At a recent lunch gathering hosted by HAIL (the Hub for AI and Data Science Leadership) and the Mascaro Center for Sustainable Innovation, this summer’s MCSI research cohort sat down with faculty advisors and industry-connected guests to share project updates and dig into one of the most pressing questions in higher education today: What does it mean to learn in the age of AI?
By: Michael Colaresi, PhD | Director, Hub for AI and Data Science Leadership
I know I'm not alone in finding AI dazzling, dizzying, and depressing—sometimes all at once. But sitting in front of my screen, just me against a billion-parameter model and a suite of surveillance tools with a profit motive, the work can feel lonely and the decisions daunting.
What can I teach learners in my classes that will help them lead in the age of AI?
One of the most common mistakes institutions make when approaching AI education is treating it as a single thing to be delivered — a workshop, a module, a certification, a tool. Attend the session, check the box, move on. Provide it and it will be used.
"AI literacy." "AI fluency." These terms are (and have been) everywhere in higher education. These terms appear in strategic plans, faculty development programs, and institutional reports. And while they point at something real and important, they can also feel frustratingly abstract. What does it actually mean to be "AI literate"? How do you know when you've achieved it?
We are pleased to introduce the newest members of the HAIL Graduate Student Awardees Program!
Through this program, the following students have the opportunity to explore the questions: "What does responsible data and AI use and development entail in different disciplines or professions?" and "What individual actions can be taken to practice responsible data and AI use and development?"
More information about the program is available here.
At this year’s DataSci+AI Forum, we wanted to know something simple: how is our community actually using AI? Not in theory — in real life, right now. So we put up a board and asked everyone to place a dot.
With the DataSci+AI Forum on March 26–27 just around the corner, we've been thinking about what it means to build AI strategy that's grounded in real human experience. A study conducted last year, right here at the University of Pittsburgh offers a timely and provocative set of answers.
The headlines say students are cheating their way through college with AI. The data says it's more complicated.
Pitt's own Annette Vee — Associate Professor of English, Director of the Composition Program, and a leading researcher on AI and writing — recently synthesized findings from over ten major reports on student AI use, including her own original research with Pitt students. Here's what stands out.
