<![CDATA[This study explores the transformative potential of deep learning in enhancing curriculum design and learning processes within modern educational environments. Drawing on a qualitative research design, the study employs documentary and thematic analysis to synthesize insights from scholarly literature, policy documents, and empirical studies on artificial intelligence (AI) in education. The findings reveal that deep learning enables the development of adaptive, data-driven curriculum frameworks capable of responding to diverse learner needs and rapidly evolving knowledge landscapes. Deep learning technologies also support personalized learning pathways by analyzing real-time and historical student data to identify learning gaps, predict performance, and recommend targeted instructional interventions. Moreover, the analysis highlights the role of deep learning in improving instructional practices through intelligent feedback, automated assessment, and enhanced learner engagement. However, several challenges remain, including issues related to data privacy, algorithmic bias, teacher readiness, and infrastructural limitations. Addressing these concerns is essential for ensuring equitable and responsible integration of deep learning technologies. Overall, the study concludes that deep learning offers significant promise for reimagining curriculum and instruction, provided that implementation is carefully planned and supported by appropriate pedagogical, ethical, and institutional frameworks.]]>
Copyrights © 2025
