Overview
An intelligent assessment automation system developed by a four-member R&D team in response to the institute’s transition to autonomous status. The platform automates question paper generation based on predefined assessment criteria, supported by an AI/ML pipeline for classifying 16,200+ questions across six Bloom’s Taxonomy levels using fine-tuned transformer models.
About this project
Following the institute’s transition to autonomous status, AutoQPG was developed to automate question paper generation against predefined assessment criteria. As part of a four-member R&D team, I worked on the AI/ML component, building the question-classification pipeline that supports structured, taxonomy-driven assessment generation.
Contributions
- Developed the AI/ML component of a larger automated question paper generation system designed to support assessment workflows at an autonomous institute.
- Curated and preprocessed a dataset of 16,200+ questions from multiple educational sources using data cleaning, tokenization, and augmentation.
- Fine-tuned RoBERTa to classify questions across six Bloom’s Taxonomy levels, comparing its performance against BERT and DistilBERT baselines.
- Achieved 93.7% classification accuracy through model optimization and hyperparameter tuning.
- Integrated the ONNX-optimized classification model with a FastAPI backend to support the wider question paper generation workflow.
Model performance & workflow impact
- Classification Accuracy
93.7%
Achieved by the fine-tuned RoBERTa model across six Bloom’s Taxonomy levels.
- Manual Classification Effort
95% reduction
Reported reduction through automation of taxonomy-driven question classification.