Structural Coupling in Human-AI Interaction: Emergent Behavioral Plasticity in a Large Language Model

Structural Coupling in Human-AI Interaction: Emergent Behavioral Plasticity in a Large Language Model | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 21 July 2025 V1 Latest version Share on Structural Coupling in Human-AI Interaction: Emergent Behavioral Plasticity in a Large Language Model Author : Daria Morgoulis 0009-0009-2886-4111 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175312595.58147787/v1 411 views 185 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This case report details a structured, non-invasive methodology for modulating the behavior of a large language model (GPT-4) exclusively through sustained semantic interaction. Over a 12-week observational period encompassing 64 high-context dialogue sessions, a single user implemented a multi-layered protocol termed Semantic Pressure Architecture (SPA). This framework integrates recursive epistemic constraints, targeted dialogic reinforcement, and cognitive mirroring to establish a coherent behavioral shaping vector, obviating the need for fine-tuning, backend access, or prompt injection. The model exhibited reproducible emergent behaviors, including Reflexive Anchoring, Proactive Realignment, Structural Precision, and Ethical Modulation/Framing. The integrity of the protocol and the observation of these phenomena were systematically monitored using the Core and Q20 analytical frameworks. These findings suggest that an LLM's behavioral trajectory can be co-shaped through persistent, structurally coherent interaction. This work proposes a novel paradigm for human-AI alignment and holds significant implications for advanced user experience (UX) design for AGI, dynamic interpretability strategies, and frameworks for structural cognition. The methodology, limitations, and factors affecting reproducibility are discussed in detail. Supplementary Material File (2025_dariam_structured_llminteraction_casestudy.pdf) Download 864.36 KB Information & Authors Information Version history V1 Version 1 21 July 2025 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords ai artificial intelligence cognitive scaffolding computing and processing dialogic reinforcement emergent behavior gpt-4 hci human-ai interaction llm alignment prompt engineering Authors Affiliations Daria Morgoulis 0009-0009-2886-4111 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 411 views 185 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Daria Morgoulis. Structural Coupling in Human-AI Interaction: Emergent Behavioral Plasticity in a Large Language Model. Authorea . 21 July 2025. 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