mlsanityData to 14 Oct 2026

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

arXiv 2502.20354 ↗First posted 27 Feb 2025cs.AI cs.CLcited by 0

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Abstract (arXiv)

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.

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References · 15

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  2. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
  3. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
  4. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  5. 2018A comparative study of fairness-enhancing interventions in machine learningcited by 208
  6. 2021Trustworthy AI: From Principles to Practicescited by 207
  7. 2020A Survey on Knowledge Graph-Based Recommender Systemscited by 170
  8. 2018Aequitas: A Bias and Fairness Audit Toolkitcited by 147
  9. 2019On the Apparent Conflict Between Individual and Group Fairnesscited by 126
  10. 2017Multisided Fairness for Recommendationcited by 70
  11. 2021Graph Learning based Recommender Systems: A Reviewcited by 59
  12. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
3 more
  1. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  2. 2023A Survey on Fairness-aware Recommender Systemscited by 14
  3. 2022Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrinescited by 10

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