Towards Responsible AI in Education: Hybrid Recommendation System for K-12 Students Case Study
It addresses fairness and equity issues in educational technology for K-12 students, though it appears incremental as it builds on existing recommendation methods with added bias mitigation.
This study tackled the problem of bias in AI-based recommendation systems for K-12 education by developing a hybrid system that combines graph-based modeling and matrix factorization to provide personalized suggestions for extracurricular activities, learning resources, and volunteering opportunities, while including a framework to detect and reduce biases across protected student groups.
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.