  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.
