From Tool to Collaborator: A Single-Case Perspective on Learning, Trust Calibration, and Co-Reasoning in High-Density Human–AI Interaction

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Generative AI is increasingly used not only for one-off answers but as a persistent collaborator across learning, planning, design, and technical work. This paper presents a research-informed single-case perspective on an adult participant’s high-density collaboration with a conversational AI system. The analysis focuses on a documented two-week window (3–16 September 2026) within a longer-running interaction history and uses preserved conversation records and resulting artifacts as the evidentiary substrate. Rather than asking whether the system was simply helpful, the case examines observable patterns of human correction, AI challenge, independent judgment, verification, review-assisted retrieval, question development, trust calibration, transfer, and co-creation. Selected vignettes include the creation of evidence-state rules for a software product, a revision of the study’s own learning measure after the participant challenged an overly narrow recall criterion, a software fault-injection episode in which a deliberately corrupted result (273) was rejected against a separately recomputed expected value (272), and cross-product design transfer prompted by inspection of a concrete artifact. The record also contains counterevidence, including an AI memory error that would have created unnecessary repeat work without human challenge and record checking. The case does not establish causal learning gains or generalizable benefits. Instead, it supports a narrower proposition: sustained human–AI collaboration may be worth studying as a sociotechnical process in which useful outcomes depend on calibrated trust, visible correction, externalized records, and preserved human agency. A prospective N-of-1 protocol and later adult pilot are proposed as the next empirical stages.