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.