Neuro-Linguistic AI: Exploring the Cognitive Interface Between Human Language and Machine Understanding

Abstract

Neuro-Linguistic AI represents an emerging paradigm that integrates computational linguistics, cognitive science, neuroscience, and machine learning to build models capable of interpreting and generating human language with a level of contextual depth, semantic understanding, and cognitive alignment that approximates human-like comprehension. As AI systems increasingly mediate human communication, decision processes, and knowledge work, understanding the cognitive interface between human linguistic behaviour and machine interpretation becomes essential for developing trustworthy, intelligent, and socially-aligned systems. This paper investigates how Neuro-Linguistic AI frameworks synthesize neural language models, cognitive-semantic theories, and neuro-symbolic architectures to improve comprehension, disambiguation, reasoning, and meaning representation in natural language interactions. It evaluates how advanced generative models, attention-based architectures, cognitive embeddings, and neuro-semantic integration enhance machine understanding of pragmatics, intent, ambiguity resolution, and contextual inference. Findings reveal that Neuro-Linguistic AI improves interpretability, communication fidelity, and adaptive learning but also introduces challenges such as cognitive bias propagation, inference instability, semantic drift, and ethical complexity. The study proposes a unified research framework to analyse cognitive-linguistic alignment and suggests new pathways for robust, transparent, and human-centric machine understanding.