FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applications

TL;DR

This paper provides a comprehensive review of fairness, bias, and ethics in educational AI, integrating technical fairness research with educational applications to address practical challenges and promote equitable AI-driven education.

Contribution

It offers a systematic, education-centered framework for understanding and mitigating biases in educational AI, bridging gaps between technical fairness research and practical educational needs.

Findings

Identifies sources of bias in educational AI systems

Analyzes fairness definitions and mitigation strategies

Highlights challenges in evaluating fairness in real-world educational contexts

Abstract

The integration of AI in education holds immense potential for personalizing learning experiences and transforming instructional practices. However, AI systems can inadvertently encode and amplify biases present in educational data, leading to unfair or discriminatory outcomes. As researchers have sought to understand and mitigate these biases, a growing body of work has emerged examining fairness in educational AI. These studies, though expanding rapidly, remain fragmented due to differing assumptions, methodologies, and application contexts. Moreover, existing surveys either focus on algorithmic fairness without an educational setting or emphasize educational methods while overlooking fairness. To this end, this survey provides a comprehensive systematic review of algorithmic fairness within educational AI, explicitly bridging the gap between technical fairness research and educational applications. We integrate multiple dimensions, including bias sources, fairness definitions, mitigation strategies, evaluation resources, and ethical considerations, into a harmonized, education-centered framework. In addition, we explicitly examine practical challenges such as censored or partially observed learning outcomes and the persistent difficulty in quantifying and managing the trade-off between fairness and predictive utility, enhancing the applicability of fairness frameworks to real-world educational AI systems. Finally, we outline an emerging pathway toward fair AI-driven education and by situating these technologies and practical insights within broader educational and ethical contexts, this review establishes a comprehensive foundation for advancing fairness, accountability, and inclusivity in the field of AI education.