School-Level AI MCQ Generator
Budget: ₹1,500 – ₹12,500 INR
I need an AI-driven workflow that automatically produces multiple-choice questions (single-answer format) across standard secondary and higher-secondary subjects. The system should let me feed in a topic or a chapter outline and then return a bank of well-structured MCQs complete with four options, the correct key, and explanatory note.
Requirement Document: AI-Powered MCQ Generator (CBSE & Maharashtra Board Focus)
Objective
To develop an AI-powered Multiple Choice Question (MCQ) Generator that can create high-quality, exam-oriented questions in a standardized format compatible with Microsoft Word. The system should also be able to learn from sample MCQs provided by the user and generate questions in a similar style. The focus will be on the CBSE syllabus (Classes 5 to 10) as well as the Maharashtra State Board syllabus (Classes 5 to 10).
Key Features
Input Compatibility
Accept plain text (.txt)
Accept PDF files
Accept scanned pages/images with OCR support (.jpg, .png, .tif)
Must be able to correctly interpret mathematical symbols (integration, differentiation, geometry 2D/3D, etc.)
AI Integration
Utilize ChatGPT (OpenAI), Google AI (Gemini), or equivalent LLMs for:
Generating subtopics directly aligned with CBSE and Maharashtra Board textbooks
Creating MCQs with adjustable difficulty levels (Easy, Medium, Hard)
Ensuring questions are syllabus-aligned and exam-relevant
Generating MCQs in the same style/format as sample MCQs provided by the user
MCQ Generation Logic
Generate questions on one subtopic or combinations of subtopics
Quantity control: User can request n number of MCQs per run (e.g., 10, 50, 200, 1000+)
Difficulty control: User can set a defined difficulty level for the entire batch (Easy/Medium/Hard) or a mix distribution (e.g., 20% Easy, 60% Medium, 20% Hard)
Distractor options must be plausible, reflecting common misconceptions
Each distractor must have a specific logic/reason so that when a student selects a wrong option, the system can explain why that choice was incorrect
Maintain difficulty calibration based on user selection
Replicate tone, phrasing, and structure from sample MCQs
Output Format
Each MCQ should be exported into a Microsoft Word .docx file
Format: Homeworkstick 9-Row, 1-Column Table
Question Text
Option A
Option B
Option C
Option D
Correct Answer (only option letter)
Hint
Stepwise Detailed Explanation (including reasoning for distractors)
Subtopic Name(s)
Scalability
Support bulk generation (hundreds of MCQs per chapter)
Handle large syllabus content efficiently
Export on a per-chapter or per-subject basis
Examination Assembly: The system must be able to automatically create full examinations with a pre-defined number of questions, selected from different chapters and distributed across different difficulty levels as specified by the user.
Technical Requirements
Architecture: The MCQ Generator must be primarily AI/ML (LLM)-based, leveraging models such as ChatGPT or Google Gemini for generation. It must be augmented with rule-based validation to ensure format compliance and SymPy-based correctness checks for mathematical accuracy.
OCR: Tesseract OCR (baseline) with optional MathPix/Google Vision for complex math
Word Export: Python-docx or equivalent
Validation: Ensure schema compliance for MCQs (correct answer must match one option)
Platform: Windows-only (Windows 10/11)
Language: English-only
Style Adaptation: Use few-shot learning from user-provided sample MCQs to replicate formatting and phrasing
Distractor Logic: Each distractor must be tied to a misconception or error type (calculation mistake, conceptual misunderstanding, wrong formula, etc.)
Duplicate Question Detection (Mandatory): Detect and flag exact duplicates and near-duplicates before export, using a combination of text normalization, fuzzy matching, and semantic similarity (embeddings). Provide a review report listing suspected duplicates with similarity scores and suggested merges.
Adaptive Difficulty (Mandatory): Adjust MCQ difficulty dynamically based on individual student performance (e.g., Elo/IRT-Rasch style updates) to maintain a target accuracy band (e.g., 55–75%), with per-student ability and per-item difficulty parameters stored for future sessions.
Mathematical Validation (Mandatory): Integrate SymPy to verify numerical and symbolic correctness (e.g., simplification, differentiation/integration checks, algebraic equivalence, unit consistency where applicable). The system must auto-check the correct option and sanity-check distractors (e.g., typical sign/error traps).
Deliverables
A working application (CLI/GUI or Web-based)
Source code with documentation
Demo with sample input files (text, PDF, scanned image)
Demo with sample MCQ training to show similarity in generated MCQs
Duplicate detection report and UI flow for resolving/merging duplicates
Output: Word document with MCQs formatted in a 9-row, 1-column structure
Additional Features (Recommended)
Exam Paper Generation: Export full exam papers in Word and PDF formats with automatic answer keys, marking schemes, and randomized order of questions and options.
Question Bank Management: Maintain a central repository of generated MCQs, tagged by chapter, subtopic, difficulty, and concept type, with search and reuse capabilities.
Bloom’s Taxonomy Alignment: Classify MCQs according to Bloom’s levels (Knowledge, Comprehension, Application, Analysis, Evaluation, Creation) to support competency-based assessments.
Adaptive Testing Engine: Deliver personalized tests that dynamically adjust difficulty during the exam (CAT-style).
Export & Integration Options: Support exports to CSV/Excel for LMS uploads and SCORM-compliant packages for Moodle/Google Classroom.
Analytics & Reporting: Provide reports on difficulty distribution, topic coverage, and distractor statistics.
Version Control & Randomization: Generate multiple versions of the same exam with shuffled questions and answer keys.
Feedback-Enhanced Distractors: Include mini learning points with each distractor to reinforce correct concepts.
Plagiarism & Originality Check: Ensure originality of generated MCQs, avoiding direct duplication from textbooks or other sources.
User Interface / Deployment: Provide a Windows desktop application with an optional web dashboard for teachers, featuring GUI-based controls for difficulty levels and chapter selection.
Requirement Document: AI-Powered MCQ Generator (CBSE & Maharashtra Board Focus)
Objective
To develop an AI-powered Multiple Choice Question (MCQ) Generator that can create high-quality, exam-oriented questions in a standardized format compatible with Microsoft Word. The system should also be able to learn from sample MCQs provided by the user and generate questions in a similar style. The focus will be on the CBSE syllabus (Classes 5 to 10) as well as the Maharashtra State Board syllabus (Classes 5 to 10).
Key Features
Input Compatibility
Accept plain text (.txt)
Accept PDF files
Accept scanned pages/images with OCR support (.jpg, .png, .tif)
Must be able to correctly interpret mathematical symbols (integration, differentiation, geometry 2D/3D, etc.)
AI Integration
Utilize ChatGPT (OpenAI), Google AI (Gemini), or equivalent LLMs for:
Generating subtopics directly aligned with CBSE and Maharashtra Board textbooks
Creating MCQs with adjustable difficulty levels (Easy, Medium, Hard)
Ensuring questions are syllabus-aligned and exam-relevant
Generating MCQs in the same style/format as sample MCQs provided by the user
MCQ Generation Logic
Generate questions on one subtopic or combinations of subtopics
Quantity control: User can request n number of MCQs per run (e.g., 10, 50, 200, 1000+)
Difficulty control: User can set a defined difficulty level for the entire batch (Easy/Medium/Hard) or a mix distribution (e.g., 20% Easy, 60% Medium, 20% Hard)
Distractor options must be plausible, reflecting common misconceptions
Each distractor must have a specific logic/reason so that when a student selects a wrong option, the system can explain why that choice was incorrect
Maintain difficulty calibration based on user selection
Replicate tone, phrasing, and structure from sample MCQs
Output Format
Each MCQ should be exported into a Microsoft Word .docx file
Format: Homeworkstick 9-Row, 1-Column Table
Question Text
Option A
Option B
Option C
Option D
Correct Answer (only option letter)
Hint
Stepwise Detailed Explanation (including reasoning for distractors)
Subtopic Name(s)
Scalability
Support bulk generation (hundreds of MCQs per chapter)
Handle large syllabus content efficiently
Export on a per-chapter or per-subject basis
Examination Assembly: The system must be able to automatically create full examinations with a pre-defined number of questions, selected from different chapters and distributed across different difficulty levels as specified by the user.
Technical Requirements
Architecture: The MCQ Generator must be primarily AI/ML (LLM)-based, leveraging models such as ChatGPT or Google Gemini for generation. It must be augmented with rule-based validation to ensure format compliance and SymPy-based correctness checks for mathematical accuracy.
OCR: Tesseract OCR (baseline) with optional MathPix/Google Vision for complex math
Word Export: Python-docx or equivalent
Validation: Ensure schema compliance for MCQs (correct answer must match one option)
Platform: Windows-only (Windows 10/11)
Language: English-only
Style Adaptation: Use few-shot learning from user-provided sample MCQs to replicate formatting and phrasing
Distractor Logic: Each distractor must be tied to a misconception or error type (calculation mistake, conceptual misunderstanding, wrong formula, etc.)
Duplicate Question Detection (Mandatory): Detect and flag exact duplicates and near-duplicates before export, using a combination of text normalization, fuzzy matching, and semantic similarity (embeddings). Provide a review report listing suspected duplicates with similarity scores and suggested merges.
Adaptive Difficulty (Mandatory): Adjust MCQ difficulty dynamically based on individual student performance (e.g., Elo/IRT-Rasch style updates) to maintain a target accuracy band (e.g., 55–75%), with per-student ability and per-item difficulty parameters stored for future sessions.
Mathematical Validation (Mandatory): Integrate SymPy to verify numerical and symbolic correctness (e.g., simplification, differentiation/integration checks, algebraic equivalence, unit consistency where applicable). The system must auto-check the correct option and sanity-check distractors (e.g., typical sign/error traps).
Deliverables
A working application (CLI/GUI or Web-based)
Source code with documentation
Demo with sample input files (text, PDF, scanned image)
Demo with sample MCQ training to show similarity in generated MCQs
Duplicate detection report and UI flow for resolving/merging duplicates
Output: Word document with MCQs formatted in a 9-row, 1-column structure
Additional Features (Recommended)
Exam Paper Generation: Export full exam papers in Word and PDF formats with automatic answer keys, marking schemes, and randomized order of questions and options.
Question Bank Management: Maintain a central repository of generated MCQs, tagged by chapter, subtopic, difficulty, and concept type, with search and reuse capabilities.
Bloom’s Taxonomy Alignment: Classify MCQs according to Bloom’s levels (Knowledge, Comprehension, Application, Analysis, Evaluation, Creation) to support competency-based assessments.
Adaptive Testing Engine: Deliver personalized tests that dynamically adjust difficulty during the exam (CAT-style).
Export & Integration Options: Support exports to CSV/Excel for LMS uploads and SCORM-compliant packages for Moodle/Google Classroom.
Analytics & Reporting: Provide reports on difficulty distribution, topic coverage, and distractor statistics.
Version Control & Randomization: Generate multiple versions of the same exam with shuffled questions and answer keys.
Feedback-Enhanced Distractors: Include mini learning points with each distractor to reinforce correct concepts.
Plagiarism & Originality Check: Ensure originality of generated MCQs, avoiding direct duplication from textbooks or other sources.
User Interface / Deployment: Provide a Windows desktop application with an optional web dashboard for teachers, featuring GUI-based controls for difficulty levels and chapter selection.