In a rapidly changing world, where adaptability is essential for navigating complex knowledge systems shaped by artificial intelligence (AI), striking the right balance between structure and flexibility is crucial. The Elasticity of Hyperfluid Concepts (EHC) Model serves as a framework that sheds light on how rigidity and adaptability interact within learning environments driven by AI. This study introduces five interconnected concepts: epistemic viscosity, hyperfluidity, semantic turbulence, meta-semiosis and ontic irreversibility. Each one of them is presenting its own set of challenges when it comes to structuring and interpreting knowledge. AI’s role, in learning is quite paradoxical. It improves learning by providing custom recommendations and adjusting pathways to needs; however, it can also lead to confusion and misunderstandings due to an overflow of information and shifting meanings. The EHC model examines how AI interacts with these factors in a manner focusing on organizing knowledge to ensure that AI supports meaningful learning while maintaining cognitive consistency. This paper presents approaches for implementing the EHC model in AI enhanced education, such, as creating structures in learning platforms using AI guided support to handle information overload and implementing methods to prevent misinterpretations of meanings. These conceptual contributions add to the ongoing discourse, about how AI influences education by highlighting the need to find a balance between rigidity and adaptability, in emergent knowledge systems.