为中小学生成立公平推荐系统，避免算法偏见影响学习机会
Towards Responsible AI in Education: Hybrid Recommendation System for K-12 Students Case Study
- 融合图模型与矩阵分解，个性化推荐课外活动与资源
- 通过分组反馈分析识别并降低对学生群体的算法偏见
- 适合关注教育公平与AI伦理的研究者与教育科技开发者
教育技术（EdTech）的兴起使基于人工智能的推荐系统能够为每位学生提供高度个性化的学习体验。然而，这些系统可能无意中引入偏见，限制学生公平获取学习资源的机会。本研究针对K-12学生设计了一种推荐系统，结合图建模与矩阵分解技术，为学生提供课外活动、学习资源及志愿机会的个性化推荐。为应对公平性问题，系统包含一个框架，通过分析受保护学生群体的反馈数据来检测和减轻偏见。研究强调，在教育推荐系统中持续监控算法公平性，对于保障所有学生的平等、透明且有效的学习机会至关重要。
原文摘要 · Abstract (English)
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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