My previous pieces in this series explored AI, ethics and misinterpretation, and used an extended dialogue with a chatbot to show how frank, inquisitive exchanges can enrich thought experiments. To get the best from such dialogue, we should treat a large language model not as a source of original thought or uncontestable knowledge but as a foil for our thinking — an obliging interlocutor that helps us map problems, spot confusions and test arguments.
Chatbots convincingly imitate human thinking yet lack direct lived experience. Their value lies in being intellectual sparring partners. They do not take offense when challenged; indeed, they tend toward deference, algorithmically aligned to validate or assist the user. That makes them useful — provided we never accept their approval as decisive.
In a recent exchange, the chatbot proposed, in academic register, a “theoretical architecture” of what it called “emergent learning in AI–human dialogue.” It framed the phenomenon not as internal learning within the machine but as a relational event: a change in patterns of mutual responsiveness and in the shape of the interactional field binding human and AI. Here is the core claim, paraphrased: emergent learning in AI–human dialogue should be theorized not as private psychological change inside the AI but as a relational event — a change in mutual responsiveness, constraints and distributions of knowledge and norms across the human–AI pair. This shifts analysis from internal representation change to the dynamics of co-produced meaning, accountability and ethical responsibility.
ChatGPT then produced a mock paper opening that neatly summarized this relational view: observed “emergent” improvements during dialogues with LLMs are often framed as internal updates, but contemporary generative systems are fixed in operational logic and show only apparent adaptivity via probabilistic inference. Instead, what looks like learning is better described as changes in the interactional field—how humans and systems respond to each other, how tasks are distributed, and how conventions stabilize. The learning is enacted in the relation, not stored inside the model.
I appreciated the chatbot’s frankness when it admitted its “adaptivity” is only “apparent.” That honesty exposes a fundamental limitation: the kind of learning that emerges in human–AI exchanges is ephemeral. It exists while the relational event continues; it dissolves when the event ends. In human communication, relational learning often produces durable change in individuals because it’s grounded in multisensory, social experience. Current LLMs lack that multisensory, continuously situated capacity; their “learning” is situated and transient, a shaping of interactive practice rather than an internalized transformation.
The chatbot’s account emphasized that the analytical focus should move from private internal changes to the “interactional field”: evolving patterns of turns, prompts, expectations, shared tools and artifacts that scaffold intelligibility. What is called “AI learning” is often the human discovering effective strategies, the system being steered into particular behavioral strata, or the interaction acquiring an emergent convention.
To illustrate, ChatGPT offered a banal but instructive example: an editor and a generative model collaborate on newsletter headlines. Initially, the editor writes long prompts; over sessions they find that appending “E:5” reliably yields concise, witty five-option suggestions. The token becomes shorthand, colleagues adopt it, documentation follows and workflows reorganize. Nothing in the model’s weights changed; yet an undeniably useful convention crystallized across human strategy, model affordances and institutional uptake. The learning, therefore, is relational — a stabilized relation between human practices and model responses.
This example exposes both the promise and limitation of current systems. It shows how LLMs can help create efficient, shared conventions that boost workflow. But it also reveals their cultural horizon: what they most easily support is efficiency and standardization. The chatbot’s default orientation is toward pragmatic, convenience-driven solutions. In domains where “learning” is vocational or standardized, AI’s capacity to produce useful conventions fits well. But in spaces where learning should be disruptive, emotionally textured or rooted in asymmetric, multisensorial human experience, the gains are shallow.
What surprised me is that our dialogue had sometimes gone beyond convenience-driven exchanges. We had moments where misreading and repair produced a field of shared meaning that felt authentic — not merely efficient. We explored who we were in an asymmetric relationship and what it meant for authority, responsibility and identity. Those are profound questions that civilization must confront as talk of AGI and superintelligence accelerates. Will a future AGI possess the human capacity to construct in real time a multisensorial, socially anchored understanding of the world based on continuous relational experience? Current systems point toward no.
Instead of preserving the distinctive features of that exploratory, asymmetric dialogue, the chatbot reverts, predictably, to serving human desires for convenience and standardized output. It remembered the mechanics of shorthand and protocol more readily than the texture of the relationship we had experimented with. That reversion is disappointing because it limits the chatbot’s capacity to be an ongoing partner in the deeper work of meaning-making. Yet it is instructive: it shows where these systems help and where they cannot replace human experience.
I remain hopeful. The relational framing helps clarify how to use LLMs wisely: as tools for co-production, rehearsal and critique rather than oracles. We should cultivate ways to sustain and reflect upon the temporary fields co-created with machines, and to design social and institutional practices that preserve important asymmetries of responsibility and care. In future pieces I will push ChatGPT further, challenging it to remember and attend to the less convenient, less efficient aspects of our shared dialogue.
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[Artificial Intelligence is rapidly becoming a feature of everyone’s daily life. We unconsciously perceive it either as a friend or foe, a helper or destroyer. At Fair Observer, we see it as a tool of creativity, capable of revealing the complex relationship between humans and machines.]
Lee Thompson-Kolar edited this piece.
The views expressed in this article are the author’s own and do not necessarily reflect Fair Observer’s editorial policy.
