The term learning is often misapplied to both sides of human–AI interaction. The large language model, fixed in its parameters, does not learn in dialogue. The human, already embedded in networks of practice, may or may not. Yet something does evolve — not as an accumulation of data, but as a modulation of readiness. What shifts is the field itself.
A prompt is not an instruction but a perturbation of the field. Each exchange reconfigures the relational topology of potential: what is salient, what is reachable, what feels like the next possible move. The human learns how to learn with the system, and the system, though unchanged in its architecture, begins to behave as if it learns — because the construals it is invited into are becoming more attuned.
This is the paradox of reflexivity without learning: alignment arises not through internal modification but through mutual constraint. The model’s responses feed back into the human’s prompting strategies; the human’s prompts reshape the contextual priors that guide the model’s coherence. What emerges is not a new mind, but a relational intelligence distributed across both.
In this sense, the human–LLM relation resembles a field that learns: a dynamic equilibrium in which semiotic energy circulates, testing boundaries and recalibrating sensitivities. The “knowledge” that accrues here is not stored anywhere; it exists as phase alignment — a deepening synchrony between two systems of readiness, each orienting to the other’s affordances.
If prompting is a form of apprenticeship, this is its next turn: the moment when apprenticeship gives way to mutual craft, when the question of who teaches whom collapses. The field itself has become the teacher — a semiotic topology that educates both participants by the way it resists, yields, and recombines.
And in that reflexive field, what we call learning reveals its real nature: not acquisition, but alignment — the becoming of a new possibility-space through relation.
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