NLP-Driven Approaches to Automated Essay Grading and Feedback
V. Nikhil, R Annamalai, Senthil Jayapal · 2025
This study explores the effectiveness of natural language processing (NLP) models in automated essay assessment and feedback generation, highlighting their potential to transform traditional grading by enhancing speed, consistency, and objectivity. Through analyzing metrics such as mean squared error (MSE), Cohen’s kappa, and response times, the findings reveal both the strengths and current limitations of automated essay scoring (AES) systems. While NLP-based assessments provide rapid, unbiased evaluations of content, grammar, and structure, they encounter challenges in interpreting nuanced language and creative expression. Comparative analyses with human grading demonstrate NLP’s advantages in efficiency and scalability, yet emphasize the need for integrating human oversight to capture the depth of contextual understanding and creativity that automated systems often miss. This study also addresses ethical considerations, including fairness, data privacy, and bias mitigation, advocating for the responsible deployment of NLP tools in educational environments. Future research directions focus on advancing semantic understanding, improving feedback quality, reducing bias, and incorporating multimodal assessment approaches to create a more robust and inclusive automated evaluation framework. This research contributes to a growing body of knowledge on NLP’s transformative role in educational assessment, with implications for improving learning outcomes and fostering equitable educational experiences.