When Should AI Enter the Classroom?
A recent study by Romero (2025) reveals that introducing AI too early in the learning process may erode the very creativity, discussion, and cognitive effort that universities are trying to cultivate.
Imagine a group of postgraduate students working on a complex water management problem. 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.
This is not just a coincidence. A study by Romero (2025) found that groups with immediate access to AI consistently converged on similar solutions, suggesting that early reliance on AI narrowed idea diversity and encouraged familiar lines of thinking rather than independent exploration.
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, the study highlights the importance of giving students time to think before introducing AI.
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 assumptions, negotiating different perspectives, and building an initial solution together, before AI was introduced later as a tool for refinement. A third group completed the task entirely without AI.
The results were striking. Groups with immediate access to AI consistently converged on similar solutions, suggesting that early reliance on AI narrowed idea diversity and encouraged familiar lines of thinking rather than independent exploration. By contrast, students who first grappled with the problem themselves generated a broader range of ideas before using AI to challenge, extend, and refine their thinking.
Metacognitive Laziness
But why should fifteen minutes without AI make such a difference? Fan et al. (2025) offer a compelling explanation by examining what happens inside learners’ thinking when AI becomes part of the learning process.
In a randomised experiment involving 117 university students, participants completed a two-stage English reading and writing task before revising their work with one of four forms of support: ChatGPT, a human expert, a structured writing checklist, or no additional support. Researchers then used trace data to map how students regulated their own learning during the revision process – what they monitored, what they evaluated, when they paused to orient themselves.
The ChatGPT condition centred students’ self-regulatory process on the AI. Interactions were extensive. Essays improved. But compared with the human expert and checklist conditions, the ChatGPT group showed relatively fewer of the metacognitive processes — evaluation and orientation — that involve stepping back, assessing where you are, and deciding what to do next.
Human expert support triggered transitions between orientation and evaluation that ChatGPT simply did not. And crucially, when knowledge gain and transfer were measured, not just task performance alone, the ChatGPT group’s advantage disappeared.
Creativity Researchers and the Incubation Stage
Creativity researchers have long recognised that good ideas rarely appear the moment a problem is presented. Instead, they emerge through something closer to Wallas’s (1926) classic four-stage sequence of preparation, incubation, insight, and verification.
The incubation stage, where people continue thinking without immediately reaching for an answer, is often where unexpected connections begin to form. The delayed access condition in Romero’s study can be understood as a pedagogical attempt to protect that incubation space, which might otherwise never open at all.
Designing Learning Environments
Ultimately, the deeper challenge is designing learning environments in which students remain the authors of their own thinking. Getting the timing right is one important step. Preserving learners’ agency as AI becomes a permanent feature of education may prove to be the much larger task.
However, agency cannot indefinitely depend on pedagogical structures that merely delay access to AI. As students encounter AI beyond carefully designed classroom activities, they will increasingly need to regulate their own use of these tools.
This means developing practical habits of good AI use: generating ideas before prompting, critically evaluating AI suggestions, and using AI to extend rather than replace their own thinking. In this sense, agency is not simply self-regulation, but self-regulation guided by an understanding of how to sustain learning over the long term.
