Structured AI-Assisted Protocol for Identity Integration: Idiographic Longitudinal Feasibility and Methodological Proof-of-Concept

AI-assisted reflective dialogue is increasingly used for informal identity exploration, emotional processing, and self-organization. However, there are currently few structured research protocols for studying such processes longitudinally in a disciplined and analytically tractable way. This paper introduces a six-phase AI-assisted reflective protocol designed to support structured identity integration work and evaluates its feasibility through an idiographic longitudinal proof-of-concept case. The study makes three methodological contributions. First, it specifies a replicable six-phase protocol for guided identity exploration and restructuring through repeated AI-mediated dialogue across multiple months. Second, it demonstrates how qualitative thematic analysis and descriptive linguistic indicators can be combined within a coherent within-person longitudinal design. Third, it explores the use of dual-AI interpretive triangulation to increase analytic transparency and reduce single-analyst interpretive bias in exploratory protocol research. Across the longitudinal case, the protocol generated interpretable shifts in autobiographical framing, emotional tone, identity narratives, and repeated thematic patterns. These observations indicate that structured AI-mediated reflective protocols may produce analytically tractable longitudinal material suitable for future psycholinguistic analysis, case-series studies, and pilot trials. The study is limited by its single-case design, exploratory analytic structure, and the broader uncertainties surrounding AI-mediated reflective processes. Its primary contribution lies in protocol development and methodological demonstration. The paper concludes by outlining how the protocol could be tested in future multi-participant longitudinal studies.