Bloom's Analyzer

Project Proposal to build an AI-Based Bloom's Taxonomy Knowledge Level Analyzer for Question Papers, covering all your requested points:

Project Proposal

AI-Based Bloom’s Taxonomy Knowledge Level Analyzer for Question Papers

1. Introduction to Bloom’s Taxonomy

Bloom’s Taxonomy is a widely accepted hierarchical classification of educational learning objectives. Developed by Benjamin Bloom in 1956 and revised in 2001, it categorizes cognitive skills into six ascending levels:

  1. Remembering – Recall facts and basic concepts

  2. Understanding – Explain ideas or concepts

  3. Applying – Use information in new situations

  4. Analyzing – Draw connections among ideas

  5. Evaluating – Justify a stand or decision

  6. Creating – Produce new or original work

This taxonomy aids educators in designing questions that evaluate different depths of knowledge and cognitive ability.

2. Need for an AI-Based Analyzer for Question Paper Setters

In traditional educational assessment, faculty often struggle to balance questions across Bloom’s levels. An AI-based Bloom’s Taxonomy Analyzer addresses this by:

  • Automatically classifying questions by cognitive level

  • Ensuring diverse question types for holistic assessment

  • Saving time for educators and quality auditors

  • Promoting outcome-based education and accreditation compliance (e.g., NAAC, NBA)

3. Objectives of the System

  • Analyze individual questions to determine the Bloom's Taxonomy level

  • Provide a report summarizing question distribution across levels

  • Allow batch processing of entire question papers

  • Recommend rebalancing strategies for improvement

  • Support multilingual and subject-specific nuances (optional extensions)

4. Process Involved

Step 1: Input Collection

  • Upload .docx, .pdf, .txt, or enter questions manually

  • Bulk import option for full papers

Step 2: Natural Language Processing (NLP)

  • Preprocessing: Tokenization, lemmatization, stop-word removal

  • Semantic analysis using transformer-based models (e.g., BERT or LLaMA)

  • Bloom verb identification and context evaluation

Step 3: Classification Engine

  • Trained AI model classifies questions into the correct Bloom level

  • Confidence score is provided per classification

Step 4: Output Generation

  • Dashboard view of question classification

  • Exportable reports (CSV, PDF)

  • Recommendations for improvement if imbalance is detected

5. System Architecture

Frontend (React or Angular):

  • Intuitive interface for uploading questions or entering them manually

  • Visualization of Bloom level distribution via charts

  • Responsive design for mobile/tablet access

Backend (Python – Flask/FastAPI with NLP pipeline):

  • AI engine for classification using pretrained transformer models

  • Bloom verb matching and contextual classification rules

  • REST APIs for interaction with frontend

Database (MongoDB / Firebase / PostgreSQL):

  • Stores user sessions, analyzed questions, results, and feedback

6. Features

Import Options

  • Upload file: PDF, Word (.docx), TXT

  • Copy-paste question sets

  • Bulk import via Excel template

Export Options

  • Classification report (PDF, Excel)

  • Bloom distribution charts

  • Individual question annotations

Additional Features (Optional)

  • User authentication for saving history

  • Institution dashboard for multiple teachers

  • Feedback loop to fine-tune model

  • Multilingual support for regional languages

7. Technology Stack

Component Technology
Frontend React / Angular, Tailwind / Bootstrap
Backend Python (Flask or FastAPI)
NLP Engine HuggingFace Transformers, LLaMA, BERT
Database MongoDB / PostgreSQL / Firebase
Reporting Chart.js, jsPDF, Pandas
Hosting Heroku / Vercel / Dockerized on VPS

8. Benefits and Impact

  • Supports Outcome-Based Education (OBE)

  • Improves assessment quality across subjects

  • Reduces workload for teachers and exam controllers

  • Makes NAAC/NBA audits easier with Bloom alignment reports

  • Encourages better question design pedagogy

9. Timeline

Phase Duration
Requirement Gathering 1 week
Model Design & Training 2 weeks
Frontend Development 1 week
Backend API Setup 1 week
Integration & Testing 1 week
Deployment & Training 1 week

10. Conclusion

This AI-based Bloom's Taxonomy Analyzer provides a practical solution for modern educators and academic institutions to ensure balanced, cognitive-skill-based assessment. By leveraging AI and NLP, this tool empowers institutions to uphold academic standards and promote deeper learning outcomes.


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