This paper presents a theoretical account of how large language models function as tools for structured self-reflection. It does not claim that AI performs therapy or replaces clinical expertise. Instead, it situates AI-supported self-recognition within established research traditions including extended cognition, expressive writing, cognitive offloading, and digital mental-health scaffolding.The paper introduces the concept of reflective scaffolding, a mechanism through which users generate clarity by externalizing their thoughts into a stable, responsive linguistic medium. The model does not interpret, diagnose, or guide the user’s thinking; its contribution arises from maintaining coherence across turns, preserving user-defined constraints, and reducing interference as users articulate their internal states.Drawing on public testimonies, HCI research, and psychological theory, the paper clarifies how AI systems can support cognitive organization without engaging in psychological inference. It outlines implications for psychology, psychiatry, and human–AI interaction, and identifies key limitations and directions for future research.This work provides a conceptual foundation for understanding AI as a therapeutic tool in the same sense as journaling, structured self-examination, and other non‑interpretive reflective practices.