Design Smarter, Not Harder: Using AI to Redesign Your Syllabus This Summer

Here are two simple yet powerful ways you can use AI to redesign your syllabus: one rooted in creativity and fresh ideas, the other focused on improving what you already have.

Summer can be more than just a break. Summer offers us the time to breathe, reflect, and rethink how we teach. It's a good moment to reassess our courses. What worked? What felt stale? What could be done differently? If you’ve ever opened an old syllabus and felt that tug to reinvent it—but didn’t quite know how—this post is for you.

When I talk to faculty about AI, I often hear the same hesitation: “I don’t want robots writing my course.” And I agree—your course should sound like you. But here’s the thing: AI isn’t here to take over your course. It’s here to help you see it differently. Think of it as a brainstorming partner, not a replacement.

✨ Approach One: Imagine the Course You’ve Never Taught

It’s easy to default to what we’ve done before. After all, the syllabus is familiar, and we’re busy. But sometimes, what we need most is the chance to think wildly—to imagine our course if we had no constraints, no history, no default assignments.

That’s where large language models (LLMs) come in. Their power lies in making unexpected connections—introducing you to ideas you might never have considered. Start by telling the AI a little about your course: the level, the topic, the duration. Then ask it to act like a globally experienced instructional designer and pitch you a creative redesign.

Example prompt:

“I teach a 12-week in-person undergraduate course on environmental policy. You are a creative instructional designer with international experience. Design a radically creative syllabus, including topics, assignments, and any other essential details.”

What I get back isn’t a final product—it’s a springboard. Sometimes the topics are ones I’d never include. Sometimes the assignments feel offbeat. But that’s the point. It jolts me out of the rut and helps me rethink structure, pacing, and engagement.

Here's a template for you:

I teach a [length of course] [type of course] [course level] course on [course topic]. 

You are a creative instructional designer with international experience.

Design a [fresh, innovative] [radically creative] syllabus for me, including topics, assignments, and any other essential details.

When I tried this prompt on my AI for Healthcare Professionals course, I got something really different. Here's the course philosophy that ChatGPT created:

This isn’t your typical AI course. It’s a creative laboratory.

You are not just a learner — you are a designer, critic, and futurist.

Each module is a chapter in a speculative narrative, where you must engage with GenAI tools to explore what healthcare could become — and what it should never become.

Just the language is so innovative and something I might not have thought of. But it's definitely an engaging approach for learners.

🔍 Approach Two: Start with What You Already Have

The other path is more grounded. If you already have a course you like—or at least want to improve—it’s time to do a little diagnostic work.

Pull up your syllabus. Now, pull up any course evaluations, notes you made during the semester, or student feedback you remember. Where did students get stuck? What assignments didn’t land? What policies felt like overkill (or went ignored altogether)?

Instead of guessing how to fix it, you can ask AI to review your syllabus as if it were a teaching consultant. One of my favorite prompts is:

“Here’s my current syllabus, course evaluation, and some notes from student evaluations. What parts of the syllabus could be redesigned to better align with student needs and engagement?”

You can even roleplay with the AI—ask it to take on the perspective of a first-gen student, or an academic director, or a peer reviewer in your department. The feedback it offers isn’t always perfect, but it’s often illuminating.

But don’t stop there—be specific about what kind of feedback you want. The more guidance you provide, the more actionable the response will be.

Try something like:

“Here’s my current syllabus. Based on this, tell me:

– What are the weaknesses in this syllabus?
– What’s missing that could enhance learning?
– What changes could strengthen the structure, pacing, or engagement?”

This gives the AI a clear framework to work within—and helps you get feedback that goes beyond surface-level edits. You can even take it further and roleplay:

You are a first-generation student encountering this syllabus. What’s confusing or unwelcoming?

You are a department chair reviewing this course for alignment. What would you suggest?

The more lenses you apply, the richer the insights become. And once you identify what needs improvement, you can ask AI to help rewrite those sections: assignment descriptions, policy phrasing, late work guidelines—whatever needs an upgrade.

This isn’t about tearing down your course. It’s about thoughtful revision—small, smart changes that lead to clearer expectations, more inclusive design, and better outcomes for everyone in the room.

🛠 Prompting as a Practice

And here’s the key: don’t stop at the first draft. Once you get an initial response, ask follow-up questions. Reiterate. Drill down.

• “What might this look like for community college students?”

• “Can you suggest assignments that include peer feedback or collaborative storytelling?”

• “What themes would you include if this were taught in a global context?”

That back-and-forth is where the real value lies. You’re not just getting content—you’re co-creating it. AI is your brainstorming partner, your creative accelerator. You bring the context. It brings the unexpected. Together, you get something new.

Once you’ve sparked some new ideas and found assignments that excite you, don’t stop there.

Take each one and build it out—ask AI to help you design comprehensive assessment guidelines for each activity. This includes criteria, expectations, and even sample rubrics.

Let’s say the AI suggests a speculative podcast where students imagine the future of AI in healthcare. You might follow up with:

“Create detailed assessment guidelines for a student podcast. Include grading criteria, feedback categories, submission format, and how it aligns with course outcomes and rubrics.”

You can even ask for variations:

• How would this look with a peer-review component?

• Can this be scaffolded over three weeks with checkpoints?

• What if students work in interdisciplinary teams?

This step helps move your creative ideas from the hypothetical into something you can actually use in class tomorrow.

Remember, don’t settle for the first idea. Drill down. Ask for alternatives. Add constraints. Make it yours.

By the end of this process, you’ll have more than just a syllabus—you’ll have a fully envisioned learning experience, infused with clarity, intention, and a whole lot more energy than last semester.

What This Really Offers Us

For me, AI has become a space for thinking—an ideation tool more than a generator. It’s a mirror, a highlighter, and sometimes a friendly nudge. Whether I’m reimagining a whole new course or fine-tuning an old favorite, it helps me think better. And that’s really what summer redesign should be about.

The real power of these tools isn't in the outputs—they’re in the questions they help us ask. What kind of learning experience do I want to create? What do my students need most? And how can I make my course not just functional, but meaningful?