# A Crash Course in AI - University of Pennsylvania

*Источник: University of Pennsylvania*
*Дата: 2026-07-13*
*Язык: en*

**Кратко:** A Crash Course in AI
Some 75 faculty members across many disciplines and several Penn schools attended a two-day program led by Bhuvnesh Jain, Co-Director of the Data Driven Discovery Initiative and Co-Chair of the University’s AI Council. Questions like these brought 75 people—about two-thirds from Penn Arts & Sciences, the rest faculty from other Penn schools— out for a hands-on AI training spanning two days in June.

A Crash Course in AI
Some 75 faculty members across many disciplines and several Penn schools attended a two-day program led by Bhuvnesh Jain, Co-Director of the Data Driven Discovery Initiative and Co-Chair of the University’s AI Council.
How is artificial intelligence evolving? How does it even work? Questions like these brought 75 people—about two-thirds from Penn Arts & Sciences, the rest faculty from other Penn schools— out for a hands-on AI training spanning two days in June.
“Every professor is grappling with questions around this technology and what it means for their teaching and research,” says Bhuvnesh Jain, Walter H. and Leonore C. Annenberg Professor in the Natural Sciences, who created and oversaw the successful “How AI Works” program. “The level of interest was impressive. Ten minutes before we started, every seat was filled and nearly everyone stayed until the end of the day.”
For some time, issues around AI have been top of mind for Jain, an astrophysicist who co-directs the Penn Center for Particle Cosmology and the Data Driven Discovery Initiative, and co-chairs Penn’s AI council, which maps and enhances AI efforts across campus. Jain also serves as the School’s AI lead and Advisory Group Chair, work central to the recently announced strategic vision SAS Horizons: Pathways for a Changing World, which solidifies AI as a major priority area.
The mini-course Jain created evolved from a class he teaches in the College of Arts & Sciences, Introduction to AI: Concepts, Applications, and Impact. Karen Detlefsen, Adam Seybert Professor in Moral and Intellectual Philosophy and Professor of Education, asked Jain whether it would be possible to condense that material for faculty. “I was looking to gain a deeper understanding of how AI works, where its powers and limitations are,” Detlefsen says. “This is all in service of my belief that having a solid, theoretical understanding of AI will make me a better user of AI in practice.”
To create the course work, Jain boiled down a full semester into two jam-packed days guided by a series of modules, four that he led, two led by guest lecturers Chris Callison-Burch, a professor of computer and information science in Penn Engineering, and Laura Zarrow, executive director of Wharton Generative AI Labs.
The program began at a natural starting point, examining what makes a machine “intelligent” using examples from everyday life like Netflix’s recommendation algorithm or robot vacuums that can perform cleaning tasks by figuring out room layouts. The modules grew more complex from there, diving into the nuances of large language models and issues like hallucinations—when AI makes up text seemingly at random.
As someone already seeking out these technologies in his own work, Professor of Biology Michael Lampson says he found the progression useful and informative. “I’ve been trying to take advantage of AI tools in my research and wanted to learn more,” he says, explaining what drew him to the program.
Jain notes that interest was well-distributed across the natural sciences, social sciences, and humanities. “We had a number of faculty from literature and foreign languages, but also some AI-savvy scientists and engineers,” he says. “It was clear that people who use AI in their own research still want more perspective.”
Anne Albert, a historian and Director of Operations and Communications for the College, found the course deeply informative. This past spring, the College’s AI Working Group, which she chaired, recommended that faculty and students alike have opportunities to learn not just how to use AI but to understand how it works. “Knowing more about what goes on under the hood leads to using it more responsibly and with more savvy, as well as being more informed about a choice not to use it,” says Albert, who guest lectures in the History Department.
I was looking to gain a deeper understanding of how AI works, where its powers and limitations are. This is all in service of my belief that having a solid, theoretical understanding of AI will make me a better user of AI in practice.
She also found the diversity of disciplines across course attendees notable. “It’s fascinating that grappling with AI is a place where humanities and STEM thinkers converge.”
That range of expertise fueled lively conversations around everything from the environmental and surveillance concerns AI poses to its upsides for research. The extent of student fluency with AI also surprised many faculty unaware of just how widely its classroom usage has grown. Jain says questions around pedagogy and the value of liberal arts education similarly spurred thoughtful dialogue.
“Our humanities faculty brought in some interesting concerns about how to integrate AI tools while making sure students still learn critical-thinking skills,” he says.
By the event’s conclusion, even faculty like Lampson already using AI left with a deeper understanding of the technology. “I liked learning about the history and getting some intuitive feel for how modern data-driven approaches work,” he says. Lampson’s positive takeaways seem to reflect broader attendee experience: In a feedback survey, the majority of participants praised the set-up and content, and 95 percent of respondents found it valuable overall.
In fact, demand for the initial pilot was so high that it had to be moved to a bigger venue. And those who couldn’t attend will likely have a similar opportunity soon as Jain and colleagues brainstorm ways to expand the offering to reach the University’s more than 1,000 faculty. Future iterations might be hybrid, with lectures available online followed by in-person lab and discussion sessions.
Regardless of format, the focus will remain on keeping the information accessible across disciplines. “The bottom line is that I want everyone—from a history professor to a scientist—to gain a nuanced understanding of the key concepts behind modern AI,” Jain says. “I want them to walk away with confidence and a sense that they’re ready to meet the challenges AI poses while exploring the new opportunities it is opening up.”

[Оригинал](https://omnia.sas.upenn.edu/story/how-ai-works-mini-course-bhuvnesh-jain)