This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: 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 into 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; reduced quality of 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, according to Provost Francis J. Faculty and administrative leaders recognize that to fully tackle the range of AI challenges and opportunities, the entire academic community will need to work collectively. Among its key findings, the report revealed that while most students report using generative AI in the course of their studies, many of those same students worry that it is negatively affecting their long-term cognition.
Faculty members shared these concerns, and while many faculty have begun using AI in some capacity in teaching or research, many course syllabi do not adequately establish clear expectations or limitations on AI use. The GAITL committee's immediate recommendations included publishing guidelines that help clarify expectations around AI. Intermediate- and longer-term recommendations include updating academic codes to address generative AI and eventually partnering with peer institutions to set standards around its use in teaching and learning.
In August 2026, an expanded committee, called GAITL Phase 2, shared sample generative AI syllabus statements to serve as a resource for faculty in their courses, and Doyle has charged the group with engaging with the campus community about the report's longer-term recommendations. In an interview, Doyle and Littman discussed the report, its development process and next steps in Brown's effort to incorporate AI in teaching and learning in a way that harnesses its positive potential and mitigates risks to academic integrity and the ability of faculty to assess learning and understanding. Discover the latest in science, tech, and space with over 100,000 subscribers who rely on Phys.org for daily insights. d research that matter—daily or weekly.
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