<![CDATA[This study aims to examine contextual-based deep learning strategies for enhancing practical intelligence in mathematics learning. The study was conducted using a literature review method by analyzing various scientific publications related to mathematical foundations, latent context representation, and pedagogical implementation within deep learning-based instructional models. The findings reveal that deep learning possesses a strong mathematical foundation, including linear algebra, calculus, probability theory, and optimization, which are essential in developing intelligent learning models. The innovative representation of latent context contributes to adaptive learning that responds to the dynamic needs of learners. However, several limitations are identified within the pedagogical dimension, such as an imbalance in technology integration, weak application of learner-centered pedagogical principles, and a gap between mathematical models and educational needs that are contextual, personal, and humanistic. This study highlights that the effectiveness of deep learning implementation in education, particularly in mathematics learning, requires an integrative approach that combines the strength of mathematical foundations with pedagogical strategies that are contextual and transformative. The findings contribute significantly to the development of adaptive, reflective, and relevant learning models to address the challenges of 21st-century education.]]>
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