The 3Ms Framework: How to Handle AI Violations Fairly in Your Classroom

Decision tree on an easel illustrating the 3Ms Framework for fair responses to AI violations in student work.

When you identify AI-generated content in a student's work, the next steps aren't always clear. Most academic integrity policies predate generative AI tools, leaving educators to make significant decisions without consistent guidelines. The 3Ms Framework provides a structured, fair approach to these situations.

What the framework does

This one-page decision tree guides you through three critical questions: Did the student know it was against policy? Did they intend to deceive or gain an unfair advantage? Have they shown this pattern before? Your answers lead to proportional responses, from educational conversations to formal consequences.

The framework distinguishes between students who made honest mistakes and those who deliberately violated policy, ensuring fair outcomes and defensible decisions.

Why educators find it valuable

  • Consistent decision-making. Clear criteria replace subjective judgment, giving you confidence when explaining your reasoning to students, colleagues, and administrators.

  • Educational focus. The framework identifies when a violation reflects a gap in understanding rather than intentional misconduct, allowing you to teach responsible AI use instead of defaulting to punishment.

  • Fairness across cases. Whether this is your first AI integrity concern or your twentieth, the framework ensures similar situations receive similar treatment.

  • Efficiency. A structured process saves time and mental energy when evaluating potential violations and determining appropriate responses.

When to use it

Reference the framework when reviewing assignments that raise concerns, during academic integrity meetings with students, when training teaching assistants on policy application, or when developing course guidelines for AI use. The approach works across disciplines and educational contexts.

Download the 3Ms Framework

Developed by Alym Amlani and Paul Davis to support educators in responding to AI integrity concerns with clarity, consistency, and fairness.

Questions about using this framework in your setting? Contact me.

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