AI Didn’t Kill Student Learning—It Transformed My Classroom

When I first heard discussions about AI disrupting education—”AI will kill higher ed! Are we witnessing the death of the writer?! AI will destroy critical thinking!”—I saw an opportunity rather than a crisis.

Instead of fearing what AI might take away, I wondered how it could enhance student learning and career readiness. So, I took a leap and redesigned the honors section of my introductory bioinformatics course, treating AI not as a threat but as a new frontier in teaching.

Starting from Scratch: A Bold Experiment

Rather than retrofitting what I had, I opted for a complete overhaul. I wanted students to engage with AI in a way that mirrored real scientific research. My question was: What if we could use AI to simulate a research experience from scratch? Many undergraduate students don’t get into labs until later in their studies, and even then, the transition from coursework to hands-on research can be abrupt. AI, I believed, could bridge this gap, allowing students to explore research in a structured yet open-ended way.

To get started, I partnered with my colleague Kristin Davin from the Cato College of Education who is trained in educational design. Together, we approached the course like an experiment. We followed a backward design process in which we used course learning outcomes to develop performance-based assessments and design weekly modules that would push students to think
critically while engaging with AI.

How It Works: AI as a Research Partner

The course begins with students forming research teams and brainstorming project ideas. Initially, I worried that students might struggle to generate their own topics, so I prepared a list of potential research ideas. That list? Completely ignored. Instead, students excitedly developed their own questions—ranging from AI’s potential in enhancing biotechnology, to disease surveillance, to applications in environmental and human health. AI allowed students to explore problems that genuinely interested them, something difficult to achieve in a large, traditional classroom setting.

In this new space, students engaged each other in critical discussions of existing research, experimental design, data collection, analysis, and result interpretation. Students learned not only how to use AI tools but also how to critique them, recognizing when AI-generated information was inaccurate or incomplete, reinforcing the importance of verifying sources and critically evaluating AI outputs.

Unexpected Outcomes: Engagement Like Never Before

Before integrating AI, developing strategies to stimulate discussions required a high level of preparation, guidance, and standard incentives like participation grades. Now? I often find students already deep in discussion before I even enter the classroom. They want to talk about their research.

The shift from passive learning to active exploration has been dramatic.

At the end of the semester, honors students present their projects to their peers in the course to simulate a research symposium. What struck me most was not just the quality of their work and the confidence they had gained, but the engagement of the audience. Students who weren’t in the honors section were actively asking questions, building on ideas, and making connections to their own interests.

Beyond the Course: AI Literacy for the Future

One of the most significant takeaways has been students’ evolving relationship with AI. Many arrived skeptical or even dismissive—often having been discouraged from using AI in previous classes. But by the end of the semester they saw AI as a tool rather than a shortcut, understanding both its potential and its limitations. They developed a critical literacy that will serve them well in any field, whether they pursue scientific research or not.

Lessons Learned: Take the Risk

Was it scary to redesign a course without a roadmap? Absolutely. There was no literature or precedent to rely on—just a belief in the idea. But, as scientists, we experiment, we iterate, and we learn from both success and failure.

For educators considering AI integration, my advice is simple: Don’t be afraid to try something new. The potential is enormous, and the impact on student learning can be profound.

AI isn’t the end of teaching—it’s the beginning of a new kind of education, one that fosters deeper inquiry, engagement, and real-world readiness.

ChatGPT Helped Shape This Story—Here’s How We Used It

We used ChatGPT to help develop this story.

1. First, we recorded a conversation about the course using Zoom.
2. We then provided ChatGPT with a description of the key story components and asked it to generate a narrative based on our discussion.
3. After uploading a 12-page transcript of our conversation, we reviewed and refined the story generated by ChatGPT. The essay may not
4. be dead, but it is evolving—and maybe that’s exactly what education needs.