Abstract

This document presents a in reference to the developments in AI and ML to make the grading and feedback process easier. Building machines which comprehend and react to text or voice data-and answer with text or speech from their own a manner closer to that of humans is the goal of natural language processing. Grading is a tiresome and time-consuming procedure, and it's frequently difficult to get regular feedbacks. So, to goal is to implement AI and machine learning algorithms to facilitate in grading and/or provide comments on submitted curriculum in the field of education and training using NLP. Modern computing necessitates sorting algorithms and their implementations to be more efficient in sorting huge data sets, both in terms of time and memory used. In order to choose an algorithm depending on the properties of the data set, this paper reviews a variety of adaptive sorting algorithms. By analyzing the trial data, machine learning enables us to build an adaptable algorithm