Machine Learning-Driven Optimization of Public Health Education Resources and Personalized Learning Systems

As the artificial intelligence (AI) is developing swiftly, it is becoming more common in public health and education. The lack of egalitarian distribution of educational resources and the urgent requirement of the learning personalization has exposed that the intelligent, data-driven AI is desired now. We investigate the AI that has a better ability to ration public health education resources with personalized learning services. By using the mixed methods of case analysis and machine learning, an AI model based on Support Vector Machine (SVM) algorithms is used to analyze the user behavior data, and to calculate the effective educational programs recommendation models for each individual and in different regions based on their requirements. We find that the AI model, in special the one trained with SVM can leverage the efficiency and quality of public health education resources; it can bring high rewards for individual learning services and the early intervention of public health education. Finally, this work also exhibits how the AI model can help even the education resources by advancing the traditional education resource allocation in public health and learning personalization.