The AI Governance Record.

In July 2025, Dr. Jeanetta Floyd, Associate Professor at Georgetown University, published a short LinkedIn essay titled "Words Matter in AI Conversations." It did not announce a framework. It did not propose a taxonomy. It did something more useful than either. It named, in the disciplined language of a Georgetown professor who teaches through problem-based learning, a discomfort that the AI governance field has been circling for two years and refusing to land on.

The discomfort is this: when we call a model's statistical error a "hallucination," we are not being colorful. We are making a pedagogical choice. And that choice has consequences far beyond the seminar room.

The regulatory landscape produces mandates. It does not produce mechanisms. That gap is where institutions get audited, sued, and defunded — not because they were ignorant of the mandate, but because they had no mechanism to prove compliance with one.

Nine months later, in April 2026, I posted "The Pedagogy Problem in AI Governance" to SSRN. Dr. Floyd's piece was one of the signals I was tracking — one of several practitioner reflections that told me the field was already feeling the failure even if it had not yet named it. That is what research in a novel field looks like. This issue names her contribution, builds on it explicitly, and shows why the pedagogy problem in AI governance is not a niche academic concern. It is the load-bearing failure underneath most of what we currently call "AI literacy," "responsible AI training," and "user education."

I

What Dr. Floyd actually said.

Strip the LinkedIn formatting away and her argument runs in three moves.

Move one. When a large language model produces an erroneous output, that output is not a glitch and not a cognitive event. It is prediction under uncertainty — the expected behavior of a system sampling from probability distributions shaped by training data. The model did not perceive anything. It generated a token sequence with the highest available conditional probability given an under-specified prompt and an incomplete training distribution.

Move two. Calling that behavior "hallucination" imports a clinical and cognitive vocabulary that does not belong to the system. The metaphor is not neutral. It softens. It anthropomorphizes. And in doing so, it dismisses three things the field cannot afford to dismiss: our responsibility to teach users how to interrogate outputs, the structural gaps in training data, and our obligation to engage with the actual choices in model architecture that produced the error.

Move three. The defense of the term collapses under scrutiny. Simplification is not the same as accessibility. Accessibility is offering accurate, transparent explanations that respect a user's capacity to understand complexity when it is clearly communicated. Catchy metaphor is not respect. It is condescension wearing a friendly face.

That is the argument. It is short. It is correct. And it is treating language as a pedagogical instrument and holding the speaker accountable for what the instrument produces in the listener.

II

Why this is a pedagogy problem, not a vocabulary problem.

The temptation is to treat Dr. Floyd's piece as a debate about word choice. Drop "hallucination," adopt "confabulation" or "stochastic error," and the problem is solved. It is not.

The pedagogy problem in AI governance is not that we picked the wrong word. It is that the field built its entire user-facing explanatory apparatus on the assumption that learners need their cognition flattened before they can engage with the technology. That assumption is the failure. The vocabulary is downstream of it.

The result is a population of operators, executives, board members, and frontline employees who have been taught to trust or distrust AI based on metaphors that were never accurate. When the system fails — and it will fail, statistically, by design — they have no framework for diagnosing what failed, why, or what to do about it.

III

Where the frameworks fit.

Closing

The field should catch up.

Dr. Jeanetta Floyd wrote a short essay nine months before I formalized the position paper. She named the problem in the language of the classroom. I named it in the language of the field. That is how a body of literature gets built in a discipline this young — practitioner signal, researcher synthesis, standard. Both moves are necessary. Neither, alone, is enough.

Most institutions will not fail because of a bad AI model. They will fail because of a broken governance structure around it. Language is part of that structure. Dr. Floyd saw it first. I am writing it down.