AI adds the most value when it comes after a first attempt at thinking, not before it.

Groups with immediate AI access consistently converged on strikingly similar solutions.

The deeper challenge is not only timing AI correctly, but keeping students the authors of their own thinking.

Co-authored by Paul Surlis, Ava Gilmartin, and Michael Hogan.

Imagine a group of postgraduate students who have just been handed a complex water management problem. Their challenge is to develop a sustainable water management plan for a busy tourist city, balancing environmental protection, economic growth, and increasing demand for scarce resources.

Working in small teams, they have ninety minutes to understand the problem, debate possible solutions, and agree on a plan. Within minutes, several groups begin consulting ChatGPT. By the end of the session, each group submits a polished proposal. Although the groups never interacted with one another, many of their solutions are strikingly similar: in their structure, in the assumptions they make, and in the possibilities they never explore. Nothing has been copied. Yet something important has been lost.

This is the scene that lies behind a recent study by Romero (2025). As generative AI becomes an increasingly familiar part of higher education, the most important question may no longer be whether students should use AI at all. A more useful question is when AI should enter the learning process. Could introducing AI too early erode the very creativity, discussion, and cognitive effort that universities are trying to cultivate?

Testing the Impact of AI Introduction Timing

To test whether timing really mattered, Romero (2025) designed a simple but revealing experiment. Thirty-six graduate students were randomly assigned to one of three conditions while working in small groups on the water management challenge. One group had access to ChatGPT from the very beginning. A second group spent the first fifteen minutes working without AI, debating ideas, questioning........

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