Brown University will publish formal guidelines on AI use in coursework following a year-long faculty review, with the university's provost calling for a deliberate approach to a technology that is already reshaping how students learn and write.
The Generative AI in Teaching and Learning (GAITL) Committee, appointed by Provost Francis J. Doyle in early 2025, issued its report in July 2026 with recommendations that include clearer expectations in course syllabi, updates to academic codes, and collaboration with peer institutions on standardized usage rules. The university also launched AI workshops and a learning community through the Brown University Library, and faculty across departments are now teaching courses that examine AI's implications within their fields.
"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.
Students and faculty share concerns about cognitive skills
The GAITL report found that many students are using generative AI tools in their studies while simultaneously worrying about the long-term effects on their thinking abilities. Faculty members reported similar anxieties, and the committee highlighted a need for clearer expectations about what AI use is acceptable in each course.
Those concerns mirror national debates about AI's effect on fundamental academic skills, including reduced reasoning and problem-solving ability, learning loss, and a decline in writing skills. The report also flagged a reduction in quality human engagement and rising academic dishonesty as areas of concern.
Michael Littman, Brown's first associate provost for AI and co-chair of the committee, said the group deliberately focused on Brown's specific context rather than trying to solve AI policy at a global scale. "There was a realization that we're not trying to solve this problem at a global scale. We're trying to figure out what this means for Brown and how we can shape that future," Littman said.
Open Curriculum complicates AI policy
Brown's Open Curriculum, which gives students broad freedom in course selection, makes it difficult to establish consistent checkpoints for AI exposure across the student body. The committee's recommendations emphasize that instructors must clearly articulate their AI expectations upfront and integrate the tools thoughtfully into their courses.
The university has responded with practical resources. An expanded GAITL Phase 2 committee released sample generative AI syllabus statements in August 2026 to help faculty set expectations. The Sheridan Center for Teaching and Learning has also offered seminars on adapting course design and assessment strategies for an era of widely available AI tools.
A new course series, "Transcending Boundaries: AI + Science," builds on these efforts by integrating AI exploration across disciplines. Doyle has tasked the GAITL committee with continuing to engage the campus community on the report's broader recommendations.
Doyle pointed to a historical precedent for Brown's approach, citing a Brown Alumni Magazine article from 43 years ago about integrating computers into education. "If you accept that fact and realize that it has the potential for major social change - both positive and negative - and if you realize that many schools with different goals may be shaping the technology, you come to the conclusion that we have the opportunity to shape the new technology, too, and to do it in a way that is really appropriate to Brown," Doyle said, quoting the decades-old publication.
Why this matters for educators
For teachers and administrators, Brown's approach offers a working model of how to address AI without either banning it or letting it reshape academic standards unchecked. The key takeaway is procedural: establish clear syllabus-level expectations now, update academic integrity codes, and coordinate with peer institutions so students don't face wildly different rules from one course to the next. Educators looking for practical strategies on setting those expectations can find guidance in resources like the AI Learning Path for Teachers, which covers classroom-specific applications of generative AI. For a broader look at how AI is changing teaching and learning across institutions, the AI for Education collection tracks developments as they emerge.
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