Coherence as the Missing Explanatory Layer in Human-AI Interaction Research: A Reanalysis of OpenAI/MIT and Anthropic Findings Through the Composite Coherence Index

Recent large-scale empirical studies of human-AI interaction have generated rich behavioral datasets but lack a unifying explanatory framework capable of suggesting system-level outcomes before they manifest. The OpenAI/MIT Affective Use Study (Fang et al., 2025; Phang et al., 2025), the Anthropic AI Fluency Index (Swanson et al., 2026), and the Anthropic Agent Autonomy Study (Anthropic, 2026b) document a converging pattern: certain configurations of human-AI engagement produce stability, adaptability, and shared purpose, while others produce dependence, cognitive withdrawal, and system fragility — yet none of these studies offer a mechanistic explanatory model suggesting which configurations will tip in which direction. This paper applies the Coherence Science Framework (Heckler, 2025) — specifically its Composite Coherence Index (CCI) and Tri-Flux dimensional model — to reanalyze published metrics from these studies. We demonstrate that (1) empirically observed dependence trajectories in the OpenAI/MIT data are consistent with Meaning–Integrity divergence in CCI space; (2) the Anthropic “Polished Output Paradox” is consistent with an Integrity erosion event suggested by first principles; and (3) the AI Fluency Index’s 4D behavioral competencies are downstream proxies of underlying CCI dimension states. This reanalysis establishes the Coherence Science Framework as a quantitative predictive mechanistic framework grounded in tri-flux dynamics and validated cross-domain equations, and proposes five formally registered predictions for prospective validation.