Material Detail

This paper explores the transformative potential of AI in secondary education, leveraging student data and socioeconomic information to tailor learning pathways and enhance academic outcomes while addressing ethical concerns. 

Key Takeaways

AI integration into secondary education focuses on utilizing student assessment data and socioeconomic factors to create personalized learning pathways and interventions. 

The aim is to enhance academic results, promote social equity, and inclusivity by identifying and responding to the unique needs of each student. 

Support Vector Machine (SVM) and ensemble learning techniques like Random Forest, XGBoost, and AdaBoost are used for predicting student performance. 

Ethical considerations in deploying AI systems are vital, emphasizing equity, transparency, and data privacy. 

The data analyzed includes information on student demographics, academic performance, and socioeconomic background. 

Data fusion techniques combining multiple base models have shown better performance in predicting student outcomes. 

Personalization in education should consider individual differences, with AI providing tailored guidance and resources to foster student success.

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