As you finalize your course outlines for the upcoming semester, we encourage you to include a statement that provides guidance for the appropriate use of AI. Your AI statement is an opportunity to begin a dialogue with students about making ethical, deliberate choices. Consider co-designing these guidelines with your students during the first week of classes, and plan to revisit and adapt them as the term progresses. Following are two models that can help you establish a common ground.

Two models

1. The AI Assessment Scale (AIAS) v2.1 🔗

AIAS is a five-level scale from no AI assistance to full AI collaboration. Each level describes a different kind of assignment task, not a degree of permission. The scale helps educators consider how their assessment might need to be adjusted considering GenAI tools, and clarity to students how and where GenAI tool might be used in their work. Note, we shared the original 2023 AIAS in a previous blog. If you have been using the AIAS, consider revisiting the updated as v2.1.

2. The AI Stoplight 🔗

This visual model categorizes classroom tasks into three colour-coded permission levels.

• Red Light: Generative AI is prohibited because the task is designed to support foundational learning, personal reflection, or critical thinking.
• Yellow Light: AI tools may be used in limited, specified ways (such as brainstorming, outlining, or proofreading) but must be openly disclosed and cited.
• Green Light: AI use is permitted and encouraged for this assignment.
  • Note: AI tools may only be required if they have a completed Privacy Impact Assessment (PIA). Otherwise, students must be provided with an alternative way to complete the assignment.

Aligning policies with your course learning goals 

An effective AI policy is never generic. It should align with what you want students to learn. When students understand the pedagogical why behind the boundaries, they are more likely to make responsible choices. Following are three examples of how to align your policy with specific course outcomes. 

AI Stoplight examples

1. Writing-focused policy (e.g., literature or creative writing)

Course learning outcome: Draft original analytical essays that demonstrate a distinct, authoritative voice and critical reasoning, by careful reading and synthesizing course texts. 

AI statement: Writing is an essential vehicle for thinking. Because the objective of this course is to cultivate your unique human voice and original critical analysis, the use of generative AI to write draft assignments or summarize readings is prohibited (Red Light). Outsourcing this work denies you the cognitive practice needed to grow as a thinker. However, you are welcome to use basic spelling and grammar checkers (Yellow Light) to polish your own completed drafts. Our focus is on the human ‘aliveness’ and original perspective of your ideas. 

2. Application-focused policy (e.g., business analytics or design) 

Course learning outcome: Construct strategic business plans that meet industry standards by using emerging digital technologies to analyze complex market datasets. 

AI statement: To prepare you for the modern workforce, we welcome the thoughtful integration of generative AI tools in this course (Green Light). Using AI to brainstorm strategic ideas or analyze datasets directly supports our course learning goals. However, you are solely responsible and accountable for the accuracy of what you submit. Generative AI frequently hallucinates fake facts, numbers, and citations. You must verify every output against reliable, original sources. Every assignment using AI must conclude with an AI disclosure statement detailing the tools used, your prompts, and your verification process. 

3. Trades-focused policy (e.g., automotive service technician) 

Course learning outcomes:  

Theory: Analyze computer-controlled vehicle network faults to manufacturer diagnostic standards by interpreting wiring diagrams and system parameters during written unit exams. 

Practical: Execute precision circuit measurements to industry safety tolerances using digital multimeters on live shop vehicles 

AI statement: In the skilled trades, your professional competence and workplace safety rely on alignment of your head (technical knowledge), your hands (practical skill), and your heart (pride in safe, ethical craftsmanship). Automotive service relies heavily on digital technology, and AI-powered diagnostic assistants and troubleshooting chatbots are rapidly becoming standard tools on the shop floor. We want you to be fluent in these industry technologies, which is why our policies are designed to build the foundational knowledge you need to use them safely and critically. 

• Theory Tests and Unit Exams (Red Light): The use of any generative AI or digital assistants is prohibited during all written quizzes, unit exams, and safety tests. These assessments are designed to measure your personal, independent technical knowledge. When you challenge your provincial certification exams or troubleshoot a vehicle on a busy shop floor, you must rely entirely on what is in your own head.  

• Exam Prep and Study Support (Yellow Light): AI may be used as a study coach to prepare for your exams. You may use AI tools to generate practice quizzes, explain complex technical service bulletins, or brainstorm potential mechanical causes for unusual vehicle symptoms. When using AI for study prep, focus on learning how the system works so you are fully prepared to demonstrate that knowledge independently on test day. Generative AI frequently hallucinates fake facts, numbers, and citations. You must verify every output against reliable, original sources such as your course manuals and Skilled trades learning resources. 

• Practical Shop Work (Yellow Light for practice in the shop, Red Light for assessment): In our shop, digital diagnostic assistants (Yellow Light) may be used to troubleshoot and interpret technical service bulletins or navigate complex wiring schematics. However, during graded hands-on diagnostic tests, AI tools must be put away (Red Light). You must demonstrate that you have the fundamental troubleshooting and measurement skills to safely work on vehicles independently. 

AI syllabus resources 

You do not have to build your policy from scratch. The educational community has collaborative and evolving resources designed to help you draft and customize your AI statement. Consider using the resources below as a starting point. 

Creating your course policy on AI | Teaching Commons. This post offers a practical roadmap for designing a student-centered AI syllabus policy that protects academic integrity while serving as an active starting point for transparent classroom conversation. 

AI Syllabus Language Guide. This comprehensive guide provides educators with a practical, step-by-step roadmap for building a transparent AI syllabus policy, with visual tools like the AI Stoplight Model. 

Classroom Policies for AI Generative Tools. This resource highlights a range of educator-developed policies for using AI-generative tools, helping instructors create or refine their own. Click the policies tab to view the collection. 

Generative AI syllabus statement generator. This syllabus statement generator tool assists with creating language for an AI syllabus statement.  

Classroom Policies for Generative AI Tools. This crowdsourced Google document shows policies from various educators on generative AI tools. The resource is designed to help instructors create guidelines for using these tools. Visit the Policies tab to view the collection. 

Student Use of AI: A Helpful Framework | Edutopia.  This decision tree guides students to ask key questions about their AI use and to reflect on how it benefits their learning.

Looking Ahead 

In upcoming blog posts, we will take a closer look at the AI Assessment Scale (AIAS) v2.1, and strategies for documenting and disclosing AI use.

We are collecting examples of course AI use policy statements that are working well in practice. Sharing your policy helps us learn from one another and build a collection of examples that can support instructors across VIU. To contribute, submit your examples through our anonymous Microsoft Form [link].

Attribution

Bridgeman, A., & Liu, D. (2025). The Sydney Assessment Framework. The University of Sydney. https://educational-innovation.sydney.edu.au/teaching@sydney/the-sydney-assessment-framework

Corbin, T., Dawson, P., Nicola-Richmond, K., & Partridge, H. (2025). ‘Where’s the line? It’s an absurd line’: towards a framework for acceptable uses of AI in assessment. Assessment & Evaluation in Higher Education, 50(5), 705–717. https://doi.org/10.1080/02602938.2025.2456207

Eaton, L. (2026). Classroom Policies for AI Generative Tools—Google Docs. Retrieved July 28, 2023, from https://docs.google.com/document/d/1RMVwzjc1o0Mi8Blw_-JUTcXv02b2WRH86vw7mi16W3U/edit.

Winkel, D. (2026, March 31). Your AI policy probably does not work. Here is how to fix it: A simple framework and three copy-paste policies, from a single course to an entire institution. Substack. https://substack.com/@doanwinkel

Disclosure: The AI statement examples in this article were generated with the assistance of Gemini Notebook (formerly known as NotebookLM), an AI-powered research and writing assistant. These examples were iteratively refined and verified by the author for accuracy and alignment with pedagogical goals.