3 Questions on Utilizing AI for Online Course Redesign

Much of the conversation around AI in higher ed focuses on students using consumer-facing tools like ChatGPT. The University of Central Florida, one of the largest universities in the country and home to one of the biggest online learning operations, is experimenting with AI behind the scenes to review and improve its own courses.

Working with digital learning firm iDesign, UCF is using AI to evaluate and update 17 courses in its online R.N.-to-B.S.N. program, scanning for alignment, accessibility and design consistency as the program moves to a new eight-week format for working nurses.

To learn more about the current and future role of AI in online course design and redesign, I reached out to my friends Tom Cavanagh, vice provost for digital learning at UCF, and Whitney Kilgore, iDesign’s co-founder and chief academic officer, to learn more.

Q: Tell us about how this project and collaboration came to be and the role AI is playing in the work.

A: This project grew out of a genuine programmatic need: The R.N.-to-B.S.N. program is moving to a compressed eight-week format to better serve working nurses, people who are balancing demanding clinical schedules with their education. Redesigning 17 courses simultaneously, with that learner population in mind, requires both speed and precision. That’s exactly where iDesign’s AI-powered Build platform becomes a real asset.

What Build does particularly well is the work that benefits most from consistency and rigor at scale: ensuring tight alignment between standards and assessments and generating draft content that gives faculty and learning designers a strong starting point. For a nursing program, where accreditation standards and competency frameworks are nonnegotiable, having AI surface alignment gaps and produce aligned draft materials early in the process means we’re not starting from a blank page and we’re not leaving alignment to chance or memory.

But the platform is designed around a clear philosophy: AI does what AI does best and people do what people do best. Our learning designers and faculty partners aren’t reviewing AI outputs passively. They’re synthesizing, questioning and editing, bringing the kind of judgment that only comes from understanding who these learners actually are. Working nurses in an R.N.-to-B.S.N. program don’t need content written for a traditional undergraduate student. They bring clinical experience, professional identity and real-time constraints into their learning. Ensuring that the assessments and content reflect that they’re rigorous but relevant, challenging but respectful of what learners already know, is a deeply human task, and that’s where our team is focused.

The result is a workflow where AI accelerates the structural and generative work and human expertise shapes it into something that genuinely serves the learners it was designed for.

Q: What will AI mean for the future of how learning designers collaborate with faculty on online course development?

A: It will fundamentally change the texture of those conversations for the better. Right now, a significant portion of early collaboration time is spent gathering information: What are your learning objectives? How are assessments aligned? Where are the gaps? AI can do a lot of that diagnostic work up front, so by the time a learning designer sits down with a faculty member, they’re not starting from zero. They’re coming in with evidence, with a shared frame of reference and with specific questions worth discussing.

That actually creates an opportunity for deeper partnership. Faculty are subject matter experts; learning designers are experts in pedagogy and online learning environments. When AI handles more of the procedural groundwork, both sides of that partnership can spend more time doing what they’re genuinely good at. The collaboration becomes less about information gathering and more about intellectual co-creation, which is where the most meaningful course design work happens anyway.

AI will also help learning designers be more proactive advisers. Instead of reacting to what faculty bring to the table, they’ll be able to walk into a conversation having already identified potential misalignments or accessibility concerns, as well as suggestions for activities and assessment strategies based on the course objectives and known faculty preferences. That shifts the dynamic in a healthy way from service provider to strategic thought partner. It also helps to accelerate scale, allowing learning designers to be more efficient.

Q: One of the concerns in our community is that AI will end up replacing work that is now done by learning designers, media educators and educational technologists on online courses and programs. How do each of you respond to that worry, and what should we nonfaculty educators in the online learning space be doing to prepare for the coming AI tsunami?

A: We take that concern seriously and don’t think it deserves a dismissive answer. Any honest conversation about AI in our field has to acknowledge that some tasks that currently require human time and expertise will be automated. That’s real. The question is what we do with that reality.

Our view is that AI will not replace the judgment, relational intelligence and contextual expertise that great learning designers and educational technologists bring to their work. What it will do is eliminate the tolerance for teams that aren’t using it. If your value proposition is completing a checklist of procedural tasks, auditing alignment by hand, formatting course templates and producing a first draft of learning objectives, those tasks are going to be done faster and cheaper by AI-assisted workflows. That’s the pressure point.

What AI cannot do is build trust with a nervous faculty member who has never taught online before. It can’t navigate the organizational politics of curriculum change. It can’t bring together a deep understanding of a discipline, a learning population and a pedagogical framework and make a judgment call about how they should intersect and be applied in a world of humans. Those are deeply human competencies.

Some practical advice is this: Invest in your professional identity as a learning strategist, not just a production specialist. Get fluent with AI tools, not to defend against them, but to wield them. Develop your consultative skills. Strengthen your ability to connect instructional choices to institutional outcomes that matter to administrators and faculty. The professionals who will thrive are those who can say, with confidence, “I use AI to work faster, and here’s the thinking and expertise that AI can’t replicate.”

The tsunami metaphor is apt in one way: You don’t survive a wave by standing still. But skilled people who learn to move with it will go further than they ever could before.