Fairness of Large Language Models in Education

Abstract

The paper investigates the fairness of Large Language Models (LLMs) in education. It discusses the transformative impact of LLMs on teaching and learning practices, highlighting their potential biases and emphasizing the necessity for fairness. It categorizes the applications of AI in education and outlines strategies for improving fairness in LLMs through pre-processing, in-processing, and post-processing techniques. The conclusion advocates for a multidimensional strategy in leveraging AI's potential in education, ensuring a balance between technological advancements and a steadfast commitment to inclusivity and fairness.

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