Automatic answer checker application

Traditional methods of grading descriptive answers are often time-consuming, inconsistent, and prone to human error. The shift to online education during the COVID-19 pandemic further highlighted the need for more efficient and reliable assessment solutions. To address these challenges, this project developed an Automatic Answer Checker application that uses Natural Language Processing (NLP) to automate the grading of multiple-choice, short, and coding answers. The system applies similarity matching using CodeBERT and FuzzyWuzzy to compare student responses with model answers and award fair, partial, or full marks based on defined thresholds. Developed using Agile principles, the application integrates an Android mobile interface, a Python-based NLP backend, and secure cloud storage with Firebase. Real testing showed that the system effectively reduces grading time, improves consistency, and provides instant feedback for students and teachers. This will not only automate grading but also overcome major limitations of the traditional approaches with higher accuracy, efficiency, and security. The project modernizes educational technology for better engagement and convenience to both educators and students by automating assessments through NLP. This will go a long way in contributing toward improvement in online learning and enhancement in general efficiency related to assessment.