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3 College of the Environment teams funded to pursue AI projects that support teaching and learning

Дата публикации: 03-08-2026 17:33:10

The University of Washington recently awarded three UW College of the Environment teams funding to support faculty‑led, exploratory projects that thoughtfully apply artificial intelligence to enhance teaching and learning. In total, 36 teams representing 19 schools and colleges across the UW received grant funding in its inaugural year.
The Supporting Educational Excellence and Discovery with AI Grants program, also called SEED-AI grants, recognizes that AI can strengthen the UW’s educational mission, if it is applied thoughtfully and in the right contexts. 

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An aerial view of the south end of the UW campus, with downtown Seattle behind.The University of Washington recently awarded three UW College of the Environment teams funding to support faculty‑led, exploratory projects that thoughtfully apply artificial intelligence to enhance teaching and learning. In total, 36 teams representing 19 schools and colleges across the UW received grant funding in its inaugural year.

The Supporting Educational Excellence and Discovery with AI Grants program, also called SEED-AI grants, recognizes that AI can strengthen the UW’s educational mission, if it is applied thoughtfully and in the right contexts. The projects selected for this first round of funding have the potential for broad impact, serve large numbers of students and/or advance student success.

Each project will receive up to $50,000. The SEED-AI grants program is supported by the Charles and Lisa Simonyi Launch Fund for Artificial Intelligence.

Here are the UW Environment teams and projects that are funded in this cycle:

Supporting interdisciplinary Arctic learning with AI chatbot tool Project team

Michelle Koutnik, research associate professor, Department of Earth and Space Sciences

Project summary

Since change is fundamental not only to the Arctic climate and environment but also to societies that live in and interact with the Arctic, especially Indigenous communities and Arctic residents, Arctic Studies courses require integrated understanding on different topics and from different perspectives. This project will design, develop and test an AI chatbot tool to support interdisciplinary learning about the Arctic. The tool will be trained on resources the students are reading to help them reason through interdisciplinary questions and challenge their assumptions. The tool will deliver credible sources on Arctic Indigenous perspectives and priorities while emphasizing data sovereignty and Indigenous rights. 

A goal for this chatbot tool is to supplement students’ research and learning, and support how they generate ideas and advance in-class discussion to inform their own critical thinking and project deliverables. The tool will be tested and reviewed with students and consulting scholars.

Scaling AI computer vision education in marine sciences: From pilot to open-access curriculum Project team

Sasha Seroy, assistant teaching professor, School of Oceanography
Katharine Bigham, postdoctoral researcher, School of Oceanography
Ada Carter, undergraduate student, School of Oceanography

Project summary

Computer vision AI can revolutionize ocean science research by enabling underwater imagery analysis at unprecedented scales, yet most marine scientists lack training to leverage these tools. This team has successfully piloted a course teaching undergraduates to apply computer vision to research problems, delivering it three times across two UW campuses since winter 2025. However, a critical barrier remains: While the curriculum contains sufficient technical depth, it lacks comprehensive documentation needed for independent faculty adoption.

This project will transform the team’s pilot into a broadly accessible educational resource by producing: (1) a fully documented instructor handbook with lesson plans and teaching notes; (2) a standardized quarter-long course package; (3) a condensed week-long intensive format; and (4) adaptation guides for other environmental disciplines. All materials will be openly licensed via oceancv.org, enabling scaling across UW and beyond while addressing workforce demand for environmental data scientists with integrated AI capabilities.

Designing responsible AI for learning: A pedagogical framework for undergraduate education Project team

Gaj Sivandran, assistant teaching professor, School of Environmental and Forest Sciences and Center for Quantitative Sciences
Kat Huybers, assistant teaching professor, Department of Atmospheric and Climate Science
Kerry Naish, professor and director, Marine Biology
Mikelle Nuwer, teaching professor and associate director for undergraduate programs, School of Oceanography
Kristi Straus, teaching professor and associate director, Program on the Environment
Jane Dolliver, professional staff, College of the Environment
Akshay Mehra, assistant professor, Department of Earth and Space Sciences

Project summary

Generative AI has the potential to enhance undergraduate learning, but only when its use is treated as a pedagogical design challenge rather than a policy or enforcement issue. This project advances the University of Washington’s educational mission by piloting learning-centered approaches to AI integration within the College of the Environment. By pairing a student-facing course with faculty-led pedagogical development, the project explores how AI can support learning, discovery and ethical reasoning without displacing core intellectual work. Faculty learning communities will develop and share concise teaching cases that model responsible, learning-aligned AI use across environmental contexts.

Additionally, a new 200-level undergraduate course first offered in spring 2026 will teach students to engage with AI as a learning tool through active, reflective practice. Together, these efforts will generate transferable resources, clarify expectations for appropriate AI use and provide scalable models for responsible AI-informed pedagogy across disciplines at UW.

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Классификация: . Схожих патентов: 0. Схожих новостей: 10. Тональность: 0. Информативность: 7.07. Источник: environment.uw.edu.