A survey on automates grading of hand written examination answer scripts using machine learning and natural language processing

The evaluation of hand written examination answer scripts in education is traditionally performed by human evaluators, which can introduce bias, inconsistency, and significant delays, especially in large-scale assessments. Recent advances in Artificial Intelligence (AI), particularly Machine Learning (ML) and Natural Language Processing (NLP), have enabled automated systems capable of evaluating hand written examination answer scripts responses with considerable accuracy. The new system will leverage machine learning to analyze word and letter counts in student responses, enhancing efficiency and consistency. Additionally, it will use natural language processing to better understand the content of the answers, making the evaluation process smoother for educational institutions. Moreover, the system will utilize natural language processing (NLP) tools to gain deeper insights into the content of the answers. By understanding context, sentiment, and semantic meaning, it can evaluate the quality of reasoning and argumentation presented in student submissions. This will allow for a more nuanced assessment, considering factors like creativity and clarity, while reducing the likelihood of human error.