CK-12 TQA Multimodal is a comprehensive multimodal dataset for science education, containing 26,260 questions paired with 6,206 images from middle school science textbooks. This dataset is sourced from CK-12 Foundation's open educational resources and includes both text-only questions and diagram-based visual reasoning questions.

This is the complete multimodal version with all images properly organized and mapped to questions, ready for training vision-language models, visual question answering systems, and multimodal educational AI.

Dataset Highlights:

🖼️ 6,206 images across 4 types (question diagrams, labeled diagrams, teaching images, textbook figures)
📊 26,260 questions (12,567 require diagrams, 13,693 are text-only)
📚 1,076 lessons from Life Science, Earth Science, and Physical Science
🎯 Proper train/validation/test splits with images organized by split
🔬 Middle school level (grades 6-8) science content
✅ Images verified and mapped to questions with metadata

Original Source

Paper: Are You Smarter Than A Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension (CVPR 2017)
Authors: Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi, Ali Farhadi, Hannaneh Hajishirzi
Content: CK-12 Foundation open-source science textbooks
License: CC BY-NC 3.0

Supported Tasks

Visual Question Answering (VQA): Answer questions about scientific diagrams
Multimodal Reasoning: Combine text and visual information
Question Answering: Text-based comprehension
Science Education: Middle school science assessment
Diagram Understanding: Interpret charts, graphs, and scientific illustrations

Dataset Structure

Data Organization

ck12_tqa_multimodal/
├── ck12_tqa_train.jsonl          # 15,154 questions
├── ck12_tqa_validation.jsonl     # 5,309 questions
├── ck12_tqa_test.jsonl           # 5,797 questions
├── dataset_manifest.json         # Dataset statistics
└── images/
    ├── train/
    │   ├── question_images/      # 1,077 diagram question images
    │   ├── abc_question_images/  # 422 labeled diagram images
    │   ├── teaching_images/      # 185 instructional diagrams
    │   └── textbook_images/      # 1,972 textbook figures
    ├── validation/
    │   ├── question_images/      # 450 images
    │   ├── abc_question_images/  # 140 images
    │   ├── teaching_images/      # 60 images
    │   └── textbook_images/      # 593 images
    └── test/
        ├── question_images/      # 516 images
        ├── abc_question_images/  # 144 images
        ├── teaching_images/      # 31 images
        └── textbook_images/      # 616 images

Data Format

Each question includes complete metadata and image mappings:

Non-Diagram Question:

{
  "id": "NDQ_000046",
  "source": "TQA-CK12",
  "split": "train",
  "lesson_id": "L_0002",
  "lesson_name": "earth science and its branches",
  "has_diagram": false,
  "instruction": "Answer the following multiple choice question from a science textbook.",
  "input": "Earth science is the study of\n\nOptions:\na) solid Earth.\nb) Earths oceans.\nc) Earths atmosphere.\nd) all of the above",
  "output": "d",
  "question_type": "Multiple Choice",
  "question_subtype": "Multiple Choice",
  "options": ["solid Earth.", "Earths oceans.", "Earths atmosphere.", "all of the above"],
  "option_labels": ["a", "b", "c", "d"]
}

Diagram Question (with image):

{
  "id": "DQ_000001",
  "source": "TQA-CK12",
  "split": "train",
  "lesson_id": "L_0003",
  "lesson_name": "erosion and deposition by flowing water",
  "has_diagram": true,
  "instruction": "Answer the following multiple choice question about the diagram from a science textbook.",
  "input": "How many actions are depicted in the diagram?\n\nOptions:\na) 6\nb) 4\nc) 8\nd) 7",
  "output": "d",
  "question_type": "Diagram Multiple Choice",
  "question_subtype": "",
  "image_path": "question_images/erosion_6843.png",
  "image_name": "erosion_6843.png",
  "options": ["6", "4", "8", "7"],
  "option_labels": ["a", "b", "c", "d"]
}

Data Fields

id: Unique identifier (DQ_* = diagram question, NDQ_* = non-diagram)
source: "TQA-CK12"
split: "train", "validation", or "test"
lesson_id: Lesson identifier
lesson_name: Topic name
has_diagram: Boolean indicating if image is required
instruction: Model instruction prompt
input: Question text with formatted options
output: Correct answer (letter: a, b, c, d, etc.)
question_type: "Multiple Choice", "True/False", etc.
question_subtype: Additional categorization
options: List of answer choices
option_labels: List of choice labels
image_path: Relative path to image (for diagram questions)
image_name: Image filename (for diagram questions)

Image Types

1. Question Images (`question_images/`)

Purpose: Diagrams directly referenced in questions
Count: 2,043 images total
Usage: Required to answer diagram questions
Examples: Scientific diagrams, charts, illustrations

2. ABC Question Images (`abc_question_images/`)

Purpose: Labeled versions of diagrams with letter markers
Count: 706 images total
Usage: Questions about specific labeled parts
Examples: Diagrams with A, B, C, D labels

3. Teaching Images (`teaching_images/`)

Purpose: Instructional diagrams with detailed descriptions
Count: 276 images total
Usage: Supporting visual content
Examples: Concept illustrations, process diagrams

4. Textbook Images (`textbook_images/`)

Purpose: Figures from textbook lessons
Count: 3,181 images total
Usage: Contextual illustrations
Examples: Photos, drawings, charts from lessons

Data Splits

Split	Lessons	Questions	Non-Diagram	Diagram	Images
Train	666	15,154	8,653	6,501	3,656
Validation	200	5,309	2,528	2,781	1,243
Test	210	5,797	2,512	3,285	1,307
TOTAL	1,076	26,260	13,693	12,567	6,206

Split Strategy: Splits are created at the lesson level to minimize concept overlap between train/val/test sets. Related lessons are grouped together to prevent data leakage.

Usage

Loading the Dataset

from datasets import load_dataset
from PIL import Image
import json

# Load questions
dataset = load_dataset("notefill/ck12-tqa-multimodal", data_files="ck12_tqa_train.jsonl")

# Load a question with image
with open("ck12_tqa_train.jsonl", "r") as f:
    for line in f:
        q = json.loads(line)
        if q["has_diagram"]:
            # Construct image path
            img_path = f"images/{q['split']}/{q['image_path']}"
            # Load image
            image = Image.open(img_path)
            print(f"Question: {q['input']}")
            print(f"Answer: {q['output']}")
            image.show()
            break

Filter by Image Type

import json

# Load only diagram questions
diagram_questions = []
with open("ck12_tqa_train.jsonl", "r") as f:
    for line in f:
        q = json.loads(line)
        if q["has_diagram"]:
            diagram_questions.append(q)

print(f"Found {len(diagram_questions)} diagram questions")

Vision-Language Model Training

from transformers import VisionEncoderDecoderModel, AutoTokenizer, AutoFeatureExtractor
from PIL import Image
import json

# Load model (example with BLIP, ViLT, or similar)
model = VisionEncoderDecoderModel.from_pretrained("your-model")
tokenizer = AutoTokenizer.from_pretrained("your-model")
feature_extractor = AutoFeatureExtractor.from_pretrained("your-model")

# Prepare data
def prepare_multimodal_data(jsonl_file, image_dir):
    data = []
    with open(jsonl_file, "r") as f:
        for line in f:
            q = json.loads(line)
            if q["has_diagram"]:
                img_path = f"{image_dir}/{q['split']}/{q['image_path']}"
                image = Image.open(img_path).convert("RGB")
                data.append({
                    "image": image,
                    "question": q["input"],
                    "answer": q["output"],
                    "instruction": q["instruction"]
                })
    return data

train_data = prepare_multimodal_data("ck12_tqa_train.jsonl", "images")

Evaluation

def evaluate_vqa_model(model, test_data):
    """Evaluate visual question answering accuracy"""
    correct = 0
    total = 0
    
    for item in test_data:
        if item["has_diagram"]:
            # Load image
            img_path = f"images/{item['split']}/{item['image_path']}"
            image = Image.open(img_path)
            
            # Get prediction
            prediction = model.predict(image, item["input"])
            
            if prediction.strip().lower() == item["output"].strip().lower():
                correct += 1
            total += 1
    
    return correct / total if total > 0 else 0.0

Subject Coverage

Life Science (Biology)

Cell structure and function
Genetics and heredity
Ecology and ecosystems
Evolution and adaptation
Human body systems
Plants and animals

Earth Science

Geology and rock cycle
Plate tectonics
Weather and climate
Oceans and water cycle
Earth's atmosphere
Astronomy and solar system

Physical Science

Matter and its properties
Chemical reactions
Forces and motion
Energy forms and transfer
Waves and sound
Electricity and magnetism

Benchmark Performance

This dataset is designed for challenging multimodal comprehension. Key difficulty factors:

Visual Reasoning: Requires interpreting scientific diagrams
Multi-step Reasoning: Many questions need multiple inferences
Domain Knowledge: Requires middle school science knowledge
Diagram Complexity: Varying levels of visual complexity

Considerations for Using the Data

Recommended Uses

✅ Training vision-language models for education
✅ Evaluating multimodal reasoning capabilities
✅ Building educational AI tutoring systems
✅ Research in visual question answering
✅ Developing diagram understanding systems
✅ Science education assessment tools

Limitations

⚠️ Grade Level: Limited to middle school (ages 11-14)
⚠️ Subject Scope: Only Life, Earth, and Physical Science
⚠️ Language: English only
⚠️ Image Quality: Varies by source
⚠️ Answer Format: Multiple choice only
⚠️ Non-Commercial: License restricts commercial use

Ethical Considerations

Dataset designed for research and non-commercial educational purposes
Content sourced from CK-12's open educational resources
Should be used to improve educational access and equity
Human oversight recommended for student-facing applications
Consider diverse student needs when deploying AI systems

Citation

Original TQA Dataset

@inproceedings{Kembhavi2017tqa,
  title={Are You Smarter Than A Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension},
  author={Aniruddha Kembhavi and Minjoon Seo and Dustin Schwenk and Jonghyun Choi and Ali Farhadi and Hannaneh Hajishirzi},
  booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017}
}

This Multimodal Version

@dataset{ck12_tqa_multimodal2025,
  title={CK-12 TQA Multimodal: Textbook Question Answering with Images},
  author={Kuyeso Rogers and Adiza Alhassan and Notefill},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/notefill/ck12-tqa-multimodal}},
  note={Complete multimodal dataset with images from CK-12 textbooks}
}

Licensing

Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)

You are free to:

✅ Share: Copy and redistribute the material
✅ Adapt: Remix, transform, and build upon the material

Under the following terms:

📝 Attribution: Credit CK-12 Foundation and TQA authors
❌ NonCommercial: No commercial use without permission

For commercial licensing, contact CK-12 Foundation.

Additional Resources

Original TQA: allenai.org/data/tqa
CK-12 Foundation: www.ck12.org
Paper (CVPR 2017): TQA Paper
Allen AI: allenai.org

Acknowledgments

We gratefully acknowledge:

CK-12 Foundation for creating and freely distributing world-class science educational materials under open licenses
Original TQA Authors: Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi, Ali Farhadi, and Hannaneh Hajishirzi for their groundbreaking work on multimodal machine comprehension
Allen Institute for AI (AI2) for hosting and maintaining the original dataset
The educators and content creators who contributed to CK-12's science curriculum

About CK-12 Foundation

CK-12 Foundation is a non-profit organization providing free, high-quality K-12 STEM content. Their mission is to reduce the cost of textbook materials and increase access to quality education worldwide through openly-licensed, customizable content.

Dataset Statistics

{
  "total_questions": 26260,
  "total_images": 6206,
  "total_lessons": 1076,
  "diagram_questions": 12567,
  "text_questions": 13693,
  "image_types": 4,
  "subjects": ["Life Science", "Earth Science", "Physical Science"],
  "grade_level": "Middle School (6-8)",
  "languages": ["English"]
}

Contact

For questions about this multimodal version, please open an issue on the dataset repository.

For questions about the original TQA dataset, visit allenai.org/data/tqa.

For CK-12 content and licensing inquiries, visit www.ck12.org.

Downloads last month: 197

You need to agree to share your contact information to access this dataset

CK-12 TQA Multimodal: Textbook Question Answering with Images

Dataset Description

Dataset Summary