Uzaib52/edubloom-question-bank

🎓 EduBloom: Academic Question Bank Dataset

📌 Overview

EduBloom Academic Question Bank Dataset is the official academic question dataset developed for EduBloom: Bloom's Taxonomy-Based Academic Intelligence Platform under NEP-2020.

The dataset is designed to support intelligent academic assessment systems, automated question-paper generation, semantic question retrieval, difficulty prediction, and cognitive-level classification.

It organizes academic questions according to Bloom's Taxonomy, enabling educational AI systems to generate balanced and outcome-oriented assessments across six cognitive levels:

Level	Bloom's Taxonomy	Cognitive Objective
L1	Remember	Recall facts, concepts, and basic information
L2	Understand	Explain, summarize, or interpret concepts
L3	Apply	Apply knowledge to practical situations
L4	Analyze	Examine relationships, patterns, and components
L5	Evaluate	Critically assess, justify, or compare solutions
L6	Create	Design, construct, or develop new solutions

The dataset is intended to support NEP-2020, Outcome-Based Education (OBE), and AI-assisted academic assessment workflows.

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🎯 Key Objectives

EduBloom's dataset can be used for:

• 🤖 Automated examination paper generation
• 🧠 Bloom's Taxonomy-based cognitive classification
• 📚 Academic question-bank management
• 🔎 Semantic question retrieval using vector databases
• 📊 Question difficulty prediction
• 📝 Intelligent assessment generation
• 🎓 Outcome-Based Education (OBE) alignment
• 🏫 AI-powered academic evaluation systems
• 🔗 Integration with RAG and LLM-based educational applications

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📊 Dataset Structure

The dataset contains structured academic questions with metadata that enables intelligent filtering, retrieval, classification, and assessment generation.

Data Fields

Field	Type	Description
question_id	String	Unique identifier for each question
question	String	Main academic question text
subject	String	Academic discipline such as Computer Science, Mathematics, Physics, or Economics
topic	String	Specific topic or sub-topic covered by the question
bloom_level	String	Bloom's Taxonomy cognitive level from L1 to L6
question_type	String	Question format such as Definition, Comparison, Analysis, Design, etc.
difficulty	String	Estimated difficulty level: Easy, Medium, or Hard

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🧩 Example Record

{
  "question_id": "Q001",
  "question": "Explain the basic concept of machine learning.",
  "subject": "Computer Science",
  "topic": "Machine Learning",
  "bloom_level": "L2",
  "question_type": "Explanation",
  "difficulty": "Easy"
}

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🚀 Quick Start

Install Dependencies

pip install datasets pandas

Load the Dataset

from datasets import load_dataset

# Load the EduBloom dataset from Hugging Face
dataset = load_dataset("Uzaib52/edubloom-question-bank")

# Access the training split
df = dataset["train"].to_pandas()

# Display sample records
print(df.head())

Explore the Dataset

# Dataset dimensions
print(df.shape)

# Available subjects
print(df["subject"].unique())

# Bloom's Taxonomy distribution
print(df["bloom_level"].value_counts())

# Difficulty distribution
print(df["difficulty"].value_counts())

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🔬 AI/ML Applications

The dataset can be integrated into an educational AI pipeline:

                    EduBloom Dataset
                           │
                           ▼
                  Data Preprocessing
                           │
                           ▼
                Question Classification
                           │
              ┌────────────┴────────────┐
              ▼                         ▼
       Bloom's Taxonomy          Difficulty Prediction
              │                         │
              └────────────┬────────────┘
                           ▼
                  Semantic Embeddings
                           │
                           ▼
                     ChromaDB / RAG
                           │
                           ▼
                  Question Retrieval
                           │
                           ▼
                AI Question Generation
                           │
                           ▼
              Automated Question Paper

Potential Technology Stack

• Python
• Pandas
• Scikit-learn
• TensorFlow / Keras
• PyTorch
• Sentence Transformers
• ChromaDB
• LangChain
• FastAPI
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)

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🧠 Bloom's Taxonomy Integration

EduBloom uses the six cognitive levels of Bloom's Taxonomy to create structured and meaningful assessments.

L1 — Remember

Focuses on recalling facts, definitions, terminology, and basic concepts.

L2 — Understand

Measures the ability to explain, interpret, summarize, or describe concepts.

L3 — Apply

Evaluates the ability to use knowledge in practical or problem-solving situations.

L4 — Analyze

Measures the ability to break information into components and identify relationships.

L5 — Evaluate

Focuses on critical judgment, comparison, justification, and decision-making.

L6 — Create

Measures the ability to design, develop, construct, or propose new solutions.

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🎓 Academic Context

EduBloom is designed around modern academic assessment principles, particularly:

• National Education Policy (NEP-2020)
• Outcome-Based Education (OBE)
• Bloom's Taxonomy
• AI-Assisted Assessment
• Automated Question Paper Generation
• Personalized Learning
• Data-Driven Education

The structured dataset enables academic platforms to move beyond simple question storage toward intelligent assessment generation and analysis.

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🔗 Integration with EduBloom

This dataset can be integrated into the broader EduBloom Academic Intelligence Platform for:

1. Question-bank management
2. Question classification
3. Bloom's Taxonomy mapping
4. Difficulty prediction
5. Semantic search
6. Question recommendation
7. Automated paper generation
8. AI-powered academic workflows

Dataset

Hugging Face: https://huggingface.co/datasets/Uzaib52/edubloom-question-bank

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📈 Future Scope

Potential future improvements include:

• Multilingual academic questions
• Automated Bloom's Taxonomy classification
• Advanced difficulty prediction
• Question quality scoring
• Duplicate question detection
• Semantic similarity analysis
• Curriculum and syllabus mapping
• CLO/PLO alignment
• Personalized question recommendation
• LLM-assisted question generation
• RAG-based academic assistants
• Automated question-paper balancing

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📝 Citation

If you use this dataset or reference the EduBloom system in an academic project, research paper, presentation, or evaluation, please cite:

@software{EduBloom2026,
  author = {
    Sharma, Pushkar and
    Saiyad, Uzaib and
    Sable, Rajesh
  },
  title = {
    EduBloom: Bloom's Taxonomy-Based Academic Intelligence Platform
  },
  year = {2026},
  publisher = {Hugging Face},
  url = {
    https://huggingface.co/datasets/Uzaib52/edubloom-question-bank
  }
}

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📄 License

This dataset is distributed under the MIT License.

See the LICENSE file for complete license information.

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👨‍💻 Project Team

EduBloom — Bloom's Taxonomy-Based Academic Intelligence Platform

Team Members:

• Uzaib Saiyad
• Pushkar Sharma
• Rajesh Sable

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⭐ Acknowledgement

EduBloom demonstrates how Artificial Intelligence, Machine Learning, Natural Language Processing, and educational data can be combined to build intelligent academic assessment systems aligned with modern educational frameworks.

If you find this dataset useful, consider starring ⭐ the repository and sharing the project.

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🔗 Resources

• Dataset: https://huggingface.co/datasets/Uzaib52/edubloom-question-bank
• Project: EduBloom Academic Intelligence Platform
• License: MIT