ANSWER EVALUATION SYSTEM

Abstract

The Answer Evaluation System is an intelligent AIbased application designed to automate the evaluation of descriptive answers by analyzing their semantic meaning rather than relying solely on keyword matching . The system uses Natural Language Processing (NLP) techniques to process textual data efficiently and generate meaningful insights . It employs a pretrained SentenceTransformer model to convert textual answers into vector embeddings, enabling accurate comparison between student responses and predefined model answers . Cosine similarity is used as the primary metric to measure the closeness between answer representations and determine evaluation scores . The system is implemented using Python and integrated with libraries such as NumPy and Scikit-learn to support efficient computation and model performance. A Streamlitbased web interface allows users to input answers and receive real-time feedback and scores in an interactive manner . The system addresses limitations of traditional evaluation methods, which are time-consuming and prone to inconsistency due to human involvement . By providing instant and unbiased evaluation, it enhances efficiency in educational systems and online examination platforms . Additionally, the system supports scalability by handling large volumes of student responses with minimal computational overhead . The architecture includes modules for preprocessing, embedding generation, similarity computation, and result display to ensure a seamless workflow. Overall, the system improves accuracy, fairness, and speed in answer evaluation, making it a valuable tool for modern education systems .