Cognitive Interaction Layers for Neuro-Symbolic AI

Despite recent breakthroughs in large language models (LLMs), current AI systems remain limited in their ability to engage with knowledge in ways that align with human cognition. While LLMs excel at syntactic and contextual processing, they often fall short in semantic interpretation, conceptual association, and memory-oriented reasoning. This gap underscores the need for cognitive interaction layers, which serve as human-AI interfaces that integrate structured knowledge with cognitive encoding strategies to support intuitive, interpretable, and memory-efficient learning.
This paper introduces a conceptual and technological framework for cognitive interaction layers that function as mediators between AI systems and human users. By embedding mechanisms such as semantic cues, associative representations, visual metaphors, and structured schemas, these layers enable more human-aligned interaction and knowledge transfer. We discuss the theoretical foundations of cognitive scaffolding and neuro-symbolic reasoning, provide a mathematical formulation of cognitive encoding and retrieval functions, and compare existing cognitive architectures with the proposed approach. The framework opens new avenues for human–AI interaction by transforming static knowledge representations into cognitively enriched environments that support education, skill acquisition, and interpretability in intelligent systems.