Beyond Traditional Assessment: Exploring the Impact of Large Language Models on Grading Practices

This paper examines how large language models can be integrated into educational grading practices, analyzing their potential to automate assessment, provide personalized feedback, and improve consistency while addressing challenges like bias, data privacy, and ethical concerns in automated grading systems.

their advanced capabilities in natural language processing and machine learning, which are pivotal in understanding and evaluating student responses. We delve into the mechanisms by which LLMs process, analyze, and grade a wide range of responses, from short answers to complex essays, highlighting their ability to provide detailed feedback and insights beyond mere correctness. The core of the discussion revolves around real-world applications and case studies in which LLMs have been implemented

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