Brown University's associate provost had a message for the campus 43 years ago: schools have the power to shape new technology, not just watch it happen. The article in the Brown Alumni Magazine was about computers in education. Today, Francis J. Doyle III, the university's provost, reads that old quote and sees a mirror of the moment right now. "We cannot sit and wait for this to happen to us," he says. "I think we are in a moment where Brown has a chance to seize this opportunity."
The opportunity Doyle is talking about is generative artificial intelligence — the tools that write essays, answer questions, and draft code in seconds. In just a few years, these have gone from odd novelties to genuine disruptors that may change how people learn, work, and even think. That worried educators nationwide, and for good reason. The debates rage about AI killing problem-solving skills, draining writing ability, shrinking human connection, and fueling cheating on a scale no one has seen before.
Brown chose to meet the moment head-on rather than hide from it. The Sheridan Center for Teaching and Learning began offering seminars on designing courses and assessing learning in the age of AI. The Brown University Library launched a series of AI workshops plus a learning community that meets regularly to talk through emerging issues. Faculty started teaching classes on what AI means for their own fields.
But the clearest signal came from a committee called GAITL — the group studying Generative AI in Teaching and Learning. Its report found something striking: most Brown students say they use generative AI in their studies, yet many of those same students worry it is quietly harming their long-term thinking. Faculty shared the concern, and while many professors use AI in teaching or research, most course syllabi still did not spell out clear rules about when students may use it and when they must not.
The committee's first fix was simple and powerful: publish guidelines that make expectations explicit. The longer plan involves updating the university's academic codes and eventually partnering with peer institutions to set shared standards. In August 2026, an expanded group called GAITL Phase 2 shared sample AI syllabus statements that professors can drop straight into their own courses.
Doyle insists this proactive streak is part of Brown's DNA, the same instinct that pushed the school toward computers four decades ago. The university is betting that by setting its own rules rather than waiting for someone else's, it can keep academic excellence strong while maximising AI's benefits and containing its risks. It is a far cry from simply banning the tools — and, Brown hopes, a model for how the whole academic world can learn to live with them.