Applying AI to accelerate discovery, foster learning
At an Alumni Weekend panel moderated by President J. Larry Jameson, Presidential Associate Professor César de la Fuente of Penn Medicine and Hamsa Bastani of the Wharton School highlighted how research at Penn is putting AI insights and tools into practice.
3 min. read
From left to right: Penn President J. Larry Jameson, Hamsa Bastani of the Wharton School, and César de la Fuente of Penn Medicine at the Annenberg Center for the Performing Arts.
At a recent Alumni weekend event, Penn President J. Larry Jameson led a conversation with Presidential Associate Professor César de la Fuente of Penn Medicine and Hamsa Bastani, associate professor of operations, information, and decisions in the Wharton School, exploring how AI has influenced biology and education.
Applying AI for new solutions to existing problems
De la Fuente’s lab, which bridges the Perelman School of Medicine, School of Engineering and Applied Science, and School of Arts & Sciences, leverages machine learning to speed up the discovery of lifesaving drugs and combat antibiotic resistance. Bacterial infections that are resistant to existing medications is a growing global health crisis linked to nearly five million deaths worldwide annually.
“We have an opportunity here … to think outside the box, to try to come up with solutions against what I think is one of the greatest challenges we’re facing as a society—and really for the future of humanity,” de la Fuente said.
Jameson, an endocrinologist and biochemist, noted that antibiotics are still relatively new in medicine, dating to the discovery of penicillin in the 1940s, and emphasized that Fuente’s machine-learning approach to discovery is still an unconventional one.
De la Fuente explained the AI-powered approach treats all of biology as “a soup of letters” that AI systems can explore as code in its efforts to identify new compounds and molecules that might be the next antimicrobial used to save lives. His lab’s approach is already uncovering new antibiotics in ancient microbes and in snake and spider venom.
At Penn, de la Fuente said, the ability to apply AI and bring together top minds from different disciplines—computer scientists, engineers, biologists, and physicists—has allowed faculty to “push the boundaries of knowledge.”
“There’s magic that happens when you bring together a team of people who have completely different backgrounds,” he added, “and that magic leads to breakthroughs.”
Applying AI to personalize future learning
Bastani, whose research focuses on developing machine learning algorithms for data-driven decision-making, gave a brief overview of a long-running desire to personalize learning. She described how, when generative AI surfaced widely in 2023, there was much speculation about it making learning faster, easier, and reducing disparities, because “not everyone has access to a great teacher, but everyone has access, basically, to ChatGPT,” Bastani said.
However, she added, research has demonstrated that learning “really comes from doing”—and from struggling. When students short-circuit a problem by retrieving answers quickly, she explained, they may feel more productive but aren’t necessarily learning.
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“Now that [researchers have] run long-term studies, we see they actually lose engagement and motivation [when using AI] because part of what makes you excited to do something is the struggling,” Bastani said. “Nobody likes to struggle, but when you struggle and figure something out and have that eureka moment, that’s what gives you motivation and excitement. And so, that starts getting eroded as well.”
The research is currently focused on how AI can help understand a particular student’s learning needs so no one is left behind.
“Students are talking to these chatbots in real time, and that’s giving us a really rich window into their thinking process, what they’re struggling with, and so we actually understand from these signals where they are in the learning curve,” she said. “We can [for example] personalize practice problems to them. Being able to cater to the student at their level is critical to keep motivation and learning going.”
The human-AI collaboration
Jameson, in closing, asked the researchers about the human-AI relationship.
De la Fuente spoke of it as an augmentation tool for his team—akin to the development of the calculator. Bastani acknowledged that the human-AI relationship in learning will evolve as time goes on, by way of newer generations growing up with a different learning process. Ensuring humans still have “critical learning and thinking processes underneath,” she says, will be a challenge.
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