R.I. [Brown University] — In just a few short years, generative artificial intelligence tools have made a leap from novelties that produce stilted text to genuine societal disruptors that may alter the way people learn, work and even think
At Brown University, academic leaders and faculty recognized immediately that generative AI would have a profound impact on teaching and learning. Faculty began offering classes that explore what AI means for their disciplines and investigating ways of incorporating it in curricula. Brown’s Sheridan Center for Teaching and Learning began offering seminars on course design and learning assessment in the age of AI, among other resources. The Brown University Library launched multiple programs, including a series of AI workshops and a learning community that meets regularly to discuss emerging issues.
At the same time, national discussions about AI use among students continue to focus on AI literacy on one hand, and abuses of AI on the other. Issues dominating conversations inside and outside academia — among parents, employers, policymakers and others — focus on threats of reduced cognitive reasoning and problem solving skills; learning loss, with a particular focus on loss of writing skills; reduction in quality human engagement; and a rise in academic dishonesty. Educators across the nation and around the world, including at Brown, have wrestled with how to promote AI innovation while addressing accusations of cheating by students using AI, as well as questions of fairness and equity when it comes to grading and assessing student work in an AI world.
At Brown, the priority is to sustain academic excellence while maximizing the benefits and mitigating the risks of AI Faculty and administrative leaders recognize that to fully tackle the full range of AI challenges and opportunities, the entire academic community will need to work collectively
“There are reasons to be excited about the future of AI, but we also must grapple with new questions surrounding ethics, authorship, intellectual property and a host of other areas that overlap with teaching and learning,” Doyle said. “Our work has been focused on leveraging our entire academic community and all the expertise found within it to chart a path forward. There are exciting things ahead of us here, but we need to proceed thoughtfully and prudently.”
Early in 2025, Doyle appointed the Generative AI in Teaching and Learning (GAITL) Committee. With a focus on supporting innovative and equitable teaching and learning, the committee was charged with understanding how AI use is evolving at Brown and elsewhere and making recommendations on how the University should proceed. Michael Littman, a computer science professor who became Brown’s first associate provost for AI in July 2025, co-chaired the committee, which released a report in July 2026 detailing its findings and recommendations.
