Automated Bias Assessment in AI-Generated Educational Content Using CEAT Framework
Project Overview
The document explores the advancements in Generative Artificial Intelligence (GenAI) and its transformative applications in education, particularly in developing tutor training materials. It addresses significant ethical concerns regarding biases in AI-generated content, including issues related to gender and racial stereotypes. To tackle these challenges, the study introduces an innovative automated bias assessment method, employing the Contextualized Embedding Association Test (CEAT) within a Retrieval-Augmented Generation (RAG) framework. The findings reveal a strong correlation between automated and manual bias assessments, demonstrating the effectiveness, reliability, and scalability of this method for evaluating biases in educational materials. Overall, the document underscores the potential of GenAI in enhancing educational resources while emphasizing the necessity of ethical considerations in its implementation.
Key Applications
Automated Bias Assessment using CEAT framework
Context: Assessment of AI-generated educational content for tutor training
Implementation: Integration of CEAT with prompt-engineered word extraction within a RAG framework
Outcomes: High alignment between automated bias assessment and manually curated word sets; enhanced fairness, scalability, and reproducibility in bias auditing.
Challenges: Initial reliance on limited datasets and the need for broader validation across various educational contexts.
Implementation Barriers
Ethical Concerns and Bias Mitigation
Biases embedded in AI-generated content can reinforce harmful stereotypes, compromise educational equity, and the current approach focuses solely on bias detection without addressing how to mitigate identified biases.
Proposed Solutions: Automated bias detection and assessment methods to proactively identify and address biases in educational materials, along with exploration of bias mitigation strategies in model training phases and post-training interventions.
Implementation Limitations
Current validation relies on a limited dataset of AI-generated texts, limiting generalizability.
Proposed Solutions: Broader validation across various contexts and larger-scale case studies in real classroom settings.
Project Team
Jingyang Peng
Researcher
Wenyuan Shen
Researcher
Jiarui Rao
Researcher
Jionghao Lin
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Jingyang Peng, Wenyuan Shen, Jiarui Rao, Jionghao Lin
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai