Deep Learning Approaches in Education: A Literature Review on Their Role in Addressing Future Challenge
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Abstract
This study aims to examine the role and potential of Deep Learning (DL) approaches in the field of education through a literature review method. DL, as a branch of artificial intelligence, is increasingly applied across various educational domains, including personalized learning, educational data analysis, and the development of intelligent teaching systems. This review analyzes more than 20 scholarly articles published in the past decade to identify DL’s contributions to the effectiveness and efficiency of the learning process. The findings indicate that DL enhances the accuracy of student performance predictions, supports adaptive learning, and strengthens natural language processing in educational contexts. However, challenges such as limited infrastructure, low technological literacy among educators, and ethical issues like data privacy remain major obstacles. The study concludes that DL approaches hold a strategic role in addressing future educational challenges, though their success largely depends on the wise and collaborative integration of technology.
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