The Evolution of School Space in the AI Era - Focusing on Neural Network Characteristics of the Subject Center System -

Purpose: This study focuses on the importance of ‘hyper-connected intelligence’ and ‘collective intelligence’ required to solve the complex problems of the contemporary era. Based on George Siemens' educational philosophy of Connectivism, it explores the potential of future school spaces designed as ‘neural network environments.’ To avoid abstract discourse, the study critically evaluates the practical achievements and limitations of this concept through an advanced architectural case study. Method: Using a spatial analysis framework of six neural network characteristics—neuron, synapse, neural circuit, plasticity, homeostasis, and emergence—this study conducts an in-depth analysis of Itabashi No. 2 Junior High School in Japan, an advanced model of the ‘Subject Center System’ demonstrating high homology with these theoretical goals. Result: The case analysis confirmed that Itabashi No. 2 Junior High School operates as a practical spatial system where the six neural network characteristics function organically, facilitating the co-evolution of knowledge and the manifestation of collective intelligence across subject boundaries. At the same time, however, it clearly revealed the macroscopic limitations of vertical and linear disconnection inherent in traditional school architectural frameworks. Conclusion: This study emphasizes that schools in the AI era must overcome these physical disconnections and evolve into ‘spaces of organic connection’ where fragmented knowledge and individuals converge three-dimensionally. This represents a paradigm shift from supporting individual learning to facilitating the construction of robust learning networks, ultimately proposing specific architectural directions that meet the educational demands of the hyper-connected era.