Beyond Prompting: Who Is Watching the Thinking? Layered Vector Epistemology, Relational Calibration, and Metacognition in Sustained Human–LLM Sense-Making

Much of the public and educational discourse surrounding generative artificial intelligence treats interaction with large language models as a transactional process: a human provides a prompt, the system generates an answer, and the exchange ends. This conceptual paper argues that such a model is insufficient for understanding sustained, recursive human–LLM interaction. Drawing upon Layered Vector Epistemology (LVE), a relational model of Organic Machine–Inorganic Machine (OM–IOM) interaction, metacognition, relational calibration, lived experience, and scholarship on situated, distributed, embodied, and human-centered cognition, I propose that sustained human–LLM engagement can develop temporal and relational structure. Previous exchanges may become part of the conditions under which subsequent exchanges occur, while the human may also leave an interaction differently positioned than when it began. The paper distinguishes relational production from symmetrical understanding and argues that human and artificial systems make fundamentally different contributions to interaction. The human brings embodiment, lived experience, culture, memory, emotion, judgment, accountability, and metacognitive capacity. The LLM generates language through computational architectures shaped by training, tuning, system constraints, affordances, and available context. Relational calibration can make sustained interaction increasingly responsive to accumulated interactional history, but calibration alone cannot ensure epistemic quality. Human metacognition is therefore necessary to monitor, evaluate, interrupt, redirect, accept, reject, and reinterpret what emerges. A formal OM–IOM model is offered as a conceptual representation rather than an empirically established causal model. The model distinguishes accumulated accessible context from an evolving relational state and treats relational history as theoretically consequential for subsequent interaction. The paper concludes that generative AI literacy requires more than prompting. It requires learning how to remain intellectually present while thinking with, against, and through generative systems and learning how to watch ourselves thinking while doing so.