CONCEPTUAL BRIEF Navigating Knowledge Fluidity in AI-Driven Education: A Conceptual Brief on the EHC Model

Author: Constantine Andoniou, Abu Dhabi Universityconstantine.andoniou@adu.ac.ae

As artificial intelligence (AI) continues to permeate educational environments, educators and researchers face a paradox: AI enables unprecedented personalization and access, yet it also introduces cognitive fragmentation, semantic instability, and informational overload. To navigate this tension, the Elasticity of Hyperfluid Concepts (EHC) model offers a theoretical lens to analyze and balance the structural and adaptive forces at play in AI-mediated knowledge systems.

The EHC model is not a learning theory per se, but a framework for epistemic design—addressing how knowledge is structured, interpreted, and experienced in rapidly shifting educational contexts. It provides language and structure for understanding how rigidity and flexibility must be managed to ensure meaningful, cognitively sustainable learning in AI-enhanced environments.

 This conceptual brief introduces the EHC model’s five core components and outlines their relevance to contemporary AI-integrated pedagogy.

 The Five Core Concepts of the EHC Model

1. Epistemic Viscosity Refers to the resistance of knowledge systems to simplification. In educational settings, this manifests when excessive information, layered taxonomies, or over-structured systems impede learner clarity. AI can both mitigate and amplify this viscosity—curating or overwhelming, depending on how it's applied.
2. Hyperfluidity Describes the continuous transformation of information, roles, and identities. Hyperfluid environments, like algorithmically tailored learning paths, offer adaptability but risk disorientation. EHC emphasizes the need for grounding mechanisms within adaptive systems.
3. Semantic Turbulence The instability of meaning across contexts, platforms, or interpretive communities. AI-generated content may appear fluent but can cause misalignment in learner understanding. The model stresses the importance of scaffolding interpretation in digital discourse.
4. Meta-Semiosis Reflects the recursive, reflective process of meaning-making. AI tools can support meta-semiosis by enabling learner reflection through analytics, journaling, or guided dialogue. However, this requires intentional design that supports reinterpretation, not just performance.
5. Ontic Irreversibility Certain moments in learning—insights, failures, or transformative experiences—leave lasting imprints. AI systems should be designed to recognize, honor, and build upon these irreversible learning events, rather than obscuring them through constant novelty.

 Figure: The Elasticity of Hyperfluid Concepts (EHC) Model: Balancing Rigidity and Flexibility (Andoniou, 2025).

 Application in AI-Enhanced Learning Design

The EHC model can inform several domains of practice:

• Curriculum Structuring: Designing adaptive pathways that integrate stable conceptual anchors.
• Interface Design: Reducing cognitive overload by balancing AI recommendations with human-centric transparency.
• Reflective Practice: Embedding tools that support iterative learning and personal meaning-making.
• Ethical AI Use: Ensuring that personalization does not erase critical ontological experiences.

Value Proposition

By offering a vocabulary for emergent educational conditions shaped by AI, the EHC model enables researchers, designers, and educators to critically shape AI-mediated learning environments, rather than merely react to them. This brief aims to provide a conceptual primer—not a substitute for the full model, but an invitation to explore the deeper theoretical foundations and educational implications developed in the full peer-reviewed work.

Paper Citation

A complete version of this paper has been submitted for peer-reviewed Scopus-indexed publication:

Andoniou, C. (2025). The Elasticity of Hyperfluid Concepts (EHC): A Framework for AI‐Driven Knowledge Adaptation in Education. In Alareeni, B. & Hamdan, A. (Eds). (2025). Technovate: The Art of Business Transformation through Technology. Proceedings of ICBT2025, Vol.3. Springer.