In classrooms and faculty meetings across the world, a quiet confusion is growing louder. Universities that proudly unveil new AI-integrated programs simultaneously penalize students for using AI in their coursework. The contradiction is striking and symptomatic of a deeper issue: we have not agreed on what we are assessing in higher education.

This is not just a technological problem. It’s a pedagogical and philosophical one.

Are we evaluating the student’s intellect—the raw cognitive power of reasoning, recall, creativity? If so, does that mean we must isolate students from any external tools? For decades, we have assumed that an unaided exam or essay can reveal a student’s “true mind.” But the realities of the world, and the nature of intellect itself, are far more complex.

Or are we assessing the student’s work—a product that reflects both their intellect and their skillful use of tools? If this is our goal, then the ethical and strategic use of generative AI becomes part of what we should be evaluating. In nearly every professional domain today, success depends on how well individuals harness tools to enhance their thinking, not on how independently they can perform without them.

Alternatively, are we trying to preserve a model of tool-free work as the benchmark of “authentic” learning? If so, we must ask whether that model still reflects the real world our students are entering. There is certainly value in knowing what a student can do unaided. But if this becomes the default expectation across all disciplines, we risk preparing students for a world that no longer exists.

Until we settle this question, what are we really evaluating? The debate around AI in education will continue to generate anxiety, inconsistency, and confusion. Some instructors will ban ChatGPT outright, others will encourage its use. Some students will be praised for innovation; others punished for the same behavior under different policies.

At the heart of this confusion is a crisis of educational purpose. Are we assessing performance or process? Mastery or adaptability? Knowledge or wisdom?

In the age of AI, education must evolve from simply measuring what students know to understanding how they think, choose, and collaborate with or without tools. We must stop asking whether students used AI and start asking how they used it, why they used it, and what they learned in the process.

Until then, we are not assessing students, we are assessing our own institutional uncertainty.