AI in the Classroom: When to Encourage It, When to Restrict It

How should we decide how much AI and what kind of AI to encourage in university classrooms? A useful starting point might be distinguishing between fundamental and higher-order skills, and between learning and performing.

At work, I’m seeing the pace of AI advancement exhaust some of the best lecturers I know. They want to do right by their students, so they keep learning, keep adjusting, keep rewriting syllabi and assessments to match a moving target. At home, my seven-year-old is learning math and reading the way I did decades ago: no calculators, no computers, no AI. Just pen and paper.

Are these two approaches in conflict or are they responding to different goals? One could argue that universities are meant to teach higher-order skills that prepare students for the labor market, while primary schools focus on foundational skills that make later learning possible. In practice, though, universities teach both.

They teach foundational competencies such as calculus, programming, basic statistics, writing, and communication alongside higher-order capacities like critical thinking, research design, leadership, and policy analysis. These different types of skills may call for different uses of AI in the classroom. To see why, it helps to distinguish between learning and performing. 

Performance vs. learning

Technology’s core value often comes from embodying human knowledge and ability. It does things we used to do ourselves, saving time and freeing us up to do other things. In other words, technology drives performance and productivity: typewriters embody writing, calculators embody arithmetic, computers embody a wide range of human capabilities, and AI, an even wider one. When AI does a task for us, it can boost performance, but not necessarily learning.

Learning, by contrast, is slow and effortful. It demands concentration, self-control, and patience. Importantly, once we internalize knowledge, we can apply it across domains - a powerful foundation for problem-solving and creativity.

Foundational skills

This is where the distinction between foundational and higher-order skills becomes useful. If we don’t restrict AI when teaching foundational skills, we risk producing graduates who can complete tasks with AI support, but struggle to apply concepts creatively when the context changes. Foundational skills may therefore be best taught in environments that limit AI use and help students internalize knowledge and know-how. Well-designed AI can certainly serve as a tutor, but the goal must remain learning and not performance alone. If the foundational skill in question is calculus, for example, teachers can use AI tutors to support students as they learn the mechanics of integration. But the practice of solving integrals must still be done by the students.

Higher-order skills

But if foundational skills are about building internal understanding, higher-order skills are often about navigating complexity and here collaboration with tools has always been part of the job. Historically, technology has automated lower-order tasks while complementing higher-order ones. When computers automated complex statistical models, for instance, they allowed researchers to focus on model assessment and interpretation rather than on solving numerical optimization problems. This shift made it easier to teach research design and empirical analysis.

To the extent that AI complements higher-order skills, educators may need to encourage broader use of it, treating AI as an acknowledged teammate and exploring what students can achieve through augmented collaboration. In a course on economic development policy, for example, AI can help students understand economic models faster by offering explanations tailored to how they learn. 

Still, even in higher-order courses, not every part of the learning process should be outsourced. The headstart that AI gives us in learning economic models, allows for richer discussion on which theories apply in real situations, and what policy options follow. This “application” phase should remain student-led. Here, students are practicing how to transfer what they have learned into new domains. That ability to adapt and apply knowledge is itself a critical higher-order skill.

A takeaway

A practical takeaway is that educators can make their objectives explicit at the onset, both the syllabus and the course’s AI policy: what parts of the course are meant to build foundational mastery, and what parts are meant to develop higher-order thinking. In foundational parts, AI may be best positioned as a tutor, supporting practice without replacing it. In higher-order parts, AI can be treated more openly as a teammate, as long as students are still required to demonstrate independent reasoning and transfer. The goal is not a blanket rule about AI, but a coherent design: aligning how we use AI with what we are actually trying to teach.