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Course Syllabus: Language and Policies

A student working on coursework on her computer.

As AI and machine-learning tools such as Microsoft Copilot, Google Gemini, and more continue to evolve and expand their capabilities, faculty are asked to consider the role these tools will play in their courses and how best to manage their use in ways that support teaching and learning.

Since policies will vary across courses and instructors, it is important that faculty communicate with students clearly and early in the semester. It is also important to consider that students will be navigating multiple courses with varying AI policies which can be confusing if guidance is inconsistent or unclear.

To help students understand what AI use will look like in a course, faculty should develop a clear and consistent AI policy that students can easily reference throughout the semester and revisit as needed. In addition to a syllabus statement, we also recommend incorporating specific AI guidance within individual assignment guidelines as well.

Common AI Policy Frameworks

Most often, AI usage in a course falls in one of the three categories below. When developing your own policy, it can be helpful to look through these to determine which one aligns closest with the expectations for your course.

Another option is to adopt a tiered framework for AI use across course assignments, with expectations adjusted based on the purpose and learning goals of each assignment. The AI Assessment Scale, developed by Mike Perkins, Leon Furze, Jasper Roe, and Jason MacVaugh, offers a structured model that helps both instructors and students understand appropriate levels of AI involvement in their assessment design. Similarly, the Spotlight Model uses a stoplight metaphor to illustrate varying degrees of permitted AI use across assignments.

Sample AI Syllabus Statements

Review the sample AI syllabus statements below for guidance. These examples are intended to serve as starting points and may be adapted to align with your course goals, teaching approach, and expectations for student use of AI. Faculty are encouraged to revise the language as needed before incorporating it into their syllabus.

The following document, created by Lance Eaton, Senior Associate Director of AI in Teaching and Learning at Northeastern University, offers examples of AI syllabus statements tailored to different levels of allowance across a variety of academic disciplines. This resource can serve as a valuable guide when crafting your own AI syllabus statements for your courses. When establishing your policy, it’s crucial to communicate this decision with your students and, as mentioned earlier, include reminders about AI usage in your assignment instructions as well.

Important Note on AI Detection Tools

At Stony Brook, Turnitin’s AI detection  is available and can be enabled for assignments in Brightspace. With that said, AI detection tools are never perfect and can at times result in false positives and/or false negatives. We do not recommend using AI detection tools as the sole means of determining academic dishonesty. To ensure fairness and accuracy, we recommend following our AI-Flagged Paper Evaluation Process. We do not recommend the use of AI detection tools that have not been approved by the university.

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