The Life Cycle of Large Language Models: A Review of Biases in Education

TL;DR

This paper reviews the entire life cycle of large language models in education, highlighting sources of bias and discussing challenges in evaluating fairness to promote equitable educational outcomes.

Contribution

It provides a comprehensive map of the LLM life cycle in education, identifying bias sources and explaining why traditional bias measures may not apply.

Findings

Current bias measures often fail for LLM-generated educational content.

Bias can arise at each stage of the LLM life cycle in education.

Understanding bias sources helps improve fairness in educational LLM applications.

Abstract

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.