07-10, 13:15–13:45 (US/Pacific), Ballroom
In this talk, I will discuss learning, ethical, and legal issues when using large language models to supplement learning and practicing scientific computing. Engineering disciplines are becoming
increasingly dependent upon computational tools and resources. Writing scientific computing code has become increasingly easier with GitHub CoPilot, ChatGPT, and more LLM tools. Problems can arise when
practitioners begin to use code without reviewing and understanding why it was written that way. I will present my findings when incorporating ChatGPT into the wealth of learning resources and how I discuss academic integrity in relation to U.S. copyright law and ethical responsibility. What constitutes intellectual contribution" and "independent work" and "plagiarism" when we resuse code from open source software and from LLMs?
This talk will introduce the methods for learning numerical methods using NumPy and SciPy in my classes at the University of Connecticut. Then, I will describe the new approaches that students have begun to use by embracing ChatGPT and how these relate to learning objectives related to critical thinking, problem solving, and practical coding skills. Then, I will review current US copyright laws and case studies regarding the use of AI to claim legal ownership of creative products. We don't
require students to copytright their assignments, but our academic integrity agreements make it clear that work should be their own intellectual contributions. The role of the student in the creation,
comparison, and improvement of LLM results should be clearly stated; this would be same case for using references to describe formulas, algorithms, or solutions. I will conclude with current best-practices
and future work for using or avoiding AI use to learn to code and use numerical methods.
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.