<![CDATA[This study aims to examine the role of cognitivist learning theory in enhancing the effectiveness of Deep Learning, both as a pedagogical approach and as a form of artificial intelligence (AI) technology. Cognitivism emphasizes the importance of internal mental processes, knowledge structures, and cognitive load management strategies in understanding and retaining information. Meanwhile, Deep Learning in education demands higher-order thinking skills, conceptual understanding, and the ability to connect knowledge across contexts. Using a literature review method, this study analyzes scholarly works published between 2010 and 2025 that discuss the integration of cognitive theory into digital learning design and adaptive AI systems. The findings indicate that strategies such as worked examples, fading, chunking, advance organizers, and metacognition-based active learning effectively improve learners’ comprehension in Deep Learning contexts. Furthermore, the use of AI-powered adaptive technologies developed based on cognitivist principles—such as neural cognitive diagnosis—can enhance learning personalization and instructional effectiveness. This study concludes that the synergy between cognitivist theory and Deep Learning can shape a more meaningful, reflective, and sustainable learning framework.]]>
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