Natural Language Processing Techniques for Enhancing Automated Essay Scoring Systems

In this experimental work, we explore how Automated Essay Scoring (AES) systems can be improved through the use of sophisticated Natural Language Processing (NLP) approaches. Discretion and impartiality are becoming more and more important, especially when educational institutions are depending more and more on automatic technologies to assess essays. This study examines the most recent advances in natural language processing (NLP), including syntactic parsing, deep literacy algorithms, and semantic analysis, and assesses their applicability to the ongoing refinement of AES systems. The research specifically focuses on evaluating the utility of various approaches. This work aims to address issues related to environment knowledge, consonance evaluation, and bias reduction in automated scoring using these techniques. These techniques will be integrated to achieve this. The results show that the application of sophisticated NLP techniques may produce new, valid, and trustworthy assessment problems. This could be one of the main causes of the widespread AES abandonment in educational environments. This work opens the possibility that natural language processing (NLP) could be used to change the essay score by offering a more thorough and nuanced assessment of student notes. This would have the effect of improving every facet of the teaching process.