<![CDATA[This study aims to examine the utilization of behaviorist learning theory to strengthen deep learning instruction through a systematic literature review approach. Deep learning, as a branch of artificial intelligence, demands high-level conceptual understanding and technical skills. Meanwhile, behaviorist theory offers structured learning principles—such as reinforcement, stimulus-response, and repetition—that are relevant for building disciplined and goal-oriented learning processes. A review of scientific literature from 2020 to 2025 indicates that the application of behaviorist principles can enhance student motivation, concept retention, and technical proficiency in mastering deep learning materials. Positive reinforcement and immediate feedback have been shown to effectively reinforce desired learning behaviors, while systematic repetition improves long-term retention and problem-solving abilities. Furthermore, integrating behaviorist principles into digital learning systems increases the effectiveness of adaptive and self-directed learning. Nevertheless, behaviorism should be combined with cognitive and constructivist approaches to achieve more comprehensive learning outcomes. This study recommends the strategic use of behaviorism as a foundational framework in the design of deep learning instruction in higher education settings.]]>
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