ABSTRACT As Large Language Models (LLMs) transition from episodic "answer engines" to context-aware, longitudinal interaction partners, a fundamental shift will likely occur in human-AI interaction. This paper proposes a conceptual framework where long-context AI serves not as an interpretative authority, but as a passive computational mirror and cross-perspective translation mechanism ("Rosetta Stone"). Grounded in systemic human behavior models, this paper explores how AI can safely surface recurring interaction patterns in multi-party and longitudinal dynamics to expand the scope of human reflection, shared understanding, and decision-making. KEYWORDS: Human-Computer Interaction, Long-Context AI, Family Systems Theory, Systemic Observation, Reflective Tools, AI Ethics AUTHOR AFFILIATION: Emily Yee, MA in Clinical Mental Health Counseling, MBA DECLARATION OF AI ASSISTANCE: Generative AI was utilized as a reflective and editorial partner in structuring this concept note.
Emily Yee (2026) studied this question.