AI-Powered Short-Answer Grading and Feedback System for Indian EdTech Platforms

Objective: To develop and validate an automated system for grading short-answer responses with personalized feedback for Indian EdTech platforms.

Indian EdTech platforms face challenges in scaling personalized assessment for millions of students, especially when grading subjective short-answer questions in regional and English-medium contexts. Manual grading is time-consuming, expensive, and inconsistent, limiting timely feedback that is critical for learning outcomes in large online and blended classrooms.

The project aims to design a machine learning-based solution using NLP models to automatically grade short-answer responses and generate constructive, rubric-aligned feedback. The approach involves collecting anonymized answer scripts from Indian EdTech providers, applying domain-specific NLP pre-processing, and training/finetuning models like BERT or IndicBERT for Indian language contexts. Feedback templates will be generated using sequence-to-sequence models and rule-based mapping to rubrics.

Deliverables include a working prototype for automated grading and feedback, rubric alignment analysis, accuracy and fairness evaluation, and a cost-benefit analysis compared to manual grading. The system will be validated on real datasets with educator review and statistical performance metrics (accuracy, F1, Cohen's kappa, feedback usefulness).

This solution enables EdTech companies and educational institutions to scale personalized assessment, reduce grading turnaround time, and improve student outcomes, informing product design, operational strategy, and resource allocation decisions.