Symposium Features Student Research on AI

On May 4, Rackham Graduate School hosted a capstone symposium to feature the research of the Rackham Student AI Working Groups Program. Organized during the winter 2026 term, the program was designed to allow students to explore and contribute to the dynamic and quickly evolving use of AI in graduate education through collaborative, interdisciplinary learning.

More than 100 graduate students and postdocs participated in a total of 12 working groups. Their topics of inquiry ranged from AI’s role in teaching and research to its impact on the workforce; its ramifications for governments, public safety, and mental health; connections between AI and creative practice; and the sustainability implications of data centers, among others.

“The goals of this program were ambitious and aimed to address several intersecting themes,” Rackham Dean Mike Solomon said. “We asked students to explore how AI might create new research methods and new objects of inquiry, to identify both potential benefits and risks, and to advance recommendations. Most importantly, we asked them to do so in working groups that spanned academic disciplines and programs—because no single field has all of the answers.”

“In the last few years, we’ve seen efforts, task forces, and committees formed across campus trying to figure out what we are doing with AI, how AI will affect research and teaching, and how it will affect the campus at large,” said Rackham Associate Dean Dave Sept. “We at Rackham felt student voices need to be an important part of that conversation, and that’s why we pursued this idea of putting together groups of students from across Rackham’s programs.”

In addition to morning lightning talks where students shared key findings from their projects, the symposium included a faculty panel discussion with Janice M. Jenkins Collegiate Professor of Computer Science and Engineering Rada Mihalcea and Associate Professor of English Tung-Hui Hu, an afternoon poster session, and a keynote address by Latanya Sweeney, a leading voice in AI technology and policy, and the Daniel Paul Professor of the Practice of Government and Technology at the Harvard Kennedy School.

At the poster session, student participants were able to engage with each other’s work and answer questions from attendees from across campus and the broader community. A common theme was the benefit of pursuing this work with people from different fields.

“I’m a humanities major, and for me looking at these things through the eyes of institutional studies or personal use for AI looks very different from looking at AI through organized, qualitative data gathering,” said Erin Leary, a Ph.D. candidate in American culture. “It was really helpful to have people from policy, people from qualitative fields, as well as people from the humanities. They have perspectives that I don’t have, and I have perspectives that they don’t have, and a variety of methods of data collection is really essential. I don’t think that you can make decisions without having interdisciplinary involvement.”

Sam Butcher, a master’s student in design science and engineering, felt that participating in the program allowed him to reconsider the positive possibilities of AI in a landscape where the technology has driven anxiety about its potential impact.

“I think there are so many good opportunities for AI, and a lot of good that can come from it,” he said. “When you look around the room, there’s a topic for AI in all aspects and all fields. That surprised me. There’s such a diverse amount of projects all around, and it can be used for so much.”

“I value the agency that students brought to this work,” Dean Solomon said. “Their level of engagement tells me that graduate students are not waiting on the sidelines. They are invested in asking the questions, building the frameworks, and establishing the practices that they will need to successfully engage with AI in the future.

“I believe it likely that every faculty-graduate mentoring pair, every seminar course, every lab, should be having broad conversations about the role of AI in their field. But I think in general those communities need some help to get going, especially the conversations between students and faculty. By disseminating the work of this symposium, I hope we will catalyze that important next step.”

The complete list of student projects follows, and videos of their lightning talks can be found on the Rackham Student AI Working Groups webpage:

• Responsible AI in Education and Teaching
• Labor and Society Transformations in the Age of AI (on Campus)
• Bias, Fairness, and Ethics in AI Systems
• Labor and Society Transformations in the Age of AI
• Sustainability Implications of AI-related Data Center Expansion
• Multi-Level Institutional Governance, Oversight, Policy, and Evaluation
• Responsible AI in Education and Research: GenAI in Graduate Research at U-M: Practices, Trust, and Impact
• Toward Responsible and Equitable AI in Higher Ed: An Environmental Scan of AI Policy Across Health-Related Units at U-M
• AI and Machine Learning Applications in Oral Microbiome Analysis for Pregnancy Outcomes and Oral-Systemic Health
• Artistic and Co-Creative Applications and Challenges
• AI-Expanded Communication Boundaries
• Bias, Fairness and Ethics in AI Systems (AI Exposure, Usage, and Perceptions Among U-M Grad Students)