Towards Responsible AI in Education: Hybrid Recommendation System for K-12 Students Case Study

TL;DR

This paper develops a hybrid AI recommendation system for K-12 education that personalizes learning resources while actively addressing bias and fairness to promote equitable access for all students.

Contribution

It introduces a novel hybrid recommendation framework combining graph-based modeling and matrix factorization with bias detection and mitigation for educational settings.

Findings

Effective bias detection and reduction in recommendations

Enhanced personalization for diverse student groups

Framework supports ongoing fairness monitoring

Abstract

The growth of Educational Technology (EdTech) has enabled highly personalized learning experiences through Artificial Intelligence (AI)-based recommendation systems tailored to each student needs. However, these systems can unintentionally introduce biases, potentially limiting fair access to learning resources. This study presents a recommendation system for K-12 students, combining graph-based modeling and matrix factorization to provide personalized suggestions for extracurricular activities, learning resources, and volunteering opportunities. To address fairness concerns, the system includes a framework to detect and reduce biases by analyzing feedback across protected student groups. This work highlights the need for continuous monitoring in educational recommendation systems to support equitable, transparent, and effective learning opportunities for all students.