer quiz service where you can solve AI-generated questions with friends.



๐Ÿ–ฑ๏ธ Usage

1. Convert PDF content into quizzes

Users upload a PDF file, which is parsed and vectorized. Based on the extracted content, an LLM generates customized quiz questions.

2. Create a quiz room

A room is created where users can join to solve the quiz together. Users can communicate using simple emojis and chat messages.

3. Real-time quiz solving

Once the game starts, users solve questions in real-time. Questions are presented sequentially, and when a majority of players have answered, the game moves to the next question after a short countdown. Scores are updated live based on correct answers. Light interactions through emojis are supported during the game.

4. Share and discuss answers

After the quiz ends, a final scoreboard is shown. Users can view and share each otherโ€™s answers and engage in discussion about them.



๐ŸŽจ User Interface

Start Page Main Lobby Create Room
Join Room Solve Quiz View Answers


๐Ÿ› ๏ธ Architecture

Overall Architecture
CI / CD


๐ŸŽฎ Demo

Video Label



๐Ÿ“ Documents



๐Ÿš€ Team

Team WeQuiz
Kyuyeon Park(Team Lead) Jaemin Shim Keumjang Ahn Woorim Kim Junhyeong Bae
๐Ÿค– โš™๏ธ โš™๏ธ ๐ŸŽจ ๐ŸŽจ
ML Engineer Back-end Developer Back-end Developer Front-end Developer Front-end Developer


๐Ÿพ My Contributions (Kyuyeon Park - ML Engineer)

I implemented the end-to-end quiz auto-generation pipeline based on RAG (Retrieval-Augmented Generation), enabling question creation from document content. [ ๐Ÿ‘‰ directory ]

This pipeline includes:

  • Document Summarization: Parsing and chunking uploaded PDFs using LangChain
  • Question Generation: Extracts key concepts from the document and uses retrieved chunks to generate contextually relevant quiz questions via LLM prompts
  • Answer Evaluation: Assessing user answers against ground-truth answers using similarity scoring
Quiz and Document Summarization Workflow

2024-1 KMUCS Capstone Design | Copyright 2024. WeQuiz All rights reserved.

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Shall We Quiz?๐Ÿ˜† Play and solve quizzes generated by AI โ€” together with your friends!

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