The Real Role of Faculty in an AI World

Over the course of this series, I've explored several ideas about AI and education. I've argued that AI isn't the real problem. That students naturally optimize for efficiency.

That purpose matters. That AI confidence isn't the same as accuracy. That policing AI use is unlikely to be a sustainable strategy. That we need better evidence of learning. And that small design changes can help make student thinking more visible.

Taken together, these ideas lead to a larger question: What is the role of faculty in an AI world?

For many years, higher education has often operated under an implicit assumption that instructors are primarily providers of information.

·      We explain concepts.

·      We share expertise.

·      We create content.

·      We answer questions.

And for a long time, access to information was itself a significant part of the value we provided. But AI is forcing us to confront a reality that was already emerging long before ChatGPT.

Information is abundant. Answers are abundant. Explanations are abundant. Students can access more information in seconds than previous generations could access in days. The challenge is no longer finding information. The challenge is making sense of it. The challenge is knowing what to trust. The challenge is understanding when a convincing answer is actually wrong. The challenge is recognizing what we know, what we don't know, and what questions we should be asking next.

Those are not information problems. They are thinking problems. And I believe that is where the role of faculty becomes even more important. Not because we are the sole source of knowledge. But because learning has never been just about acquiring information. Learning is about developing judgment. Learning is about making connections. Learning is about evaluating evidence. Learning is about navigating ambiguity. Learning is about reflecting on our decisions. Learning is about understanding not only what we think, but how we arrived there.

That last idea may be one of the most important.

As AI becomes increasingly capable of generating content, explanations, summaries, and recommendations, students need more than subject matter knowledge. They need the ability to examine their own thinking. They need to recognize when they understand something and when they don't. They need to identify assumptions. They need to evaluate reasoning. They need to recognize gaps in their knowledge. They need to understand how they make decisions and how tools influence those decisions.

In other words, they need metacognitive skills.

For many students, those skills do not develop automatically. They develop when faculty intentionally create opportunities for reflection, discussion, feedback, revision, and self-assessment.

They develop when students are asked not only for answers, but for explanations. Not only for conclusions, but for reasoning. Not only for products, but for process.

That's why I increasingly see the role of faculty as something larger than content delivery. Faculty are designers of learning experiences. We create environments where students can practice judgment. We create opportunities for productive struggle. We help students connect ideas across contexts. We challenge assumptions. We ask questions that push thinking deeper. And perhaps most importantly, we help students become more aware of how they learn.

That role matters because AI can generate content. But it cannot determine what is worth learning. It cannot fully understand the context of a particular classroom. It cannot build meaningful educational relationships. And it cannot replace the human work of helping students develop self-awareness, judgment, and intellectual independence.

The future of education is not about competing with AI. Nor is it about preventing students from using it. It's about helping students learn how to think effectively in a world where AI is increasingly present.

That challenge belongs to all of us. And I believe it will require more than new policies, new technologies, or new tools. It will require intentional learning design. It will require helping students make their thinking visible. And it will require helping students better understand their own thinking in the first place.

This concludes the first series of posts, which focused on understanding some of the challenges AI is creating for teaching and learning.

In the next series, I'll shift from theory to practice.

We'll explore specific instructional design strategies that can help faculty create learning experiences where thinking remains visible, reflection becomes part of the process, and students develop the skills needed to learn effectively alongside AI.

We'll look at assignment design, reflection activities, discussion strategies, transparency practices, verification skills, and other practical approaches that can be implemented without redesigning an entire course.

Because understanding the challenge is only the first step. The next step is designing for it.