This project focuses on the automated evaluation of subjective answers by utilizing a pretrained language model such as Gemini AI. The system takes a question, its correct answer, and a student’s answer as input. It then compares the student’s response with the correct answer using semantic understanding to generate a score. To ensure secure handling of user data and answers, the system uses cloud-based infrastructure and algorithms. This approach reduces manual grading efforts, improves scoring consistency. It is designed with modular components for user management, answer input, AI-based evaluation, and result display. By integrating machine learning with secure system design, the project offers a scalable and efficient method for evaluating descriptive answers in academic environments.

Keywords: subjective answer evaluation, machine learning, Gemini AI, semantic analysis, student assessment, score prediction, cloud computing, automated grading, NLP, data security.