Whether you plan to keep AI out of your coursework, permit it for specific tasks, or make it part of your course learning outcomes, students need to know what you have decided. That can be harder than it sounds. AI features now appear in search engines, writing software, library platforms, web browsers, and common productivity tools. A statement such as “AI is not allowed” may leave students wondering whether that includes Grammarly, Microsoft Word’s writing support, Google AI summaries, or tools embedded in research platforms. A statement that permits AI can raise different questions: Which tools? For what purposes? On every assignment? With what kind of disclosure?
Faculty will make different decisions about AI, and those decisions may change by course or assignment. Students need expectations they can understand, find again, and apply to the work they are being asked to do. With cultivating a student-centered learning environment being one of the anchors of Teaching Effectiveness at UMSL, clear communication of expectations is an important way to set the tone and ensure students have the information they need to be successful. The CTL has developed a collection of resources to help faculty work through these decisions and communicate expectations clearly.
Communicating Expectations through Course Policy
Students need to know what AI use means in the context of your course. A policy should explain whether AI is permitted, which tools and uses are covered, whether expectations change by assignment, and what students should do when they are unsure. We encourage faculty to establish AI expectations regardless of whether AI is prohibited, permitted in limited ways, or integrated into learning activities.
To help with this process, the CTL developed a Course AI Policy Builder Checklist, which shifts the broad question, “What is my AI policy?” into five more manageable decisions: What role, if any, will AI have in this course?
- Which tools and built-in features does the policy cover?
- Which uses are allowed, limited, or prohibited?
- What disclosure, citation, or process documentation is expected?
- Where and when will students encounter the policy?
Crafting Language to Communicate Your Expectations
Once you have made the central decisions, the next step is putting them into language students can understand and apply. Your policy should align with your learning outcomes, assignments, discipline, and departmental expectations. It should also be specific enough that students can tell what is permitted without having to guess whether a particular tool or use falls within the policy.
To support this process, the CTL offers the Artificial Intelligence Course Policy Examples, which mirror the checklist, and the Sample Generative AI Syllabus Statements, which provide historical language from courses at UMSL and peer institutions. These resources are intended as starting points rather than ready-made policies. Faculty should adapt the language to fit their teaching context.
If you permit AI, the AI Disclosure Options resource provides several ways for students to explain how a tool contributed to their work, ranging from a brief acknowledgment to a process reflection or documentation of an AI conversation.
Revisit Expectations During the Semester
A syllabus policy gives students a starting point, but they may need more information when beginning a particular assignment. A short reminder in an assignment prompt, rubric, Canvas announcement, or class discussion can help students apply the course policy to the work in front of them.
Before an assignment begins, consider whether students can answer these questions:
- May I use AI for this assignment?
- Which tools and features count as AI?
- What kinds of use are acceptable?
- What work must I complete independently?
- Do I need to disclose or document my use?
- Who should I ask if I am unsure?
The Revisiting AI Expectations in Assignment Briefs resource provides a communication checklist and sample language that can help faculty restate expectations and explain how students should disclose permitted use.
If a Submission Raises Concerns
Concerns about possible AI misuse can be difficult for both faculty and students. Before drawing a conclusion, return to the course policy, assignment instructions, and the information you can observe in the work.
AI detection tools are not reliable evidence that a student used AI and should not be used to determine misconduct. Instead, consider identifying the specific features that require clarification:
- Are cited sources missing, inaccurate, or unverifiable?
- Does the submission respond to the assignment?
- Are claims unsupported by evidence?
- Does the work demonstrate the skills the assignment was designed to assess?
- Can the student explain the reasoning, evidence, or process behind the work?
The Suspected AI Use Quick Conversation Guide helps faculty prepare for a direct, non-accusatory conversation with the student. The purpose of the conversation is to understand how the work was completed, not to confirm a conclusion made in advance. This approach is consistent with UMSL's academic dishonesty guidance, which directs faculty to present the specific information prompting concern and provide students an opportunity to share their perspective.
Future Resources
We hope these resources serve as useful starting tools that empower instructors at UMSL to set and communicate expectations in their courses. If these resources have been helpful, consider sharing your success stories with us so we can continue building a repository of language and approaches that have worked. We also encourage you to connect with us through consultations and share ideas for future tools and resources that can support your teaching journey.