Deep learning na educação inclusiva

uma revisão sistemática da literatura

Abstract

Deep Learning, a branch of Machine Learning, employs deep neural networks to process data and identify complex patterns. Its application holds the potential to benefit the education of students with special needs, enabling the creation of adaptive systems that cater to their individual demands, such as accessibility features and language translation. However, challenges arise, including algorithmic bias, which reproduces inequalities, necessitating data protection, assurance of integrity in technology access, and responsibility in the use of these systems. Internet governance becomes crucial in establishing ethical, inclusive, and secure policies for the use of deep learning in inclusive education. This study proposes a systematic review of existing literature on the subject, aiming to identify the challenges faced and the approaches adopted in the context of internet governance. In doing so, the goal is not only to comprehend the potential of Deep Learning in inclusive education but also to propose guidelines and principles guiding the ethical and effective application of this technology, contributing to the construction of a more egalitarian and accessible educational environment for all.