Transforming Teacher Professionalism in the Era of Big Data: A Deep Learning Approach for Future Education

Abstract

The era of big data presents both significant challenges and strategic opportunities for transforming teacher professionalism. This study aims to examine how deep learning approaches can enhance teachers’ roles within the digital education ecosystem. A systematic literature review was conducted by analyzing 116 articles retrieved from major academic databases, including Scopus, ScienceDirect, SpringerLink, and ERIC. Based on relevance criteria related to big data, deep learning, and teacher professionalism, five core studies were selected for in-depth analysis. The findings revealed four key dimensions of transformation: the types of data utilized in educational practices, the stakeholders involved in the transformation process, the urgency of adapting to data-driven environments, and the strategies for implementing deep learning in professional development. The study emphasizes the importance of strengthening teachers’ data literacy, providing contextual and practice-oriented training, and establishing supportive policies that position teachers as reflective and proactive agents in digitally mediated learning environments. These results contribute to the development of an adaptive, ethical, and sustainable model of AI-driven teacher professionalism and offer valuable insights for future education policy in the era of big data