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

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.

Questions this source addresses

• At which stages of the LLM life cycle, from development to deployment, can algorithmic bias enter educational applications?
• How might personalized LLM tutoring affect cognitive load management for different student populations?
• Why can tailoring an LLM's responses to individual learners be pedagogically desirable rather than a form of unfair bias?
• Why do traditional machine learning bias metrics fail to transfer to LLM-generated educational content?