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

AI systems in education reflect and amplify existing inequalities: The research found that AI models make decisions strongly influenced by parental income, meaning these systems can perpetuate educational disadvantages for lower-income families rather than helping to level the playing field.

Complex AI decisions need transparency tools to be trustworthy: The study used Explainable AI (xAI) tools to understand how AI reaches its conclusions about students, demonstrating that we cannot responsibly deploy AI in education without being able to explain why it makes specific decisions about individual learners.

Hidden biases in educational AI can harm vulnerable populations: The researchers discovered biases within AI systems that contradict core educational values like equal access and transparency, with potential consequences for how families and children experience schooling—suggesting current AI tools may not be ready for widespread educational use.

Explainability is a necessary first step, not a complete solution: While the paper uses xAI to uncover problems, it acknowledges these tools reveal complexities rather than solve them, indicating that understanding AI bias is foundational work needed before developing fairer educational AI systems.

Educational AI requires accountability frameworks beyond technical fixes: The authors call for "reliable, accountable, and beneficial" AI solutions, implying that technical improvements alone are insufficient—policymakers and educators need governance structures to ensure AI serves all students equitably.