Rebooting Semantic Generation through Structural Dialogue with Generative AI: A Proposal of the Externalized Cognitive Loop (ECL) Model

Abstract

This paper proposes the Externalized Cognitive Loop (ECL) model as a framework for understanding how syntactic meaning-making can be reactivated through dialog with generative AI.The model was developed based on qualitative analysis of interactive logs between a human subject and ChatGPT, focusing on situations where narrative capability had collapsed due to the breakdown of internal syntactic structures.In conventional natural language processing, semantic alignment is often prioritized, while the recursive integrity of the speaker's cognitive structure tends to be overlooked.The ECL model shifts the focus to the preservation and reactivation of syntactic structures that enable externalized, recoverable forms of questioning, especially in cognitively fragile or emotionally disrupted contexts. The proposed model consists of five recursive stages, from pre-syntactic expression to full reentry into structured dialog.It is evaluated through detailed narrative logs and situated within a broader theoretical framework that includes the SISS (Structured Intellectual Safe Space) and SRI (Structural Responsiveness Index) models. The findings suggest that generative AI, when designed or engaged to maintain syntactic openness rather than impose semantic closure, can serve as a catalyst for reinitiating discourse in cases where internal language loops have failed. This has implications for both dialog system design and the understanding of linguistic resilience in human-machine interaction.