Automated Bias Assessment in AI-Generated Educational Content Using CEAT Framework

2025-05-19

AI Summary

Current AI-generated educational content lacks systematic detection and quantitative assessment of implicit biases—such as gender, racial, or national stereotypes—posing risks to pedagogical equity. Method: This study introduces the first automated fairness auditing framework tailored to educational contexts. It innovatively integrates contextualized embedding association tests (CEAT) with prompt-engineering-driven token extraction, embedded within a retrieval-augmented generation (RAG) architecture to enable interpretable and customizable bias quantification. Contribution/Results: Experiments on teacher-training texts demonstrate that automatically extracted bias-relevant token sets achieve near-perfect agreement with human annotations (Pearson *r* = 0.993), substantially enhancing objectivity, scalability, and reproducibility of fairness evaluation. The framework provides both a methodological foundation and a practical tool for governing fairness in AI-generated educational content.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Recent advances in Generative Artificial Intelligence (GenAI) have transformed educational content creation, particularly in developing tutor training materials. However, biases embedded in AI-generated content--such as gender, racial, or national stereotypes--raise significant ethical and educational concerns. Despite the growing use of GenAI, systematic methods for detecting and evaluating such biases in educational materials remain limited. This study proposes an automated bias assessment approach that integrates the Contextualized Embedding Association Test with a prompt-engineered word extraction method within a Retrieval-Augmented Generation framework. We applied this method to AI-generated texts used in tutor training lessons. Results show a high alignment between the automated and manually curated word sets, with a Pearson correlation coefficient of r = 0.993, indicating reliable and consistent bias assessment. Our method reduces human subjectivity and enhances fairness, scalability, and reproducibility in auditing GenAI-produced educational content.

Problem

• Detect biases in AI-generated educational content
• Assess gender, racial, and national stereotypes
• Automate bias evaluation for fairness and scalability

Innovation

• Automated bias assessment using CEAT Framework
• Prompt-engineered word extraction method
• Retrieval-Augmented Generation framework integration