For the last few years, I have been grappling with the response to artificial intelligence,  both as an educator and professional. How do we take this disruptor and bring it into our curriculum responsibly, when it can so easily replace critical thought and conceptual development? Where ignoring it fails, refocusing seems to function.

In my courses on graphic design, I have brought AI into aspects of lessons, discussions, and projects with the purpose of reframing the technology as less a brain replacement and more a piece of a larger process. This approach allows students to engage with the technology in an environment where the goal is experimentation and discovery, ultimately positioning AI as one piece of a larger, conceptual process.

How To Bring AI Into the Classroom and Retain Pedagogical Integrity

In the following examples, I document a few of the approaches I take to address my students and their approach to emerging technologies. While these come from my design courses, the framework can be applied to a variety of disciplines.

Treat AI As What It Is

Generative AI is good and bad at a lot of things. One of the more impactful lessons in Survey of Graphic Design, an art history course, aims to reveal how the machine thinks. The idea is based on a simple premise: if generative AI is trained on the whole of the internet, then it ought to return something average.

After a lecture on propaganda, I give a live demonstration of generating images where the prompt includes the word “propaganda.” The images AI conjures are now objects that contextualize the lesson, showing my students examples of visual languages and symbols, as well as showing them some of the assumptions the technology makes, ascribing socially and politically charged values to seemingly innocuous search terms.

A Friendly Engineer

In an early course within our program, Graphic Design I, I assign my students a data project where the goal is to capture raw data relating to their own life and present it visually. During a guided working session where I give loose directions, one of the steps requires the students to engage AI with their data. This allows me to illustrate the difference between discriminative and generative AI models. If we don’t want the machine to do our thinking, then let it do what it’s always done well: catalog and identify patterns in sets of data. In the classroom, this looks like the students uploading a spreadsheet and then prompting AI with questions like, “What implications does this data say?” and “What would be compelling qualitative data to supplement this?” This has the benefit of restricting AI to the process phase of the assignment, where the machine functions like a peer, allowing the students to bring some or none of the pieces to the final work.

A Child With a Pen

In our writing intensive courses, I generally restrict AI usage. However, there are moments where AI creates more robust and engaging learning opportunities. In one assignment, I used to require my students to research an artist or movement they found interesting. Now, step one of that assignment is to have AI generate a 500-word essay on the student selection. Then, my students get the chance to respond to the generated text, highlighting inaccuracies, challenging the core principles of the subject, and adding context where it is missing. I have found that this creates a more dynamic assignment, allowing the students to treat AI almost like competition to debate. 

Conclusion

It’s one thing to tell students they can use AI. However, it can be empowering to situate AI as a counterpart, encouraging competition between students and generated work. I have found students take great pride in their own work when they can directly compare their writing with automated text. If it isn't debate and response style, think of way students can engage AI generated content as it relates to your field allowing them to be humans in response.

References and Resources

These ideas were presented as part of Jonathan’s AI Teaching Talk “Process-Oriented AI in Teaching to Build Thinking, Not Shortcuts” on January 30, 2026.

About the Author

Jonathan Cooper is an Assistant Professor of Graphic Design at the Department of Art, Art History and Design. He runs his creative practice, BirdBomb Studio with his creative partner, Jesse Augustine. His research focuses on the intersection of design and technology. Off campus, you can find him at hardcore concerts, binging fiction, and drawing cats for his kids.

Others may share and adapt under Creative Commons License CC BY-NC.

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