It is a common story in higher education today. You assign a paper, a reflection, or a coding project. When you grade it, the writing is perfect—but it feels empty. It reads like a computer wrote it.

The truth is, a computer probably did write it.

With Generative AI tools at their fingertips, students can generate a completed assignment in seconds. If we only grade the final product (the “output”), we are no longer grading student learning. We are grading their ability to type a prompt.

As faculty, how do we move forward? The answer isn’t to become “AI police.” Instead, we need to guide our students to use AI wisely. Here is how we can shift our focus from policing to pedagogy.

Flip the Focus: Grade the Journey, Not Just the Destination
If AI can instantly create the final product, we have to start grading the process of learning. Here are three practical ways faculty are doing this right now:

• Break It Into Milestones: Instead of grading one final draft, grade the steps. Give points for brainstorming notes, outline changes, and rough edits. Look for the human footprint in how the ideas grew over time.
• Add Audio and Video: Ask students to record a quick two-minute audio clip or video reflection. Have them explain their work or do a “think-aloud” about how they solved a problem. AI can write an essay, but it cannot mimic a student’s personal explanation of what they learned.
• Run an “AI Audit”: Embrace the tool openly. Ask students to use an AI program to build a baseline draft or map out a project. Then, make the actual assignment about critiquing the AI. Have them track what the AI got right, where it made up facts (hallucinations), and how they fixed it.

The L.O.V.E. Framework for the Classroom
When you introduce AI to your students, you can use a simple framework to guide them toward critical thinking. It is called the L.O.V.E. model:

• L – Logic: Remind students why the assignment matters. AI should be used as a personal tutor to help them understand a concept, not a ghostwriter to do the work for them.
• O – Originality: Encourage students to bring their own lives into their work. Tie assignments to local community issues or personal stories. AI doesn’t know your students’ unique backgrounds.
• V – Verifiability: Teach students to treat AI text like an unverified rumor. They must fact-check everything, spot errors, and prove their points using trusted sources.
• E – Ethics & Equity: Show students that AI models are trained on old data that often contains biases. Help them look closely at AI answers to see who is being left out or misrepresented.

Free Resources for Your Toolkit
You do not have to figure this out alone! Campus support teams around the country have built incredible, free guides to help you bring these conversations into your classroom. Take a look at these resources to kickstart your planning:

• The L.O.V.E. Framework
• University at Albany: Talking to Students About AI
• Drexel University: Dragons’ Guide to Generative AI in Teaching and Learning
• National Joint Project (Elon University, AAC&U, Princeton): AI Guide for Students
• Elon University: Human Wisdom Self-Assessment Tool

By changing our approach from catching AI users to coaching critical thinkers, we can help our students build the exact skills they need for a changing world: discernment, human reasoning, and deep learning.

Looking for a thought partner?
The CTLE team can help you generate new ideas, revise assignments, and thoughtfully integrate AI into your teaching. We are here to support you!
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