An article circulating this week argues that faculty AI buy-in in higher education is a human factors engineering problem. The framing is correct. The path the piece describes skips the only two steps that matter, and the reason it skips them is structural, not pedagogical.
Start with the framework on its own terms. Human factors engineering, as a discipline, is most rigorous in the places where mistakes kill people — aviation, medicine, nuclear operations, military command. In none of those places does participatory design mean asking operators to author protocols for systems they have not yet operated. Crew Resource Management in commercial aviation was built by pilots who had logged thousands of hours on the platform. The accident-investigation literature, the cognitive task analyses, the checklists, the cross-checks — all of it sits downstream of operator-grade familiarity. Mature HFE practice in industrial settings treats prerequisite familiarity as a precondition for authorship, not as a parallel track. The order is fixed: operate the system, then design the safeguards.
The Step the Article Skips
The piece on faculty AI buy-in moves directly from "engage faculty as co-designers" to the outcomes — trust, transparency, governance, alignment with academic values. The prerequisite that holds every successful HFE program together never appears in the prose. The article asks faculty to co-design governance for tools the average faculty member has used for less than ten hours total, primarily in artificial training contexts.
What the article describes as co-design is closer to structured surveying. Faculty in a one-hour ChatGPT workshop can tell you what the demo felt like. They cannot tell you which boundaries a graduate seminar in clinical psychology needs around hallucination, or which retention defaults a research-methods course needs around student-generated prompts, or which provenance attribution rules an introductory writing course needs to keep its rubric honest. Those are the governance questions that matter. Surface familiarity produces surface governance.
What the article wants — discipline-specific, boundary-aware, defensible against edge cases — requires sustained use in the actual work. Faculty have to teach with the tool, grade against the tool, fail against the tool, and revise around the tool, for weeks or semesters, before they can author governance worth shipping. The discipline has a name for this kind of sustained operation in the actual work, and the name is praxis.
The Sequence That Makes the Outcomes Hold
The order matters because what comes out of a co-design session is exactly proportional to what its participants have actually done with the tool. A committee composed of operators who have spent a semester working through real student artifacts produces governance that survives the first stress case. A committee composed of policy interpreters who watched a demo produces governance that fails on contact with real coursework.
The fix is a sequencing change: praxis programs first, in disciplines, with real workflows and instructional artifacts, for at least one cycle of student work. Governance authorship after. The order is not optional, and the patience required to hold it is the part most institutions cannot afford politically. The faculty AI committee is sitting now; the spring catalog is locked; the student-affairs office wants a policy by July. So the committee is asked to ship governance from surface familiarity, and the result is governance theater.
The Second Step Hidden in Plain Sight
There is a second reason most institutions cannot deliver participatory design on AI even when they want to, and this one has nothing to do with pedagogy. By the time the faculty AI committee convenes, the enterprise contract has already been signed. Microsoft 365 Copilot for Education was procured eighteen months ago. The Google Workspace AI add-on, the OpenAI Edu tier, the Canvas-integrated AI tutor — all already on the books, with contract terms negotiated by procurement and counsel against the vendor's standard data-protection and indemnity language.
The actual policy surface — data flows, retention windows, opt-out defaults, training-data carve-outs, accountability allocation, liability for hallucinated outputs reaching students — was decided in that contract. What the faculty AI committee ships from here is acceptable-use guidance inside a perimeter that was drawn elsewhere by people the committee never met. Co-design at the policy layer is downstream of choices that already foreclosed most of what could be co-designed.
This is the same structural pattern that shows up whenever software arrives through the procurement door instead of the operator door. The real co-design moment is the moment the contract is being negotiated. The operators are not in that room. By the time the operators are in the room, the room has been redecorated, and the decisions that needed operator input are the wallpaper.
The Reframe
The vocabulary the discussion runs on is part of the trouble. Buy-in is a marketing term. It implies persuading a population to consent to a decision that has been made. Higher-ed faculty are operators of AI workflows in disciplines where errors compound — into student records, into transcripts, into citations, into degree credentials. Authorship is the target the framework actually requires.
Authorship requires praxis. Praxis requires sustained operation in the actual work. Sustained operation requires that the procurement phase admit it is the policy phase, and seat operators where the contract gets negotiated. The article describes the destination correctly. Trust, transparency, governance, alignment — all of those are the right outcomes. The path it draws skips the only two steps that can produce them.
What This Looks Like In Practice
For an institution willing to do the work, the program structure is concrete. A nine-to-twelve-month operator residency for each discipline before its AI governance is drafted, structured around real student artifacts and graded course outputs. A standing seat for faculty operators in the procurement workstream, with veto power on terms that touch retention, training-data use, and provenance. An explicit acknowledgment in published policy that the contract terms are the upstream constraint, named and dated, so the limits of faculty authorship are honest and visible. A sunset clause on every contract that returns the policy surface to renegotiation when the operator cohort says the boundary is wrong.
None of this is the part faculty AI committees are currently asked to produce. All of it is the part the human factors engineering frame, taken seriously, would require. The framework is right. The implementations being shipped this year are the framework with the prerequisites filed off.
Higher education will get AI governance worth defending only when the operators arrive before the contract is signed and the praxis arrives before the committee meets. Until then, what most institutions are calling co-design is a way of borrowing the legitimacy of participation without paying its operating cost.
