Navigating Course Map Design

Michael Ciocco, PhD, Associate Vice President of Online Learning, Rowan University

Most travel begins with a destination. You open your favorite map app, choose whether you want the fastest route or the most fuel-efficient one, and start driving. Along the way, you might hit construction, miss a turn, or disagree with the app’s suggestion entirely. Still, the technology keeps you pointed toward where you need to go. The trip becomes a blend of human judgment and assisted navigation.

Designing a course map is remarkably similar. Subject matter experts (SMEs) set the destination, define learning objectives, incorporate expert knowledge and decision-making on content and chart the path students navigate to get there. But unlike choosing between highways or back roads, building a course map requires research, aligning materials, and crafting assessments that spark engagement and knowledge transfer rather than check boxes. It’s work that demands intention, creativity, and time, resulting in a clear map to guide the student.

At Rowan University, our Online Learning team in the Rowan Online Division has taken on a major directive: align all online program curricula with clearly defined learning outcomes through course maps. Like many institutions, our assessment practices have evolved over time, and we’re now shifting to a model built around alignment to best practices, data, and continuous improvement. That shift has revealed something pivotal to our success: SMEs often need more support to design courses that meet these goals.

Many of our faculty are already skilled in backward design and are fluent in mapping a course with clear objectives and strong assessments. Others, especially practitioners who teach part-time, are passionate educators but may be less experienced learning designers. I wanted to find a way to support our SMEs, regardless of background, in building high-quality, well-aligned course maps.

AI seemed like a promising direction. But before recommending it broadly, I wanted evidence that it could genuinely help. So, I designed an experiment: Could an AI model recreate the course map I built for my own online class, without seeing my original materials, and produce something comparable? And more importantly, would the process save time and effort?

“Building a course map requires research, aligning materials, and crafting assessments that spark engagement and knowledge transfer rather than check boxes.”

I began by asking the AI to act as an expert in online learning design. I provided only the official course description. From there, the model helped generate a set of learning objectives that aligned with established best practices. With the “destination” set, I moved on to planning the route. Through a guided series of prompts, the AI proposed modules, topics, and module-level learning objectives. We then collaborated—yes, collaborated—to generate activities and assignments rooted in those goals.

I didn’t simply accept its suggestions. I redirected the AI several times, especially when I wanted more authentic assessments or problem-based activities. I also described my student population so the model could consider real learner needs. Throughout the process, I remained the decision-maker, steering the design whenever the AI drifted off course.

In the end, the AI-assisted course map looked strikingly similar to the one I had originally developed. So similar that it validated much of my instructional design work. This outcome wasn’t surprising once I considered how AI models operate. They identify patterns across thousands of data sources, surfacing ideas that reflect the collective wisdom of existing subject matter and instructional design literature. When abundant, high-quality information exists, the model becomes a kind of curated chorus of expert recommendations.

Of course, AI isn’t perfect. For niche or emerging subjects with limited published material, its suggestions become less reliable. But in many established domains, the support it provides can meaningfully enhance and streamline the design process.

When I presented my experiment to the Rowan faculty, the reaction was immediate and enthusiastic. Many recognized how AI could reduce the burden of course planning and help them focus on what they do best: teaching. Their response has encouraged Rowan to pursue the development of an AI mentor for SMEs, designed to help instructors rapidly generate aligned, backward-designed course maps.

I’m optimistic about where this will lead. AI won’t replace the expertise, creativity, or lived experience of educators. But it can serve as a powerful navigational tool that supports both new and seasoned instructors as they design courses that are unique, rigorous, engaging, and aligned to student success.

Like the map app that reroutes you after a wrong turn, AI can help guide instructors through the twists and turns of course design. The human remains forever in the driver’s seat and guided along the way, but the journey becomes smoother with the right type of collaborative assistance to arrive at the desired destination.

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