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AI Question-Paper Generator

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₹4,999.00

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  ₹4,999.00

Detail Description

1. Abstract

The AI Question-Paper Generator is a Generative AI-powered application designed to automatically create question papers based on subjects, topics, syllabus content, difficulty levels, and examination patterns. The system uses Large Language Models (LLMs) and Natural Language Processing (NLP) to generate multiple-choice questions, short-answer questions, and long-answer questions. Teachers can customize the number of questions, marks distribution, and difficulty level, then preview and download the generated question paper. This system reduces manual effort and helps educators prepare structured assessments efficiently.

2. Objectives

  1. Automate question-paper creation using Generative AI.
  2. Generate questions from syllabus documents, textbooks, and study materials.
  3. Support multiple question types, including MCQs and descriptive questions.
  4. Allow teachers to select difficulty levels: easy, medium, and hard.
  5. Configure total marks, exam duration, and question distribution.
  6. Generate answer keys and explanations when required.
  7. Export question papers in PDF or Word format.
  8. Reduce the time required for examination preparation.

3. Existing System

In the traditional system, teachers manually prepare question papers by referring to textbooks, previous examination papers, and syllabus documents.

Disadvantages

  1. Manual question preparation is time-consuming.
  2. Maintaining balanced difficulty levels can be challenging.
  3. Repeated questions may occur across examinations.
  4. Formatting and marks distribution require additional effort.
  5. Preparing multiple question-paper sets increases workload.
  6. Creating answer keys separately requires extra time.

4. Proposed System

The proposed system uses Generative AI and LLMs to generate structured question papers according to teacher-defined requirements.

Key features:

  1. Teacher registration, sign-in, and dashboard.
  2. Subject, topic, syllabus, and chapter selection.
  3. Upload syllabus or study materials in PDF and text formats.
  4. Automatic generation of MCQs, short-answer, and long-answer questions.
  5. Easy, medium, and hard difficulty settings.
  6. Customizable total marks, exam duration, and question count.
  7. Automatic answer-key generation.
  8. Duplicate-question checks and question editing.
  9. Multiple question-paper sets with different question combinations.
  10. PDF and Word export options.
  11. Admin dashboard for managing users and question-paper templates.

5. Implementation Procedure

  1. Requirement Analysis: Identify examination patterns, question types, and marking schemes.
  2. Frontend Development: Build the teacher dashboard, input forms, and question-paper preview.
  3. Data Collection: Prepare sample syllabi, textbooks, and subject-wise learning materials.
  4. Document Processing: Extract text from uploaded documents using PDF and document-processing libraries.
  5. Prompt Engineering: Design prompts that specify topics, difficulty, question types, marks, and output format.
  6. LLM Integration: Connect an LLM API to generate relevant questions and answer keys.
  7. Question Validation: Check question structure, duplicate content, marks distribution, and relevance to the supplied material.
  8. Paper Assembly: Organize questions into sections with instructions, numbering, and marks.
  9. Export Functionality: Generate downloadable PDF and Word documents.
  10. Testing and Deployment: Test generated papers for accuracy, formatting, and alignment with the selected syllabus.

6. Software Requirements

  1. Programming Language: Python
  2. Frontend: Streamlit or React.js
  3. Backend: Flask or FastAPI
  4. Generative AI: OpenAI API or a compatible LLM
  5. NLP Framework: LangChain or LlamaIndex
  6. Document Processing: PyPDF and python-docx
  7. Database: SQLite, MySQL, or MongoDB
  8. PDF Generation: ReportLab
  9. Word Document Generation: python-docx
  10. Development Environment: VS Code
  11. Version Control: Git and GitHub
  12. Operating System: Windows or Linux

7. Hardware Requirements

  1. Processor: Intel Core i3 or equivalent and above
  2. RAM: Minimum 8 GB; 16 GB recommended
  3. Storage: At least 10 GB of free space
  4. Internet Connection: Required for hosted LLM APIs
  5. GPU: Optional when using cloud-based AI services

8. Advantages of the Project

  1. Saves teachers' time in preparing question papers.
  2. Generates questions across multiple difficulty levels.
  3. Supports different examination patterns and subjects.
  4. Creates answer keys to simplify evaluation preparation.
  5. Enables reusable and editable question-paper templates.
  6. Reduces repetitive manual formatting.
  7. Supports multiple paper sets for practice and assessment.
  8. Improves assessment preparation when outputs are reviewed by teachers.


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