Author: NCFDD
A faculty member deciding how students can use AI in a course may also need answers to questions that extend beyond that classroom. Which tools has the institution approved? What should students disclose? Where can an instructor turn when a course policy raises questions that existing guidance does not address?
Institutional AI guidance can give faculty a clearer starting point for those decisions. For provosts, deans, faculty affairs leaders, and department chairs, developing that guidance means working through which expectations should apply across campus and where disciplinary expertise should shape the approach.
Faculty are already making many of these choices. In NCFDD’s Faculty Perspectives on AI report, 71.2% of respondents say they set their own AI policy for students, while 8.5% follow a university-wide policy. Yet only 6.6% say AI-related expectations are consistent across their institution.
Those findings raise a practical question for campus leaders: What shared foundation would help faculty make and explain decisions that fit their courses?
Clarify what institutional AI guidance covers
Differences among course policies can serve an educational purpose. An introductory course may require students to practice a skill independently. An advanced course may ask them to evaluate how AI performs a related task. Faculty need room to establish expectations that reflect those differences.
Uncertainty becomes harder to manage when the boundaries themselves are unclear.
“The boundaries everyone is working under, I think we all have different ones and they are unspoken,” a humanities respondent observed in the report.
Leaders can begin by identifying the questions that require an institutional answer. Guidance could explain which tools are approved for particular uses, where existing data-handling rules apply, and how faculty should seek help with unresolved questions. It should also state which decisions faculty retain about teaching and assessment.
That distinction needs to be visible in the guidance itself. If faculty can adapt a syllabus statement, say so. If an institutional requirement applies regardless of course policy, explain its scope and where to find further information. Faculty should be able to distinguish a requirement from a recommendation without having to interpret several disconnected documents.
Give faculty adaptable examples
A statement of principles can establish direction, while examples help faculty work through what those principles mean in practice.
Consider guidance asking students to disclose AI assistance. Faculty may still need to decide what counts as assistance and how students should document it. Examples could show how disclosure might work for brainstorming, editing, or generating material that becomes part of a submission. Instructors could then adapt the language to the assignment.
Lisa Hanasono, PhD, NCFDD’s Academic Director of Training and Content and a professor at Bowling Green State University, described the balance this requires:
“Providing faculty with a framework is great, but also giving them some license in terms of determining for them what makes the most sense for their classes, and what makes the most sense for their students,” she said.
An institution could offer sample syllabus statements covering several approaches, with explanations of the learning goals each might support. Departments could contribute examples that address questions specific to their fields.
This also gives faculty a way to build on colleagues’ work. Karenanna Boyle Creps, PhD, Subject Area Leader of Arts Integration in the Department of Teacher Education at Michigan State University, described how faculty at her institution share and revise syllabus-language options as their approaches develop.
“Every semester, somebody’s written a new version, and they share it,” Creps said in the report.
A shared collection of examples can make those revisions available beyond the individual conversations in which they begin.
Help students navigate course-level AI expectations
Students may encounter several reasonable AI policies in the same semester. Institutional coordination can help them understand how to navigate those differences.
For example, an institution could recommend that every course explain where AI is permitted, what students must complete independently, and how to ask about a use that falls outside the stated examples. Faculty would determine the answers for their courses, while students would know what information to look for.
Departments can also examine expectations across a course sequence. If an introductory course limits AI to support foundational practice, how will a later course explain a different approach? Faculty teaching those courses may benefit from discussing the progression together.
Connecting AI expectations to learning goals helps students understand why the rules may differ across courses. Leaders can support faculty in making that reasoning explicit in syllabi and assignment instructions.
Involve faculty while decisions are taking shape
Faculty participation needs to begin while there is still room to influence the guidance.
A proposed policy may seem straightforward until instructors consider how it would apply to a laboratory assignment, a clinical setting, or a large introductory course. Those discussions can reveal where language needs clarification and where a shared requirement would create difficulties.
Leaders can bring draft guidance to existing faculty governance bodies and departmental discussions, asking faculty to test it against actual situations. Participation should include faculty with different levels of AI experience, including those who have chosen to limit its use. Their questions can help identify assumptions that a group of frequent users might overlook.
Department chairs can provide a connection between these conversations and institutional decisions. They can gather recurring questions from faculty, identify issues that need a broader answer, and explain how faculty input affected the resulting guidance.
When a suggestion cannot be incorporated, explaining the reason helps faculty understand the decision and the constraints behind it.
Establish a process for questions and revision
Institutional AI guidance will need attention as tools and disciplinary practices change. Faculty should know who maintains it and how to raise an issue between scheduled reviews.
A useful starting point is a central page that identifies current guidance, links to adaptable resources, and names a contact for unresolved questions. Updates should explain what changed and whether faculty need to take action.
Leaders can also ask faculty to assess whether the guidance is helping. Can they find an answer to a common question? Do they understand which choices remain theirs? Are the examples relevant to the courses they teach?
Faculty also need time to apply revised guidance to their courses. A departmental working session could give instructors time to review examples and revise course language together.
For campus leaders, a practical next step is to bring current guidance into a faculty discussion and work through a few recurring questions. That conversation can show where the institution needs to provide an answer and where faculty need better resources to exercise their judgment.
Download Faculty Perspectives on AI for more findings and faculty perspectives on institutional guidance and support.
