Need of AI in Modern Education: in the Eyes of Explainable AI (xAI)

TL;DR

This paper discusses the importance of explainable AI in modern education, highlighting biases related to parental income and emphasizing the need for fair, transparent AI solutions to improve educational equity.

Contribution

It explores the complexities and biases of AI decision-making in education using explainable AI tools, advocating for fairer and more accountable AI applications.

Findings

AI decisions are influenced by parental income disparities

Biases in AI can undermine fairness in education

Explainable AI helps uncover decision-making biases

Abstract

Modern Education is not \textit{Modern} without AI. However, AI's complex nature makes understanding and fixing problems challenging. Research worldwide shows that a parent's income greatly influences a child's education. This led us to explore how AI, especially complex models, makes important decisions using Explainable AI tools. Our research uncovered many complexities linked to parental income and offered reasonable explanations for these decisions. However, we also found biases in AI that go against what we want from AI in education: clear transparency and equal access for everyone. These biases can impact families and children's schooling, highlighting the need for better AI solutions that offer fair opportunities to all. This chapter tries to shed light on the complex ways AI operates, especially concerning biases. These are the foundational steps towards better educational policies, which include using AI in ways that are more reliable, accountable, and beneficial for everyone involved.