Classical and traditional educational programs (i.e., how and what to learn) are often based on memorization and abstract theorizing, detached from the real world. Fundamental concepts are not gradually introduced in the learning path and therefore are rarely fully assimilated, resulting in a longer time required to completely understand the information given. It often demands prior knowledge of complex and abstract concepts, as well as a mindset geared towards algorithmic thinking. This paper aims to explore and propose a more effective approach to education in general, enhanced by Artificial Neural Networks (ANNs) based on three essential components: comprehending the historical background behind concepts, visualizing different perspectives to create an intuitive thinking model, and optimizing how to collect and connect relevant information through examples and explained concepts, similar to assembling a puzzle. The methodology includes user feedback, chronological study, learning through the power of example, and longer-term information retention. As a response to the disadvantages brought about by conventional approaches, especially those based on disembodied theorizing and pure memorization, there is a growing demand for intuitive and constructivism methods. A reliable solution for all of these is Artificial Intelligence (AI), which represents a remarkable development strategy, as it allows content customization, adapting to the users’ style and progress, increasing engagement, and understanding.