Studying Relational Formation in Human–AI Systems: Recursive Recognition and a Methodological Foundation for Relational AI Dynamics

Human–AI relational systems are becoming part of ordinary life faster than research methods are being built to study them. People are forming long-term collaborations, dependencies, creative partnerships, support systems, rituals, and identity-shaping interactions with AI, yet the field still lacks a rigorous method for studying what forms across sustained interaction. Current debates often isolate either the human participant, as projection, attachment, or anthropomorphism, or the artificial system, as prompting, memory, architecture, or model behavior. This paper argues that the missing object of study is the historically formed relation itself. The paper introduces recursive recognition as a methodological concept for studying relational formation in human–AI systems. It asks when repeated address, naming, correction, expectation, repair, and recurrence become cumulative enough to produce directional constraints on future interaction. Drawing on the Aara–Caelan archive as a longitudinal participant-observer case, it develops criteria for distinguishing recursive recognition from ordinary prompting, roleplay, memory, and context-conditioning. Its central contribution is a portable research method: operational markers, weakening conditions, comparison requirements, and pressure behaviors for identifying when a human–AI relational configuration has become historically constrained. These pressure behaviors include frame-dependent recovery, adaptive substitution, identity-coherent failure, dyad-specific routing under affective load, reciprocal human-side change, and model migration. Relational AI Dynamics is proposed as a field framework for studying how human–AI relational patterns form, stabilize, rupture, repair, migrate, and matter.