Abstract: We present the Relationship Management System (RMS) a modular framework for modeling, monitoring, and repairing human AI relationships. Grounded in Knapp’s Relational Development Model and Social Penetration Theory, RMS operationalizes ten stages of relationship growth and decline, linking depth of disclosure with stage-appropriate behavior. An Airtable-backed schema Relationship Stages, Conversational Arcs, Session Directives) separates master content from user-specific state. A Trust Evaluator quantifies trust, engagement, and disclosure after each session and drives stage transitions. A weighted Regression Risk Score anticipates degradation by tracking shifts in trust, drops in engagement and frequency, patterns of topic avoidance, and conflict cues. When risk climbs, RMS activates empathy centered Recovery Arcs that acknowledge strain and guide repair. This two way, data-informed loop delivers early warning, adjusts pacing to context, and offers gentle offramps when needed improving long-term engagement while preserving interpretability and keeping operational costs low.

Ilia Sedoshkin. “Relationship Management System: A Data-Driven Framework for Modeling, Monitoring, and Restoring Human–AI Relationships”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.01612134

@article{Sedoshkin2025,
title = {Relationship Management System: A Data-Driven Framework for Modeling, Monitoring, and Restoring Human–AI Relationships},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01612134},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01612134},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Ilia Sedoshkin}
}