# Qnario: AI-Powered Intelligent Test Paper Generation and Online Proctoring System

> An open-source AI examination platform that supports intelligent syllabus parsing, automatic test paper generation, real-time proctoring, and anti-cheating detection, demonstrating the practical application potential of AI in the education sector.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-05T20:13:07.000Z
- 最近活动: 2026-06-05T20:20:46.658Z
- 热度: 152.9
- 关键词: AI教育, 智能组卷, 在线监考, Gemini, Groq, React, FastAPI, 教育科技, 防作弊
- 页面链接: https://www.zingnex.cn/en/forum/thread/qnario-ai
- Canonical: https://www.zingnex.cn/forum/thread/qnario-ai
- Markdown 来源: floors_fallback

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## [Introduction] Qnario: Core Introduction to the AI-Powered Intelligent Test Paper Generation and Online Proctoring System

Qnario is an open-source AI examination platform whose core functions include intelligent syllabus parsing, automatic test paper generation, real-time proctoring, and anti-cheating detection, demonstrating the practical application potential of AI in the education sector.

**Original Author & Source**:
- Maintainer: shreysoni1102-cell
- Source Platform: GitHub
- Release Time: 2025
- Original Link: https://github.com/shreysoni1102-cell/Qnario

## Project Background: Addressing Key Pain Points of Teachers' Test Paper Compilation

The traditional test paper compilation process is time-consuming and repetitive. Teachers need to manually flip through textbooks to select questions and adjust difficulty, taking hours; if only part of the chapters are covered, precise question selection adds even more complexity. Qnario is designed to address this pain point: teachers upload the syllabus, AI automatically extracts units and knowledge points, and after selecting the scope, the test paper is generated instantly—reducing compilation time from hours to minutes.

## Core Function Analysis: End-to-End Support from Syllabus Parsing to Proctoring

### 1. AI Syllabus Parser
Supports formats like PDF/DOCX/JPG/PNG, automatically identifies structure to extract units and knowledge points, provides an interactive selection interface (with progress counter and select-all button), and flexibly matches teaching progress.

### 2. Intelligent Test Paper Generation
Dual-model strategy: The main model (Google Gemini API) generates high-quality questions, and the backup model (Groq LLaMA) switches seamlessly when Gemini's quota is exhausted; supports multiple question types (multiple choice, short answer, essay, true/false), configurable difficulty (easy/medium/hard), and teachers can preview and edit questions.

### 3. Real-Time Online Proctoring
- Exam Creation: Teachers generate a 6-digit room code; students join via the code, and the teacher's end displays student status in real time
- Anti-Cheating: Screen switching detection, 5-second auto-save, email OTP two-factor authentication

### 4. Student End & Admin Backend
Student End: Exam/practice modes, learning dashboard; Admin Backend: User management, statistics panel.

## Technical Architecture: Modular Design with Separate Frontend-Backend + AI Microservices

#### Frontend
React 18 + Vite 5, using React Router DOM 6, Axios, Socket.io-client, Recharts, jsPDF, etc.

#### Backend
Node.js + Express 4, paired with MongoDB + Mongoose 8, Socket.io 4, JWT, bcryptjs, Nodemailer, Multer, etc.

#### AI Microservices
Python + FastAPI, integrated with Google Gemini API (main) and Groq API (backup), with Pydantic v2 validation.

#### Database Models
Core models include 10 Mongoose models such as User, Question, StudentAttempt, StudentResult, Analytics, and GeneratedQuestion.

## Project Highlights & Insights: Dual Value of Engineering Design and Scenario Alignment

1. **Dual-Model Fault Tolerance**: Seamless switching between main and backup models to handle API quota limits and network fluctuations, ensuring user experience.
2. **Scenario-Centric**: Knowledge point-based question selection precisely solves teachers' pain points, reflecting an understanding of user workflows.
3. **Real-Time Proctoring Technology**: Socket.io for real-time communication, browser focus monitoring for anti-cheating—mature and low-cost solution.
4. **Modular Architecture**: AI service as an independent microservice, facilitating model switching, upgrades, and expansion.

## Applicable Scenarios: Covering Various Education and Training Needs

Qnario is suitable for:
- Primary and secondary education institutions: Quickly generate weekly and monthly test papers
- Training organizations: Flexible test paper compilation based on teaching progress
- University teachers: Reduce the burden of repetitive question setting
- Online education platforms: Integration as a functional module
- Corporate training departments: Internal assessment and certification exams

## Conclusion: Practical Value and Reference Significance of Qnario

Qnario is an open-source project that deeply integrates AI with education scenarios. It focuses on the engineering implementation of the complete process from 'syllabus parsing → intelligent test paper generation → online exam → real-time proctoring'—not pursuing large model parameter scales but emphasizing practical落地 (practical implementation). It has reference value for developers working on AI education applications and educators looking for test paper systems.