Personalizing Learning Using Deep Learning: Innovation in Digital Education

Abstract

In the last decade, the development of artificial intelligence, particularly in the field of Deep Learning, has rapidly advanced and driven innovations in natural language processing, computer vision, and intelligent systems. However, despite its transformative potential, the integration of Deep Learning into education remains limited and not yet systematically structured. The purpose of this research is to explore the role and impact of Deep Learning technology in education and learning, as well as to formulate optimal strategies for its integration in modern learning contexts. As artificial intelligence advances, Deep Learning emerges as a promising approach to enhance the quality of teaching and learning. This study employs a systematic literature review by analyzing recent scientific articles from Scopus-indexed journals and other academic databases. Thematic analysis was conducted to identify patterns of application, benefits, and challenges of Deep Learning implementation across different levels and forms of learning. The findings reveal that Deep Learning, through methods such as RNN and CNN, has significant potential to support personalized learning, automated assessment, and emotion detection in online education, as well as to enable interactive media based on voice and images. However, key challenges remain, including infrastructure limitations, insufficient training data, and limited educator readiness. This study contributes by proposing a conceptual framework for integrating Deep Learning into adaptive education systems tailored to individual needs. The results are expected to provide valuable insights for policymakers, educators, and technology developers in building a more responsive and inclusive learning ecosystem in the digital era.