How AI is Helping to Prepare Students for the Future

Shortly after start-of-the-quarter introductions, typical statistics and econometrics classes can start to feel like a test of patience. Programs crash, coding bugs appear, and lessons stall as an array of charts and complex formulas hover on the board. Much like learning a new language, grasping the symbols, software, and logic of data can feel daunting to students less familiar with the topic at hand. At Chicago Booth, professors are rethinking that first-day experience. With the introduction of artificial intelligence, they’re finding new ways to lower technical barriers while challenging students to think critically about when and how to use these tools.

We sat down with two professors who are pioneering this change through Booth’s new Applied AI concentration, each providing a distinct approach to preparing students for an AI-powered future. This fall, Professor Veronika Rockova, Bruce Lindsay Professor of Econometrics and Statistics in the Wallman Society of Fellows launched her course, Data Intelligence. The course will fold in a new focus on data storytelling—the ability to interpret, analyze, and communicate insights from data. In another new course set for winter quarter 2026, Dacheng Xiu, Joseph Sondheimer Professor of Econometrics and Statistics will teach AI Essentials. His class is an introductory toolkit for navigating the cutting-edge field of AI. 

While their courses take different paths, both share a commitment: equipping future leaders with the skills and judgment to lead in an unpredictable, rapidly evolving technological landscape.

Trading Coding for Confidence

The Problem: When Technical Barriers Block Learning
Having taught data-focused courses for years, Rockova noticed frustration in business students with less technical backgrounds. Week after week, teaching assistants fielded the same coding questions: students stuck on small errors, grappling with using different versions of RStudio or struggling when libraries worked in one version but not another. The issues that arose were not rooted in misunderstanding or a lack of learning but about getting the necessary and often unfamiliar tools to cooperate. 

The Solution: AI Aiding Teaching Assistants
Enter ChatGPT. Rockova explains how these AI tools and algorithms are able to instantly catch and address those small mistakes or explain technical glitches in a more personalized way. “In those cases, tools like ChatGPT save valuable time, letting students focus on learning rather than troubleshooting,” Rockova said. “It’s a huge help for students still building confidence with technical tools. The shift has been impactful, making it easier for students to learn and for professors to teach. What once soaked up valuable office hours and teaching assistant time now happens in real time on students’ own time, allowing them to maintain their momentum, ask endless questions, and build the confidence necessary to tackle more complex concepts.

The Goal: Accessibility and Engagement
As Professor Rockova wraps up her first few weeks of teaching Data Intelligence, she shares her excitement for making data analysis accessible and engaging. She believes learning should be fun, even when it’s challenging, and recognizes that the way information is taught makes all the difference. Her goal is to guide students from feeling overwhelmed by complex data to having confidence in exploration and discovery. “Many students find statistics intimidating because it’s often taught in a heavy, math-focused way,” she explained. “I focus on making it simple, intuitive, and connected to real data and stories, hoping to inspire a genuine curiosity for the subject.”

Named the ‘Professor of Uncertainty’ in a recent episode of the Chicago Booth Review Podcast, Rockova sees uncertainty as a fundamental part of both AI and statistics. A field rooted in randomness and probability, sharp statisticians acknowledge that good decisions require the recognition of limitations and weaknesses in human knowledge. While AI can expand those limits, overconfidence in its abilities can be detrimental. Her goal is for students to develop the skills and humility needed to approach AI with curiosity, caution, and care.

Building Judgment through Understanding

The Challenge: Clarity Amid Complexity
While he shares Rockova’s cautionary element in his approach, Professor Xiu’s course takes on the introduction of AI in the classroom from a different direction. After seeing a Google DeepMind AI program beat a human at the ancient Chinese board game, Go—a board game previously thought to be too complex for a computer—Xiu realized something: AI could solve problems once thought intractable. “Since then, AI and machine learning have become central to both my research and my teaching,” he shared.

As he designed his class AI Essentials, Xiu’s goal was centered around teaching students to think critically about AI itself. His teaching aims to imbue a conceptual understanding and a practical toolkit upon his students, as they face strategic decisions about data, technology, and talent upon entering the business world. The balance between understanding and application is essential as Xiu helps students develop judgment in deciding when AI tools add real value and when they contribute to poor quality outputs. In today’s data-driven and technological world, internalizing the difference is increasingly vital. 

The Approach: Understanding, Not Coding
“I want them to embrace AI rather than shy away from it—not by writing code, but by understanding what these tools can and cannot do,” Xiu explained. His course is inspired by the rise of AI in all facets of business and society, focusing not on technical proficiency, but on managerial literacy. Xiu hopes that students leave with an understanding of the philosophy and intuition behind technologies like ChatGPT, understanding where AI can create measurable value. 

Instead of simply understanding how AI works, they will be equipped with the ability to be thoughtfully skeptical but curious, understanding both the promises and pitfalls. “If students finish with the ability to scope problems, collaborate effectively with technical teams, and make informed decisions about data and AI, I consider the course a success,” said Xiu. He focuses on key issues facing the evolution of AI, such as bias and responsible development. As AI becomes inherently intertwined in the business landscape, leaders must be able to leverage its strengths while recognizing the limitations, to set apart real problem solving from illusions of insight.

Ahead of the Curve
AI and machine learning are constantly evolving at an unprecedented pace. Because the field changes so quickly, Professor Xiu is constantly updating the materials and tools being used in his classroom, pushing him to consider potential business applications as well as the social, economic, and ethical issues that inevitably arise. Ahead of winter quarter, he is already staying one step ahead in the developing field. “Teaching this class is not only intellectually stimulating, but also deeply rewarding and it ensures that both my students and I stay at the frontier of these important developments.” Xiu explained. His new course is one that calls for a continuous evolution, mirroring the nature of the technology it examines. 

Two Paths, One Mission

Despite their different approaches, Professors Rockova and Xiu share a deeper commitment, one that defines Booth’s new AI concentration. Both are guiding students in mastering the language of AI as a critical part of preparing business leaders to face a landscape fundamentally shaped by the integration of technology into every facet of society.

Whether building confidence through hands-on problem-solving or developing nuanced judgment about AI's capabilities and limitations, both professors aim to ensure students leave their class with the skills to approach AI tools with curiosity and critical thinking. The beauty of these courses is their accessibility: whether students arrive incredibly proficient in coding or completely new to technical work, whether they are drawn to statistics or to the broader questions AI raises about business and society, there is a clear path forward. Both professors bring genuine enthusiasm to meeting students where they are and preparing them for a business world increasingly defined by AI.

Together, professors Rockova and Xiu are exemplifying Booth’s dedication to shaping the leaders of tomorrow.

AI Beyond the Classroom

These new and evolving AI-centered courses at the University of Chicago Booth School of Business reflect just how much AI is redefining business education and how much technology is changing the business world. As the field rapidly advances before our eyes, equipping students with the right skills and mindset has never been more critical. The Center for Applied Artificial Intelligence (CAAI) proudly supports this mission by connecting cutting-edge research with real-world applications, both in education and beyond, striving to harness AI for societal good. Within Chicago Booth, CAAI acts as a catalyst, preparing the next generation of leaders to navigate and shape the future of AI-powered business and society.

See a full list of Upcoming Classes in Applied AI/ML for the 2025-2026 Academic Year.