Reorienting Academic Supervision in Deep Learning Approaches: A Theoretical Review of Challenges and Transformations

Abstract

This article examines theoretically the urgency of reorienting academic supervision in the context of 21st century education, particularly through deep learning approaches. In its background, academic supervision has undergone a paradigm transformation from a traditional authoritarian model to a reflective and collaborative transformative model. This study uses literature study methods and reflective analysis to examine the challenges and opportunities in the implementation of the deep learning approach to academic supervision. The results of the study show that deep learning technology has great potential in transforming supervision practices into data-based, real-time, and personalized, through observation automation, teacher performance analysis, and student engagement evaluation. However, there are significant challenges such as limited infrastructure, human resource readiness, and ethical and data privacy issues. This article concludes that the reorientation of academic supervision requires a paradigm shift from an administrative approach to a collaborative process based on continuous professional development, supported by an adaptive and ethical education ecosystem.