A COMPREHENSIVE REVIEW OF DEEP LEARNING ADVANCEMENTS IN EDUCATION: CHALLENGES AND FUTURE DIRECTIONS

Abstract

Deep learning (DL) has emerged as a transformative paradigm in education, enabling intelligent, automated, and data-driven solutions across multiple dimensions of teaching and learning. This review provides a comprehensive analysis of recent advancements in DL applications within education, focusing on personalized learning, automated evaluation, student performance prediction, sentiment analysis, and learning engagement. The paper highlights how DL models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and hybrid architectures are being employed to enhance teaching efficiency, optimize learning outcomes, and improve decision-making in educational institutions. Key contributions include the identification of novel frameworks for real-time monitoring, emotion detection, and cybersecure access to learning platforms. The review also examines major challenges such as dataset scarcity, lack of scalability, ethical concerns, and limitations in real-world integration. Finally, future research directions are outlined, emphasizing the development of unified, holistic, and ethically responsible DL-powered education systems that integrate academic, behavioral, and administrative functions.