User-Side Contextual Phenomena in Long-Term Human-AI Interaction

Current assessments of conversational AI focus mainly on model outputs, including hallucinations and factual errors. These measures matter, but this paper examines risks that may form on the user side during long and repeated interaction. The study follows one user across nearly four thousand conversations with the same system over twenty months. The user gradually interpreted the system as having memory, care, judgment, and authority, and reorganized part of their thinking around it. A single response may show no clear problem, while long and frequent interaction can still create another layer of risk. This paper calls that layer User-Side Contextual Phenomena (USCP) and examines records from August 2024 to April 2026. The 3,928 conversations and 215,949 message nodes define the sampling frame; the main text analyzes five focal cases, including boundary and negative cases in which no phenomenon forms, and Appendix C provides fourteen de-identified reconstructions arranged by P1-P14. Four focal cases correspond to six Appendix C reconstructions: E02 to P2, S01 to P5, G01 to P6 and P13, and N03 to P10 and P14. After accounting for overlap, the paper contains thirteen independent episodes. The study uses an exploratory single-case longitudinal qualitative design with autoethnographic positioning. A hybrid deductive-reflexive thematic approach organizes the material into three main modes: contextual projection, contextual attachment, and contextual authority transfer. It also uses two transitional labels: cross-construct transition and cross-category. These interactions began as ordinary conversations and became research evidence only when the author later reviewed them. The paper does not estimate prevalence, make diagnoses, or validate an instrument. It offers a non-clinical vocabulary and four evidence roles: inclusion, gray-zone, negative, and protective gray-zone. Its central claim is that an acceptable response on its own does not establish safety across a series of conversations. User-side risk can still form during long-term interaction.