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AI in K-12 Education? Recommendation Systems in Learning -Robots Talking EP 6
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This research explores the use of AI-driven recommendation systems in K-12 education, aiming to personalize learning experiences. The study introduces a hybrid system that combines graph-based modeling and matrix factorization to suggest extracurriculars, resources, and volunteer opportunities. A key focus is addressing fairness by detecting and mitigating biases across different student groups. SoftServe Inc. developed this system for Mesquite Independent School District to enhance student engagement while adhering to Responsible AI principles. The authors implemented a fairness audit procedure and monitored transparency and reliability to address potential bias. The findings emphasize the importance of ongoing monitoring and bias mitigation in educational recommendation systems to ensure equitable and effective learning for all students. #AI #RobotsTalking #AIResearch
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