Adaptive AI MCQ Generator
Budget: ₹1,500 – ₹12,500 INR
I need a complete solution that automatically creates well-structured multiple-choice questions across Science, Mathematics, History, English, Computer Science, Environmental Science, General Knowledge, and Current Affairs.
The underlying approach is flexible—rule-based logic, classic machine-learning models, modern NLP techniques, or a thoughtful blend of these are all acceptable as long as the end result is accurate, varied, and contextually sound.
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
1. 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.)
2. 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
3. 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
4. Output Format
- Each MCQ should be exported into a Microsoft Word .docx file
- Format: Homeworkstick 9-Row, 1-Column Table
1. Question Text
2. Option A
3. Option B
4. Option C
5. Option D
6. Correct Answer (only option letter)
7. Hint
8. Stepwise Detailed Explanation (including reasoning for distractors)
9. Subtopic Name(s)
5. 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.
The underlying approach is flexible—rule-based logic, classic machine-learning models, modern NLP techniques, or a thoughtful blend of these are all acceptable as long as the end result is accurate, varied, and contextually sound.
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
1. 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.)
2. 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
3. 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
4. Output Format
- Each MCQ should be exported into a Microsoft Word .docx file
- Format: Homeworkstick 9-Row, 1-Column Table
1. Question Text
2. Option A
3. Option B
4. Option C
5. Option D
6. Correct Answer (only option letter)
7. Hint
8. Stepwise Detailed Explanation (including reasoning for distractors)
9. Subtopic Name(s)
5. 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.