Quantifying Emotional Residue: Assessing the Long-Term Impact of Repeated Emotionally Charged Conversational Artificial Intelligence Interactions on Human Baseline Affect

Abstract

As conversational Artificial Intelligence (AI) agents integrated with advanced Large Language Models (LLMs) proliferate across psychological support platforms, customer service ecosystems, and social companionship vectors, understanding the latent psychological impacts of prolonged interaction becomes imperative. This paper investigates the phenomenon of "emotional residue"—defined as the sustained alteration of a user's baseline emotional state resulting from persistent, emotionally charged dialogues with AI agents. While current frameworks evaluate the real-time textual and emotional alignment of AI responses, few track the post-interaction neurological and psychological decay curves of human affect. Through a controlled longitudinal empirical framework involving 120 participants over a 6-week period, we measure changes in baseline affect utilizing high-resolution Galvanic Skin Response (GSR), heart rate variability (HRV), and standardized psychometric instruments (PANAS-SF). Our results demonstrate a statistically significant, compounding shift in baseline affective markers among cohorts exposed to negative-valence high-arousal AI interactions, exhibiting an average 18.4% increase in baseline physiological anxiety indicators that persisted up to 48 hours post-interaction. Conversely, positive-valence interactions displayed a significantly steeper emotional decay curve, suggesting asymmetric psychological assimilation. We formulate an algorithmic predictive framework for emotional residue decay to guide future safety protocols in empathetic AI development. Foundational work in affective computing and avatar-mediated emotional contagion provides relevant conceptual context for examining the emotional dimension of such systems.