Why faculty don’t need another AI policy, they need agency
This article is part of a three-part series examining how AI is reshaping trust between faculty, students, and the institutions governing them. Reading the full series is recommended.
Part 2: The Silent Loop | Part 3: The Collision Point
Elena Marsh writes her syllabus every August at the same kitchen table, and every August for the last three years it has become harder to write. She teaches first-year composition at a mid-size public university, the kind of course where the real subject isn’t grammar, it’s teaching eighteen-year-olds to think in sentences. This year, two days before classes start, the provost’s office sent out the revised campus AI policy. It was a one paragraph, campus-wide notice banning “unauthorized use of generative AI on any graded assignment,” with a footnote instructing faculty to run all submitted essays through the university’s new AI-detection add-on before grading.
The policy didn’t match anything Elena was trying to do. She wanted her students using AI, but as a brainstorming partner, a sentence-level sparring opponent, a way to see three versions of an argument before picking one, and, most importantly, disclosing to her that they’d used it. The policy, read literally, would flag exactly that workflow as a violation. It said nothing about disclosure. It said nothing about her discipline. It said nothing about the fact that a colleague from two buildings over was teaching a coding bootcamp-style intro course and wanted her students to use AI on every assignment, because knowing how to work with it was the skill being taught.
So, Elena did what faculty have been doing under the radar for three years now. She wrote her own policy into the syllabus, in language careful enough not to contradict the campus policy outright and hoped no one asked her to reconcile the two.
The Detection Default
Call it the detection default, when an institution doesn’t know what else to do about AI, it reflexively reaches for a ban and a detector. It is the easiest policy to write, the easiest to defend to a board of trustees, and the least useful to the person who actually has to run a classroom. It treats faculty as the last line of defense, rather than as the professionals best positioned to decide how AI belongs, or doesn’t, in their own discipline.
This failure mode mirrors the one The Institutional Intelligence Crisis documented on the operations side of the university, where a single mandated tool or a blanket workaround stripped staff of the judgment that made their work reliable in the first place. In the classroom, the mechanism is identical, and the stakes are just as personal. A philosophy seminar and a coding bootcamp course need different answers to “how should AI be used here,” and the person qualified to set that answer is standing in the room and shouldn’t be constrained to an administrative “one size fits all” approach.
What the Data Actually Shows
The instinct to ban is losing ground because faculty are already abandoning it on their own, not because institutions have found something better to replace it with.
A UC Berkeley study looked at 31,692 course syllabi collected between 2021 and 2025 (Chirikov, reported in Inside Higher Ed, Feb. 2026). It found that academic-integrity concerns, the reason most often given for restricting AI, showed up as the stated rationale in 63% of syllabi in spring 2023, but in only 49% by autumn 2025.
In place of that blanket justification, faculty are writing rules that vary by task. For example, AI is barred for drafting or revising in 79% of syllabi and for reasoning or problem-solving in 65%, but only 20% ban it for coding tasks, and just 17% for editing or proofreading. Meanwhile, requirements to disclose what AI was used and how jumped from 1% of syllabi to 29% over the same period.
Faculty are done waiting for institutions to hand down permission to make these distinctions. They’re making them anyway, one syllabus at a time, without a shared template or any institutional backing.
Meanwhile, the tools institutions lean on to enforce the old model keep failing in predictable, well-documented ways. Independent benchmarking has found that AI-text detectors lose much of their accuracy once a student does any manual editing or paraphrasing, performance that holds up in a controlled test collapses under exactly the kind of light editing real students do (RAID benchmark, Dugan et al., ACL 2024). And detectors don’t fail evenly.
A Stanford team ran 91 TOEFL essays by Chinese test-takers through seven AI detectors. On average the tools flagged 61.22% of them as AI-generated, while essays from U.S. students came back almost perfectly clean (Liang et al., Patterns, 2023). The reason is mechanical. Detectors score how predictable a piece of writing is, and a student working in a second language under exam pressure reaches for familiar words and safe sentence shapes, which is the exact pattern the tools read as machine-written.
ETS, the company that owns the TOEFL, took the problem seriously enough to spend a paper on it. Working with 85,567 essays, its researchers tested three fixes: balancing the training data, stripping out the features that track a writer’s language background, and moving the detection threshold. Each one reduced the bias to some degree without gutting accuracy (Jiang et al., 2024). Reduced, not removed.
And the pressure isn’t easing. The Digital Education Council’s 2026 Global AI in Higher Education Survey, 45,398 responses from students and faculty across 35 countries, found that 88% of students and 77% of faculty now use AI in their coursework or teaching (Digital Education Council, 2026). The 2025 EDUCAUSE AI Landscape Study, meanwhile, found that teaching and learning is now the institutional function most focused on AI adoption, and that faculty training is the single most common element of institutional AI strategic plans (EDUCAUSE , 2025). Everyone agrees training matters and almost no one has funded it at the pace adoption demands.
Why the Ban Persists Anyway
If the data so clearly favors discipline-specific judgment over blanket policy, why do so many institutions still default to the ban? Speed, mostly, and the fact that it’s defensible in a meeting. It also doesn’t require trusting thousands of individual faculty members to make thousands of individual calls. Most institutions didn’t adopt blanket AI policies because they believed them the best pedagogical answer; they adopted them because they were the fastest governance response to a rapidly changing technology. The problem is that what works as an emergency response rarely becomes a sustainable long-term strategy.
The ban buys speed and legal cover at the price of faculty judgment, and it plays out the same way every time. The first semester, a ban feels like caution. The second semester, with the ban unrevised and unenforceable, it starts to feel like avoidance. By the third semester, most students have routed around it, most faculty have stopped enforcing it as written, and the only thing the policy has reliably measured is the widening gap between what the syllabus says and what happens in the room.
Underneath that gap is a category error; treating consistency and fairness as the same thing. A single campus-wide rule feels fair because it applies equally to everyone. But applying an identical AI policy to a philosophy seminar building an argument from scratch and a data science studio where AI-assisted coding is the professional standard doesn’t produce fairness, it produces a rule that’s wrong for at least one of them, and often both. Real fairness in this context looks more like a shared space with room for discipline-specific needs. Every student can count on knowing what’s expected of them, even as the specifics vary by course.
What This Costs an Institution
At their core, blanket classroom AI policies are retention and liability problems wearing a pedagogy costume.
On the faculty side, surveys through 2026 have repeatedly found that most instructors receive no formal AI guidance at all, and that the resulting ambiguity is a measurable contributor to instructor burnout. On the institutional-risk side, using an unreliable detector as grounds for an academic-integrity referral is an exposure risk, a due-process problem waiting for a mistaken accusation to surface publicly. On the trust side, every time a policy visibly fails to match classroom reality, it teaches students that the rules are theater, and the real rules are whatever their individual professor decides to enforce. That’s a lesson that risks generalizing beyond AI policy.
Agency, With a Floor Under It
The fix isn’t “let every instructor do whatever they want” any more than it’s “one rule for everyone.” It’s giving faculty real authority to set discipline-specific AI policy, backed by institutional infrastructure that makes exercising that authority fast and easy instead of exhausting and tedious.
In practice, that means a few concrete things. A policy template faculty can adapt to their own course in under an hour, with discipline-specific exemplars, such as what a reasonable AI policy looks like in a lab science, a language course, a studio art class, so no one is solving this from scratch. A fast, low-friction path to revise that policy every term as the tools and the norms shift under everyone’s feet, and real investment in the faculty training that EDUCAUSE’s own data says every institution already claims to prioritize.
None of that means abandoning consistency altogether. Students should be able to count on a baseline of clarity across every course on their schedule, even when the specific rules differ by instructor and discipline. They need to understand what to expect, not necessarily what’s allowed. That floor is what makes room for faculty agency without turning the whole institution into hordes of disconnected experiments.
Faculty agency is one half of what happens in that classroom. The other half belongs to the student sitting across from Elena’s desk, who has no idea whether the essay she’s about to turn in will be read by a person, scored by a machine, or some blend of both, and no clear way to find out.
We explore more of that in Part 2 of this series. Read it now.
Punch List
| Action | Owner | Timeframe |
| Replace the single campus-wide AI ban/allow directive with a discipline-level policy template faculty can adapt in under an hour | Provost’s Office / Center for Teaching & Learning | This term |
| Stop using AI-detection scores as sole grounds for an integrity referral; require corroborating evidence given documented false-positive and bias rates | Academic Integrity Office | Immediately |
| Fund a faculty AI-training track tied to the syllabus-revision cycle each term, not a one-time onboarding session | Center for Teaching & Learning | Next semester |
| Build a discipline-specific exemplar library (humanities, lab sciences, coding-intensive courses, etc.) so faculty aren’t drafting policy from a blank page | Provost’s Office / CTL | This academic year |
| Establish a campus-wide disclosure floor — what students can expect to know, regardless of instructor — even as specific permissions vary by course | Faculty Senate + Provost | This academic year |
Talk to Robots & Pencils about designing agentic AI for education. Request an AI Briefing.
Note: Elena Marsh is a composite drawn from patterns documented across the sources above, not a named individual or institution.
About the Author
Lindsay Pineda is a Senior Delivery Manager at Robots & Pencils, where she leads delivery of an AI-powered student intervention platform for a major public research university. With over 20 years of experience spanning higher education, educational technology, and program and delivery management, she has held leadership roles at a range of organizations across the higher education and edtech sectors. Lindsay spent nearly a decade as an adjunct graduate faculty member at a large online university facilitating master’s level courses in project management leadership and PMP exam preparation while contributing to curriculum and instructional design. A PMP-certified leader with master’s degrees in psychology and management, she brings a rare blend of strategic delivery expertise and firsthand experience in online course facilitation and the student learning experience.
FAQs
Q: What is “the detection default”?
A: The reflexive move institutions make when they don’t know how to handle AI: ban it, then run submissions through a detection tool. It’s the fastest policy to write and the least useful to the person running the classroom.
Q: Are faculty actually following campus AI bans?
A: No, and the data shows it. A UC Berkeley study of 31,692 syllabi found integrity concerns as the stated rationale for restricting AI dropped from 63% (spring 2023) to 49% (autumn 2025), while disclosure requirements jumped from 1% to 29% over the same period.
Q: Do AI detectors actually work?
A: Not reliably. The RAID benchmark found detector accuracy collapses once a student does any manual editing. Worse, a Stanford study found detectors flagged 61.22% of TOEFL essays from Chinese test-takers as AI-generated versus near-zero for U.S. students, because detectors penalize predictable phrasing, a pattern common in second-language writing.
Q: What’s the alternative to a blanket ban?
A: Discipline-specific policy, set by faculty, with an institutional floor: a fast policy template every instructor can adapt, exemplars by discipline, real training investment, and a disclosure baseline students can count on regardless of course.
Q: What does this cost an institution that keeps the ban?
A: Faculty burnout from ambiguous guidance, legal exposure from using unreliable detectors as sole grounds for integrity referrals, and erosion of student trust when the written policy doesn’t match classroom reality.
Q: How does this connect to the rest of the series?
A: Part 1 covers faculty agency. Part 2 (The Silent Loop) covers the student side, undisclosed AI in grading and advising. Part 3 (The Collision Point) unifies both into one governance standard.
Key Takeaways
- The detection default (ban plus detection tool) is fast to write and defensible to a board but disconnected from how disciplines actually need to use AI.
- Faculty are already writing task-specific rules on their own: AI is barred for drafting in 79% of syllabi but only 20% for coding, and disclosure requirements rose from 1% to 29% of syllabi in two years.
- AI detectors carry documented bias, flagging non-native English writing at far higher rates than native writing, which makes them a liability risk as sole grounds for an integrity case.
- Treating consistency and fairness as identical is a category error, one campus-wide rule applied to every discipline produces unfairness, not equity.
- The fix is faculty authority with an institutional floor: adaptable policy templates, discipline exemplars, funded training, and a disclosure baseline every student can count on.
