AI Showcase: Discipline-driven approaches
In higher education, there’s no one-size-fits-all solution to generative AI – the many different disciplines at UW–Madison need different strategies to effectively support their students and instructors. Here’s a look at how some of this work is unfolding in different corners of campus.
College of Engineering
Finding consensus and common language around AI for teaching and learning can be challenging and grows more so with scale. To support productive discussions at the department level, leaders in Chemical and Biological Engineering reached out to the Center for Innovation in Engineering Education (CIEE) for assistance with a series of faculty-driven workshops on AI, machine learning, and teaching among their faculty and instructors.
The first session focused on level-setting understanding of AI and intent on AI policy for education. Prof. Joel Paulson helped participants understand the capabilities (and limitations) of Large Language Model (LLM) tools and how university policies protect privacy and data security. Prof. Rose Cersonsky led the department in discussing opportunities to use AI ethically in teaching practices. Teaching Faculty Brendan Blackwell directed the group in thoughtful discussion of potential policies for the department.
As the workshop series continues, participants will explore AI-related skills, tools, and more. To get support with similar discussions in your Engineering unit, please contact Erica Hagen at CIEE.
School of Human Ecology
“As human ecologists, our focus is the art and science of everyday life,” says Soyeon Shim, dean of the School of Human Ecology. “In times of rapid change and uncertainty… we have a wealth of understanding and expertise that can help guide us all along this unprecedented journey – with human thriving as our north star.”
Human Ecology faculty and staff have developed a school-wide approach to AI and its impacts and potential uses in teaching, research, outreach, operations, and communications.
“The constellation of disciplines represented throughout the school uniquely positions faculty, staff and students to ask critical questions about AI that are rooted in the exploration of research and principles of teaching, and with the aim of keeping a human-centered equity focus, and optimizing the impact of the innovation on overall wellbeing,” says Lori DiPrete Brown, distinguished teaching faculty in Civil Society & Community Studies and the school’s inaugural Human Ecology Imperative Fellow.
Read the full article about Human Ecology
School of Nursing
“AI is a tool that needs to be used in the right way to get the best results for the patient,” School of Nursing instructor Britta Lothary, MSN, ANP-C, tells her students. “Your nursing gut is still going to be needed.”
As Nursing instructors adapt to and even embrace AI, the School requires them to address in their syllabi if and how AI is to be used in their courses. Instructors decide whether to incorporate AI usage or discuss its implications in class.
Some instructors, like Lothary, are using AI to develop class assignments and assessments, or encouraging students to use the tools to help them study. Others are researching the impact of AI on nursing practice. Still others, whether concerned with the potential for academic misconduct or waiting for broader uptake of generative AI tools in clinical settings, have so far eschewed the technology.