  The integration of Artificial Intelligence (AI) into education has
transformative potential, providing tailored learning experiences and creative
instructional approaches. However, the inherent biases in AI algorithms hinder
this improvement by unintentionally perpetuating prejudice against specific
demographics, especially in human-centered applications like education. This
survey delves deeply into the developing topic of algorithmic fairness in
educational contexts, providing a comprehensive evaluation of the diverse
literature on fairness, bias, and ethics in AI-driven educational applications.
It identifies the common forms of biases, such as data-related, algorithmic,
and user-interaction, that fundamentally undermine the accomplishment of
fairness in AI teaching aids. By outlining existing techniques for mitigating
these biases, ranging from varied data gathering to algorithmic fairness
interventions, the survey emphasizes the critical role of ethical
considerations and legal frameworks in shaping a more equitable educational
environment. Furthermore, it guides readers through the complexities of
fairness measurements, methods, and datasets, shedding light on the way to bias
reduction. Despite these gains, this survey highlights long-standing issues,
such as achieving a balance between fairness and accuracy, as well as the need
for diverse datasets. Overcoming these challenges and ensuring the ethical and
fair use of AI's promise in education call for a collaborative,
interdisciplinary approach.
