Large Language Models (LLMs) are increasingly adopted in educational contexts to provide personalized support to students and teachers. The unprecedented capacity of LLM-based applications to understand and generate natural language can potentially improve instructional effectiveness and learning outcomes, but the integration of LLMs in education technology has renewed concerns over algorithmic bias which may exacerbate educational inequities. In this review, building on prior work on mapping the traditional machine learning life cycle, we provide a holistic map of the LLM life cycle from the initial development of LLMs to customizing pre-trained models for various applications in educational settings. We explain each step in the LLM life cycle and identify potential sources of bias that may arise in the context of education. We discuss why current measures of bias from traditional machine learning fail to transfer to LLM-generated content in education, such as tutoring conversations because the text is high-dimensional, there can be multiple correct responses, and tailoring responses may be pedagogically desirable rather than unfair. This review aims to clarify the complex nature of bias in LLM applications and provide practical guidance for their evaluation to promote educational equity.

大型语言模型在教育环境中得到越来越广泛的应用，以为学生和教师提供个性化支持。然而，这些模型的整合引起了有关算法偏见的担忧，可能加剧教育不公平问题。本综述从传统机器学习生命周期的角度出发，提供了大型语言模型从初始开发到在教育应用中定制预训练模型的全面生命周期图。文章讨论了在教育背景下可能出现的偏见来源，并解释了为什么传统机器学习中的偏见度量无法迁移到教育中由大型语言模型生成的内容，因为文本是高维的，可能存在多个正确的回答，而定制回答可能是教学上合理而非不公平的。这篇综述旨在阐明大型语言模型应用中的复杂偏见现象，并为其评估提供实际指导，以促进教育公平。

大型语言模型的生命周期：教育中的偏见评审