Empowering Arctic Science with Artificial Intelligence: The Cyber2A Project

Project Background

The Arctic is experiencing climate-related change faster than any other region on Earth, with receding ice, increased permafrost thaw, and eroding coastlines. While seeking to understand and address these problems, scientists working in this remote region often face the dual challenge of vast, complex data and limited training in the computational tools needed to turn that data into insight. That’s where the Cyber2A project comes in: by building curricula, hosting training workshops and webinars, and fostering a community of educators, Cyber2A is equipping geoscientists with the skills needed to harness artificial intelligence (AI), in turn enabling more efficient and effective Arctic research. Cyber2A represents a new approach rooted in the same principles that guide NCEAS: open science, shared resources, and training communities rather than individuals. The story below follows how this approach is unfolding across two workshops and a growing network of Arctic scientists and educators. The project is a collaboration between Arizona State University, Woodwell Climate Research Center, the Arctic Data Center at NCEAS, and the National Center for Supercomputing Applications, and is supported by National Science Foundation Awards #2230034 and #2042102.

From Mission to Momentum

At its core, Cyber2A seeks to bridge three key elements: domain expertise (Arctic geoscience), technical capacity (AI/machine learning/data science), and educational infrastructure. The project’s mission focuses on three key objectives: expanding the Arctic-AI research network, developing customized training materials, webinars, and workshops, and fostering a scalable and sustainable community of learners. By weaving together open-access modules, hands-on workshops, and a network of instructors, Cyber2A is less about one-off workshops and more about seeding a durable ecosystem of AI for Arctic science.

As Mike Loranty, Professor of Geography & Environmental Studies at Colgate University, and participant of the 2025 workshop puts it:

“It [AI] has altered the questions we think about answering, and the techniques we use to answer them.”

This reflects a distinct shift: as AI reshapes the questions and methods of science, it also demands that we train not only scientists, but the educators and curriculum designers who can translate these changes into the classroom for future scientists.

Workshop 1: Training Arctic Researchers in AI and Machine Learning (October 2024)

Cyber2A’s first major workshop targeted scientists working in the Arctic, many with strong geoscience backgrounds but limited computational or AI exposure. The aim was to offer a data science training environment tailored to Arctic contexts through exploration of large datasets, dynamic environmental systems, and unique logistical challenges. Through lectures, hands-on coding, guided exercises, and small group discussions, participants explored topics such as AI fundamentals, model workflows, reproducibility, ethics, and domain-specific use-cases.

Beyond training, the workshop served as a proof-of-concept: What is the best way to teach Arctic researchers to adopt AI workflows effectively? And perhaps more importantly, what do they need to succeed? Feedback from the 2024 training informed iteration of the curriculum and shaped the design of the 2025 workshop.

Workshop 2: Shaping the Future of AI Education Through Curriculum, Pedagogy, and Community (October 2025)

Building on the foundation of last year’s training, Cyber2A’s 2025 workshop pivoted to focus on empowering educators and trainers to integrate AI and machine learning into their teaching. The agenda was organized around three intertwined themes: designing effective AI lessons, applying pedagogy best practices, and strengthening the community of Arctic data science educators. Participants reviewed and refined the 2024 curriculum, identified key gaps, and collaboratively redesigned modules from an instructor’s point-of-view. Through guided pedagogy sessions, they explored how to teach AI concepts across diverse skill levels and contexts, culminating in shared guidelines and annotated teaching examples.

Midway through the week, the focus shifted from lesson design to community visioning. Small teams drafted a white paper outlining what a sustainable AI-for-Arctic learning network could look like, emphasizing shared resources, open dialogue, and the responsible use of AI in research and teaching. By the end of the workshop, participants had not only co-created improved teaching materials but also helped define what should be taught, how it should be taught, and who will carry this work forward.

Why This Matters

Cyber2A is more than a set of workshops and webinars – it represents a new model for advancing data-intensive science through collaboration and open education. Its emphasis on modular, reusable curricula and a community of trained educators directly supports NCEAS’ mission to build data fluency and computational capacity across the environmental sciences. By translating advanced AI and machine learning methods into accessible, field-relevant training materials, Cyber2A extends that mission into the Arctic, where data challenges are both acute and globally significant.

The project’s focus on empowering educators and training individual scientists ensures that workshop results extend beyond in-person participation. Each trained instructor becomes a catalyst for future learners, creating a ripple effect that strengthens the broader research network. In doing so, Cyber2A illustrates how NCEAS’ collaborative training approach fosters not only technical skill but also a culture of responsible and community-driven innovation.

AI is not replacing scientists but extending their capacity, and that extension only becomes meaningful through training, materials, and community support. As one participant reflected, “I use AI as a support tool and am always aware about its limitations and most importantly, the ethical aspects of its use” - Rabindra Parajuli, Postdoctoral Researcher at the University of Georgia.

Where We’re Headed

In the evolving landscape of Earth-system science, mastering AI and advanced computational techniques is no longer optional, it’s essential. Through Cyber2A, Arctic researchers and educators alike are gaining access to tailored pathways that blend geoscience insight with computational capability. The two workshops, the first to train researchers and the second to co-develop AI teaching best practices, illustrate a thoughtful evolution from delivery to community-building.

 As the Arctic continues to be one of the most important regions for monitoring, equipping researchers with the right tools, curricula, and networks to facilitate effective use of AI may be the difference between gathering data and producing impactful science. Cyber2A is helping turn that possibility into reality.