| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| - hi |
| - te |
| - ta |
| - mr |
| - bn |
| - gu |
| - kn |
| - pa |
| - ml |
| - or |
| - ar |
|
|
|
|
| pretty_name: Multimodal Educational Question Answering Dataset |
|
|
| size_categories: |
| - 1M<n<10M |
|
|
| tags: |
| - multimodal |
| - vision-language |
| - vlm |
| - llm |
| - image-text |
| - educational |
| - question-answering |
| - document-understanding |
| - stem |
| - non-stem |
| - reasoning |
| - chart-understanding |
| - diagram-understanding |
| - mathematical-reasoning |
| - rag |
| - instruction-tuning |
| - json |
| - pdf |
| - computer-vision |
| - ocr |
| --- |
| |
| # Multimodal Educational Question Answering Dataset |
|
|
| ## Overview |
|
|
| The **Multimodal Educational Question Answering Dataset** is a large-scale educational corpus designed for **Vision Language Models (VLMs), Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), document intelligence systems, OCR research, and multimodal reasoning applications.** |
|
|
| The dataset combines **text, images, mathematical equations, diagrams, charts, tables, and detailed explanations**, enabling AI systems to reason across both textual and visual information. |
|
|
| Unlike conventional QA datasets that contain only text, this dataset preserves educational illustrations, scientific figures, mathematical expressions, graphs, and document layouts, making it ideal for next-generation multimodal AI. |
|
|
| --- |
|
|
| # Dataset Highlights |
|
|
| | Property | Value | |
| |-----------|--------| |
| | Dataset Type | Multimodal Educational QA | |
| | Modalities | Text + Images + Equations + Tables + Charts | |
| | Languages | 12 | |
| | Formats | JSON & PDF | |
| | Image Support | ✅ | |
| | Mathematical Equations | LaTeX & MathML | |
| | Multiple Choice Questions | ✅ | |
| | Detailed Explanations | ✅ | |
| | AI Ready | ✅ | |
|
|
| --- |
|
|
| # Modalities Included |
|
|
| The dataset combines multiple information sources within a single sample. |
|
|
| - Text |
| - Educational Images |
| - Scientific Diagrams |
| - Flowcharts |
| - Tables |
| - Graphs |
| - Mathematical Formulae |
| - Chemical Structures |
| - Physics Diagrams |
| - Biology Illustrations |
| - OCR-readable Documents |
| - Structured Metadata |
|
|
| --- |
|
|
| # Supported Languages |
|
|
| - English |
| - Hindi |
| - Telugu |
| - Marathi |
| - Bengali |
| - Arabic |
| - Tamil |
| - Gujarati |
| - Kannada |
| - Punjabi |
| - Odia |
| - Malayalam |
|
|
| --- |
|
|
| # Subject Coverage |
|
|
| ## STEM |
|
|
| - Mathematics |
| - Physics |
| - Chemistry |
| - Biology |
| - Engineering |
| - Computer Science |
| - Information Technology |
| - Medical Sciences |
| - Environmental Science |
| - Agriculture |
| - General Science |
|
|
| ## Non-STEM |
|
|
| - History |
| - Geography |
| - Economics |
| - Business Studies |
| - Commerce |
| - Law |
| - Political Science |
| - Sociology |
| - Teaching |
| - Communication |
| - General Knowledge |
| - Languages |
|
|
| --- |
|
|
| # Sample Dataset Record |
|
|
| ```json |
| { |
| "question": "Identify the labeled part of the human heart shown in the diagram.", |
| "image": "heart_diagram.png", |
| "options": [ |
| "Left Atrium", |
| "Right Atrium", |
| "Left Ventricle", |
| "Aorta" |
| ], |
| "answer": "Aorta", |
| "explanation": "The labeled structure represents the main artery carrying oxygenated blood from the left ventricle." |
| } |
| ``` |
|
|
| --- |
|
|
| # Dataset Features |
|
|
| - Image-grounded question answering |
| - Multiple-choice questions |
| - Rich educational explanations |
| - Embedded diagrams |
| - Scientific illustrations |
| - Mathematical equations |
| - Charts and graphs |
| - OCR-compatible documents |
| - Structured JSON annotations |
| - Human-readable PDF references |
| - Vision-language learning ready |
| - Retrieval-friendly format |
|
|
| --- |
|
|
| # AI Tasks Supported |
|
|
| - Visual Question Answering (VQA) |
| - Multimodal Question Answering |
| - Document Understanding |
| - OCR |
| - Image Captioning |
| - Chart Understanding |
| - Table Understanding |
| - Diagram Reasoning |
| - Mathematical Reasoning |
| - Scientific Reasoning |
| - Retrieval-Augmented Generation (RAG) |
| - Instruction Tuning |
| - Supervised Fine-Tuning (SFT) |
| - Vision-Language Model Training |
|
|
| --- |
|
|
| # Potential Applications |
|
|
| - Vision Language Models (VLMs) |
| - Educational AI Tutors |
| - AI Teaching Assistants |
| - Intelligent OCR Systems |
| - Document AI |
| - Academic Search Engines |
| - Digital Libraries |
| - Interactive Learning Platforms |
| - Educational Chatbots |
| - AI Examination Systems |
| - Scientific Document Analysis |
| - Knowledge Retrieval Systems |
|
|
| --- |
|
|
| # Industries |
|
|
| - Artificial Intelligence |
| - Education Technology (EdTech) |
| - Higher Education |
| - Schools |
| - Research Organizations |
| - Digital Publishing |
| - Healthcare Education |
| - Government Education |
| - Scientific Computing |
| - Enterprise Knowledge Management |
|
|
| --- |
|
|
| # Dataset Structure |
|
|
| ``` |
| Multimodal_Dataset/ |
| │ |
| ├── PDFs/ |
| │ ├── Mathematics.pdf |
| │ ├── Biology.pdf |
| │ ├── Physics.pdf |
| │ └── ... |
| │ |
| ├── JSONs/ |
| │ ├── Mathematics.json |
| │ ├── Biology.json |
| │ ├── Physics.json |
| │ └── ... |
| │ |
| ├── Images/ |
| │ ├── diagrams/ |
| │ ├── charts/ |
| │ ├── tables/ |
| │ ├── figures/ |
| │ └── illustrations/ |
| ``` |
|
|
| --- |
|
|
| # Advantages |
|
|
| - Large-scale multimodal educational corpus |
| - Supports both text and vision models |
| - Rich reasoning annotations |
| - Multiple educational domains |
| - Mathematical notation preserved |
| - High-quality structured metadata |
| - Ready for enterprise AI workflows |
| - Compatible with Hugging Face, ModelScope, and PyTorch ecosystems |
|
|
| --- |
|
|
| # Recommended Models |
|
|
| This dataset is suitable for training or fine-tuning: |
|
|
| - Vision Language Models (VLMs) |
| - GPT-style Multimodal Models |
| - LLaVA |
| - Qwen-VL |
| - InternVL |
| - Florence |
| - BLIP-2 |
| - IDEFICS |
| - Kosmos |
| - MiniCPM-V |
| - OCR + LLM Pipelines |
|
|
| --- |
|
|
| # License |
|
|
| This dataset is released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** License. |
|
|
| Users are free to use, modify, distribute, and build upon the dataset with proper attribution. |
|
|
| --- |
|
|
| # Citation |
|
|
| ```bibtex |
| @dataset{multimodal_educational_qa, |
| title={Multimodal Educational Question Answering Dataset}, |
| year={2026}, |
| license={CC BY 4.0} |
| } |
| ``` |
|
|
| --- |
|
|
| # Conclusion |
|
|
| The **Multimodal Educational Question Answering Dataset** provides a comprehensive resource for developing intelligent AI systems capable of understanding both textual and visual educational content. By combining questions, answers, explanations, images, diagrams, tables, equations, and multilingual content, it supports a wide range of multimodal learning tasks, making it an ideal dataset for training state-of-the-art Vision Language Models, document AI systems, educational assistants, and retrieval-augmented generation pipelines. |
