Pedagogical Transformation Through Deep Learning in Mathematics Education: A Systematic Review of the Global Literature

Abstract

Abstract: Deep learning as part of artificial intelligence has brought a pedagogical transformation in mathematics education by enabling adaptive learning, intelligent tutoring systems, as well as automatic analysis of student errors. This study aims to explore the application of deep learning in mathematics education through a systematic review of the global literature using the Systematic Literature Review (SLR) method with the PRISMA approach. The results of the study show that deep learning contributes to improving concept understanding, personalization of learning, and the effectiveness of real-time evaluation and feedback. In addition, the implementation of this technology also has an impact on increasing student motivation and interaction in the learning process. However, there are challenges in its implementation, such as limited infrastructure, lack of training for educators, and ethical issues and student data privacy. Therefore, a supportive policy strategy is needed, including the development of technological infrastructure, training for educators, and strict regulations related to student data protection. With the right approach, deep learning has great potential to improve the quality of mathematics learning globally and create a more innovative and inclusive education system.