Leading the future of materials science education in the AI era

Artificial intelligence opens enormous opportunities to transform materials science and materials-driven technologies, and the Department of Materials Science and Engineering at the University of Wisconsin-Madison is training the next leaders of that transformation. AI is being woven into what we teach, how we teach and how our students participate in discovery, all while strengthening the hands-on foundation that anchors our discipline.

What We Teach Has Evolved 

AI is now a core part of the intellectual toolkit our students develop. Faculty have integrated machine learning, data‑driven design and AI‑enabled analysis into courses across the curriculum, from introductory computational classes to advanced design electives. 

In our Machine Learning for Materials course, for example, students are quickly brought up to speed in the latest machine-learning software and AI‑assisted coding tools. This allows the course to focus on deeper challenges: interpreting complex datasets, evaluating model performance and applying machine learning to real materials problems. In Alloy Design, students use Large Language Models (LLMs) like Gemini and GPT to extract insights from research papers, generate hypotheses and explore design space before turning to more traditional tools of thermodynamic modeling to test their ideas. And in our computational materials course, students work through the full machine‑learning pipeline from data cleaning to prediction using hands-on, computational labs that mirror the workflows used in advanced industrial and academic research groups. 

These additions aren’t side modules or optional extras: They are central components of how we prepare students for a field where AI is embedded in every stage of innovation. 

How We Teach Has Transformed 

As AI has become part of the curriculum, it has also reshaped our pedagogy. Faculty are designing learning environments where AI supports deeper engagement, clearer communication, and more efficient preparation. 

Students use AI to refine the structure and clarity of technical presentations, identify gaps in their understanding and generate personalized practice materials. Instructors are building frameworks that help students use AI thoughtfully, as a tool for both exploration and reflection. 

This shift has opened space for more experiential education. With AI handling routine tasks, instructors can focus class time on design sprints and collaborative problem solving. Students spend more time learning by doing, and in turn become confident and effective engineers and researchers. 

A Department Moving Forward Together 

Across the department, faculty are aligned around a shared vision for AI‑enhanced, hands‑on education that is changing both how and what we learn. This integrated approach to AI is creating impact that is more than the sum of its parts, an impact seen perhaps most dramatically in expanding undergraduate research opportunities. 

MS&E faculty initiated the Informatics Skunkworks, a group dedicated to helping undergraduates participate in collaborative, project-based science and engineering informatics research. Skunkworks participants learn critical skills in teamwork, presentation, project management, software development, and applied data science, as well as drive data-centric approaches that are transforming science and engineering. This group has engaged more than 600 undergraduates in AI‑driven projects.  

Senior design teams are building machine‑learning models for real research challenges, such as predicting carbon nanotube alignment for nanoelectronics. New opportunities are emerging in autonomous experimentation, laboratory automation and AI‑accelerated materials discovery, where undergraduates contribute alongside graduate students and postdocs. Inspired by the impact of hands-on research, we will provide guaranteed opportunities for all materials science majors starting in 2026, and we expect many to pursue projects with tight AI integration. 

AI has broadened participation and accelerated progress, giving students earlier and more meaningful access to research. 

A Future Built with Intention 

Our department’s approach to AI is defined by a shared desire to equip our students with the best tools and skills to thrive and lead. We are preparing students for a future where AI is inseparable from materials innovation, and we are doing so in a way that strengthens the hands‑on, inquiry‑driven character of our program. 

Our students learn to think with AI, to question it, to refine it, and to use it to push their ideas further. They graduate ready to contribute to a rapidly evolving field and ready to lead where it goes next.