Abstract The process of learning and knowledge transfer is governed by highly organized systems, both in biological intelligence and artificial models. This paper explores the parallels and interactions between the human brain's architecture, specifically neural circuits and pathways, and the computational structure of artificial neural networks (ANNs). In human learnography, brain regions such as the prefrontal cortex, hippocampus and cerebellum collaborate through synaptic plasticity and motor science to build memory and skillsets. Similarly, ANNs utilize the layers of interconnected nodes to simulate these mechanisms, enabling machines to learn from data, recognize patterns, and make informed decisions. By examining the learning parts and structures in both systems, this study highlights how biological insights inspire artificial intelligence and how ANNs, in turn, reflect core principles of brain-based knowledge transfer. The convergence of these learning paradigms offers a dee...

In the rapidly evolving landscape of student learnography, the concept of knowledge transfer has garnered significant attention. Knowledge transfer technology, particularly in the realm of student learnography, has the potential to revolutionize how students acquire, retain and apply knowledge. Future Advancements in Knowledge Transfer Technology Central to this is the idea of brainpage development, where information is effectively transferred from source books to a student’s brain, optimizing learning outcomes. As we look to the future, several technological advancements promise to enhance the impact of personalized knowledge transfer on brainpage engagement and student achievement. Concept of Learnography Learnography refers to the science of brain learning, which is applied in the motorized learning of Taxshila Model. It emphasizes the transformation of knowledge into brain-compatible formats, fostering better understanding and retention. Unlike traditional teaching education, which...