Deep Learning in Personalized Global Education: A Systematic Review

Abstract

The adoption of deep learning technologies has significantly influenced the education sector, enabling tailored learning experiences suited to diverse learners worldwide. Personalized international education, which addresses varying cultural, linguistic, and academic backgrounds, benefits greatly from these AI-driven innovations. This paper comprehensively reviews how deep learning models—such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models—facilitate adaptive learning, automated assessments, intelligent tutoring, and personalized recommendations. Through practical case studies like Duolingo and Coursera, the paper highlights both the benefits and challenges of implementing deep learning in education. Key concerns, including data privacy, algorithmic bias, and model interpretability, are discussed alongside best practices for responsible deployment. Ethical considerations and future technological trends are also explored to present a holistic understanding of the field. The findings suggest that while deep learning offers immense potential to revolutionize international education, thoughtful integration and ethical safeguards are essential to realize its benefits fully.