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 

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.

Earning trust when AI is making decisions about you 

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 1: The Detection Default | Part 3: The Collision Point 

Maya Chen is a first-year student in Elena Marsh’s composition course, and two weeks into the semester she has already sensed a contradiction she can’t quite put into words. The program she is enrolled in requires every first-year writing student to run their essay outline through the university’s approved AI tool before drafting; not because Elena asked for it, but because the college wants to show it’s “building AI literacy.” Maya does what the assignment asks. She types a prompt into the tool, screenshots the output for the completion credit, and writes her actual essay the way she always has. She has learned nothing about the tool, but then, that was never really the point of the exercise. 

Then she submits her final draft, and it passes through the university’s writing-assessment pipeline, which is mix of an AI-assisted feedback tool and Elena’s own read. It comes back marked down half a grade for “irregular phrasing,” with no further explanation. Maya sits with that for a while. She wasn’t allowed to use AI to help write the essay; using it that way would have been an integrity violation. But something used AI to help judge it. She has no way of knowing which parts of her essay created the flag, whether a person looked closely at those sentences before the grade was finalized, or who she’d even ask to get more clarification. The tool that graded her didn’t have to explain itself, but she is required to do so in every essay. It just does not feel like a fair trade to Maya. 

The Silent Loop 

Call it the silent loop. The growing set of decisions that touch a student’s academic life; a grade, a feedback comment, a flag on their advising file, a nudge to switch majors, that are increasingly made or shaped by AI, all without the student being told when or how. No one set out to hide anything. Disclosure was never built into the system in the first place, and it’s nobody’s job is to notice the gap. 

 The loop is already running in places most students never see. Early-alert systems score retention risk. Advising platforms surface nudges. Assessment tools flag phrasing. Most of these are genuinely well-intentioned programs, and some of them demonstrably work. But in April 2026, the National Student Legal Defense Network published a Student AI Bill of Rights whose first article asserts that students have a right to know “when, where, and how AI systems are being used to evaluate them, track them or make decisions about their educational future” (National Student Legal Defense Network, 2026). Nobody writes that sentence unless the current answer is no. The tools work; whether the student knows they’re running is a separate question, and mostly an unasked one. 

What Students Already Know… and What They Don’t 

Here’s the part that should recalibrate how institutions think about this; students are not the passive party in the AI story. A 2026 Digital Education Council survey that found 77% of faculty now use AI in teaching, also found 88% of students already using it in their own learning (Digital Education Council, 2026). This generation does not need to be introduced to technology. They are more fluent in it than most of the adults setting policy for them. Students know this, and that adds fuel to the fire. Maya isn’t confused about what AI can do, she’s frustrated that the institution gets to use it without the same disclosure it demands from her. 

Research on AI-assisted grading backs up the instinct behind that frustration. A small study looked at 27 undergraduate computer science students grading a programming project, not an essay, but it asked the same underlying question: do students trust AI feedback as much as they trust a human’s? Even when the AI’s scores and clarity ratings matched or exceeded a human teaching assistant’s, most students still preferred the human. Sixty percent of students rated the TA’s feedback as fairer, and 55% said they trusted it more overall (Riahi, Storozhevykh & Catete, 2026). They chose the grader who gave them worse marks and murkier explanations. 

Students Want the Why 

Students consistently pointed to the same specific frustration Maya has; the AI could tell them what was wrong, but not why it mattered, or what to do next. This is the kind of contextual judgment that comes from an instructor who knows where a particular student is in their development. That same complaint showed up, almost word for word, in an account heard directly while researching this piece. A parent said her daughter’s high school teacher admitted that an AI tool couldn’t grade this particular ninth-grader accurately; it couldn’t tell what the student already knew versus what she still didn’t. It could grade the words on the page, but not the student behind them. 

Jisc’s 2025 survey of student perceptions of AI found the same pattern. Students want clear institutional guidance on AI use and consistently say they value personalized, human feedback over automated alternatives. This is not because the automation is inaccurate, but because it can’t yet account for who they specifically are (Jisc, 2025). 

Why the Trust Gap Is Actually a Retention Problem 

It would be easy to file this under ethics and move on, but that undersells what’s actually at risk. Students who feel monitored or misjudged by systems they don’t fully understand rarely file a complaint; they disengage instead. Students are not measuring an institution’s AI governance against another university’s policy manual. They’re measuring it against every other digital experience they have every day, from their banking app to their streaming service. Held to that standard, an AI decision that feels opaque, inconsistent, or impossible to question doesn’t just cost the tool their confidence; it costs the institution behind it. 

The same can be said for faculty not enforcing bans they don’t believe in. A flagged essay with no explanation costs Maya half a letter grade, and it teaches her that the system’s judgments about her are unappealable. This lesson generalizes fast to other areas such as advising nudges, degree-progress flags, and every other place AI touches her file. Gen Z and Gen Alpha students are, by every available measure, more AI-literate and more skeptical of unclear automated decisions than most institutional AI rollouts assume. An institution that treats that skepticism as a communications problem rather than a design problem will keep manufacturing the mistrust it’s trying to avoid. 

What Human Agency Actually Requires 

“Human-centered AI” has become a phrase institutions attach to almost anything. For a student, it means three concrete things. 

The first is disclosure. A student should always be able to find out, without having to ask directly, when AI shaped a decision about them, such as a grade, a flag, a recommendation. The second is a working path to a human. Not a hidden one, not one that requires escalating through multiple offices, but an accessible route to someone with the authority to actually look again. The third is explainability calibrated to the stakes. A scheduling suggestion doesn’t need the same depth of explanation as a probation flag or a grade that affects a scholarship. Treating every AI touchpoint with the same disclosure process either buries the important ones in noise or makes the whole system too heavy to use. The standard should scale with what the student stands to lose. 

The first two of those are already written down. The Student AI Bill of Rights asks for disclosure, and it asks that “automated systems should not be the final arbiter of high-stakes decisions affecting a student’s admission, academic standing, financial stability or other aspects of fundamental well-being” (National Student Legal Defense Network, 2026). Which is to say the standard being proposed here is not a radical one, and institutions will not get to claim they were never told. 

None of this asks institutions to slow down AI adoption, rather it asks them to build the disclosure and recourse in from the start. This is generally the way a well-governed system is designed with an audit trail from day one rather than bolted on after something goes wrong. 

From far away, it can look like teachers wanting control over their own work and students wanting to be trusted are two completely separate issues; they aren’t. The exact moment Elena Marsh’s grading tool flags Maya’s essay is the same moment both stories collide; one instructor exercising legitimate professional judgment, one student on the receiving end of a decision she never saw coming.

That collision is discussed in Part 3 of this series. Read it now. 

Punch List 

Note: Maya Chen is a composite illustration informed by parent- and educator-reported experience with mandated AI tools and AI-assisted grading, not a named individual. The high school teacher’s account referenced above is a first-hand anecdote relayed to us during research for this piece, not a published or independently verified source — it’s included as illustrative color, not as data. 

Talk to Robots & Pencils about designing agentic AI for education. Request an AI Briefing. 

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 silent loop”?
A: The growing set of decisions touching a student’s academic life, a grade, a feedback flag, an advising nudge, a major-change suggestion, that AI increasingly shapes without the student being told when or how it happened.

Q: Do students actually trust AI-generated feedback?
A: Not as much as human feedback, even when it’s just as good. A study of 27 undergraduate computer science students found that even when AI feedback matched or exceeded a human TA’s accuracy, 60% still rated the TA’s feedback as fairer and 55% trusted it more (Riahi, Storozhevykh & Catete, 2026).

Q: Isn’t this generation comfortable with AI making decisions about them?
A: They’re comfortable using AI, not comfortable with asymmetry. 88% of students already use AI in their own learning (Digital Education Council, 2026), which makes them more attuned to, not less bothered by, an institution using AI on them without the same disclosure it demands from them.

Q: What does the Student AI Bill of Rights actually require?
A: Published by the National Student Legal Defense Network in April 2026, its first article states students have a right to know when AI is evaluating, tracking, or deciding their educational future, and that automated systems shouldn’t be the final arbiter of high-stakes decisions.

Q: Why treat this as a retention issue instead of an ethics issue?
A: Students don’t file complaints when they feel misjudged by an opaque system, they disengage. They measure institutional AI against their banking app or streaming service, not against a policy manual, and an unexplainable decision costs the institution their confidence.

Q: What are the three things human agency actually requires?
A: Disclosure (knowing when AI shaped a decision), a working path to a human who can look again, and explainability calibrated to stakes, a scheduling nudge needs less explanation than a probation flag or scholarship-affecting grade.

Key Takeaways

• The silent loop is AI shaping grades, advising nudges, and early-alert flags with no disclosure built in, not by malice, but because no one’s job is to notice the gap.
• Students already outpace institutions in AI fluency (88% vs. 77% of faculty), which sharpens rather than dulls their frustration at asymmetric disclosure.
• Even when AI feedback is equally or more accurate, students trust it less: 60% rated human TA feedback as fairer in a 2026 study.
• The Student AI Bill of Rights (NSLDN, April 2026) already sets the standard: disclosure, and no automated system as final arbiter of high-stakes decisions.
• Human agency requires three things scaled to stakes: disclosure, a real path to human review, and explanation depth proportional to what’s on the line.

Where faculty agency meets student trust 

his 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 1: The Detection Default | Part 2: The Silent Loop 

Here is the moment, the collision point, stated plainly. Elena Marsh, exercising the exact discipline-specific judgment Part 1 argued she should have, uses an AI-assisted feedback tool to help her manage grading load across 90 first-year essays. It’s a reasonable professional choice, made in good faith, inside a policy her department endorses. Maya Chen, sitting on the other side of that same decision, receives a grade shaped in part by a tool she was never told was involved and with no path to ask why. Both things are true in the same instant. Elena is exercising legitimate pedagogical agency. Maya is a student having a decision made about her by a system she can’t see. Neither of them did anything wrong. The institution simply never designed for the fact that its faculty-agency policy and its student-trust policy would collide in this room, over this piece of work. 

Two Workstreams, One Collision Point 

Most institutions treat faculty AI policy and student-facing AI transparency as two different projects, run by two different offices, and on two different timelines. Faculty policy usually lives with the Provost or a Center for Teaching and Learning. Student-facing disclosure, when it exists at all, tends to live with IT, the Registrar, or student affairs. It often doesn’t exist as a formal policy so much as an assumption that someone else is handling it. Each office can point to real progress on its own workstream. Neither has been asked to think about the moment those two workstreams meet; the instant an instructor’s tool becomes a student’s outcome. 

This is the same structural blind spot The Institutional Intelligence Crisis found across university operations: departments run independently, and no one is responsible for what falls through the cracks between them. In the classroom, that crack isn’t between departments; it’s between the person given the power and the person affected by how they use it. Almost no institution has a governance table where both of them sit. 

Why the Fix Isn’t Another Committee Handing Down Rules 

The instinct, once an institution notices this gap, is to convene an IT-and-Provost governance committee and issue joint guidance. That instinct reproduces the exact failure both prior pieces in this series documented; policy written by the people furthest from the room, applied to the people standing in it. A governance model built to hold faculty agency and student trust together must include faculty senate representation, because faculty are the ones who must live inside whatever gets decided, and it must include actual student voices. And not just a single student representative added to satisfy an optics requirement, because students are the ones the decisions land on. 

This is structurally different from the accountability-owner model that works for administrative AI. A single named owner for a workflow tool makes sense when the tool serves one office and one function. That structure doesn’t work here, because the classroom isn’t one function; it’s two people with different relationships to the same decision. A governance structure that represents only one of them will keep producing policy that only looks complete but functions incompletely. 

A Standard Simple Enough to Actually Adopt 

The practical version of this doesn’t need to be complicated, and it shouldn’t wait for a perfect governance model to be built before any course adopts it. Any instructor, in any discipline, can commit to two things without needing campus-wide uniformity on how AI gets used. One, tell students what AI was used for on a given piece of work, and two, give them a real, findable way to ask for a second look if they think it got something wrong. 

That’s the whole standard. It doesn’t require Elena to disclose her exact tool stack or her grading workflow in granular detail. It doesn’t require the registrar to build a new system before anyone can use it. It requires the two things students in Part 2 said they wanted; to know how the decision was reached (disclosure), and to have somewhere to go to ask questions (recourse). Institutions already building agentic systems with real governance, which includes identity, oversight, and an audit trail designed in from the first sprint rather than bolted on after a trust failure, tend to treat this kind of disclose-and-recourse checkpoint as a basic architectural requirement. Classroom AI deserves the same standard the best-engineered institutional systems already hold themselves to. 

Scaled up, that same logic becomes the governance table’s actual job. Which is not dictating how every course uses AI, but making sure every course, regardless of how it uses AI, meets that standard. 

Designing the Relationship, Not Just the System 

The thread running through all three pieces in this series is the same; human-centered agentic AI in higher education is not primarily a data architecture problem, and it’s not primarily an operations problem; it’s both. Both of those are real, and both are already being worked on elsewhere. It’s a relationship problem, between two people who are physically in the same room and structurally treated as if they’re solving two unrelated problems. 

ASU’s framing for its Agentic AI and the Student Experience summit this October puts it well; the goal is designing AI systems that “enhance human agency, expand access, and strengthen learning in meaningful ways” (ASU, 2026). That framing only works if “human agency” means both humans in the room; the instructor deciding how AI belongs in her discipline, and the student who deserves to know when it’s being used on her. Institutions that get this right will avoid a trust problem, and they’ll have a classroom-level foundation solid enough to make everything already being built at the operational layer worth scaling. 

The institutions that solve this well won’t simply have better AI governance. They’ll strengthen one of the most important relationships on campus: the trust between faculty, students, and the institution itself. That trust becomes the foundation for every future AI initiative. 

As institutions move from AI experimentation to enterprise adoption, classroom governance will become one of the earliest indicators of whether AI can be scaled responsibly across the institution. Education leaders ready to design AI governance that faculty trust, students understand, and institutions can confidently scale can request an AI Briefing with Robots & Pencils.

Punch List 

Note: Elena Marsh and Maya Chen are composite illustrations carried through from Parts 1 and 2, not named individuals. 

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 collision point”?
A: The exact moment a faculty member’s legitimate AI-assisted grading choice becomes a student’s outcome, without the student ever knowing a tool was involved or having a way to ask why. Faculty agency and student disclosure aren’t separate problems, they meet in the same room, over the same piece of work.

Q: Why can’t a joint IT-and-Provost committee just fix this?
A: Because that reproduces the exact failure Parts 1 and 2 documented, policy written by people furthest from the classroom, applied to the people standing in it. A governance table needs faculty senate representation and real student voices, not one token student seat.

Q: What’s the actual two-part standard being proposed?
A: Any instructor, in any discipline, can commit to two things without campus-wide uniformity: tell students what AI was used for on a given piece of work, and give them a real, findable way to ask for a second look.

Q: Does this require a new system or registrar build-out?
A: No. It doesn’t require disclosing a full tool stack or grading workflow in detail, and it doesn’t require IT to build anything new before an instructor can adopt it. It’s disclosure plus recourse, nothing more.

Q: Is this a data problem or a relationship problem?
A: Both are real, but the series argues it’s primarily a relationship problem, between two people physically in the same room who are structurally treated as if they’re solving unrelated problems.

Q: How does this connect to institutional AI governance generally?
A: The same disclose-and-recourse checkpoint that well-engineered agentic systems already build in from the first sprint (identity, oversight, audit trail) should apply to classroom AI. Getting this right becomes the foundation for scaling every other AI initiative on campus.

Key Takeaways

• Faculty agency (Part 1) and student trust (Part 2) aren’t two separate governance tracks, they collide in the same classroom decision, and most institutions have never designed for that collision.
• Faculty policy and student disclosure usually live in different offices on different timelines, with nobody responsible for what falls through the gap between them.
• A single named tool-owner model works for administrative AI but fails here, because the classroom involves two people with different relationships to the same decision.
• The standard is simple enough for one instructor to adopt without campus-wide overhaul: disclose what AI was used for, and provide a real path to ask for a second look.
• Getting this right isn’t just better governance, it’s the trust foundation every future AI initiative on campus depends on.