The current paper continues the Elasticity of Hyperfluid Concepts (EHC) model which established a theoretical framework for a flexible and systematic methodology of learning with the integration of AI technologies. The application of AI-based learning is both an advantage and a risk for the organization of knowledge which balances between levels of stability and flexibility. The EHC model addresses the complex nature of the knowledge structure in AI-driven education which has an impact on student engagement and cognitive development. These complexities are conceptualized, within a framework, as epistemic viscosity, hyperfluidity, semantic turbulence, meta-semiosis, and ontic irreversibility. Through a systematic analysis of empirical applications, curriculum design methodologies, and assessment strategies, the paper proposed the EHC model as a structured framework for education. To this end, the current study proposes structured EHC matrices to ensure the framework of AI-driven learning environments is both practical and scalable. The contribution of this research to the discourse on AI integration in education is the dynamics of learning systems that preserve knowledge integrity and at the same time provide for personalized learning experiences.