SciPy 2025

Ryan C Cooper

Ryan C. Cooper is an Associate
Professor-in-Residence at the University of Connecticut. His background
is in mechanics and materials science with an emphasis on numerical
simulations and engineering education. He has been using Jupyter and
GitHub to enhance the classroom experience for over six years. Prof.
Cooper has developed and free open source materials for computational
work in engineering and volunteered with the NumPy documentation team.
Ryan is an integral part of the AI in the School of Engineering
committee. He has a Ph.D. from Columbia University and spent two and a
half years at Oak Ridge National Laboratory as a Postdoctoral
researcher.

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Sessions

07-09
15:25
30min
Generative AI in Engineering Education: A Tool for Learning, Not a Replacement for Skills
Ryan C Cooper

Generative Artificial Intelligence (AI) is reshaping engineering education by
offering students new ways to engage with complex concepts and content. Ethical
concerns including bias, intellectual property, and plagiarism make Generative AI
a controversial educational tool. Overreliance on AI may also lead to academic
integrity issues, necessitating clear student codes of conduct that define acceptable
use. As educators we should carefully design learning objectives to align with
transferrable career skills in our fields. By practicing backward design with a
focus on career-readiness skills, we can incorporate useful prompt engineering,
rapid prototyping, and critical reasoning skills that incorporate generative AI.
Engineering students want to develop essential career skills such as critical
thinking, communication, and technology. This talk will focus on case studies for
using generative AI and rapid prototyping for scientific computing in engineering
courses for physics, programming, and technical writing. These courses include
assignments and reading examples using NumPy, SciPy, Pandas, etc. in Jupyter
notebooks. Embracing generative AI tools has helped students compare, evaluate,
and discuss work that was inaccessible before generative AI. This talk explores
strategies for using AI in engineering education while accomplishing learning
objectives and giving students opportunities to practice career readiness skills.

Teaching and Learning
Room 318