How Do You Teach Computer Science in the A.I. Era?
Universities across the United States are confronting a major challenge: how to teach computer science in an era transformed by generative artificial intelligence (A.I.). As rapidly evolving A.I. technologies disrupt the traditional landscape of computing, educators are reevaluating curricula to reflect the new realities and demands of the field.
At the forefront of this shift is Carnegie Mellon University, renowned for its prestigious computer science program that has long produced graduates who join leading tech firms, innovative start-ups, and cutting-edge research institutions around the world. Despite its history of success, Carnegie Mellon’s faculty recognizes the necessity of rethinking their approach to education to keep pace with the advancements in generative A.I.
Thomas Cortina, professor and associate dean for undergraduate programs at Carnegie Mellon, describes generative A.I. as having “really shaken computer science education.” This technology, which underpins chatbots like ChatGPT capable of producing humanlike essays and answers, is rapidly making significant inroads in academia. But its impact is especially profound on computer science, a discipline traditionally focused on coding—the language through which humans communicate with computers.
The emergence of A.I. code generators threatens to redefine the role of the programmer. Large technology companies and startups alike are rolling out A.I. assistants that can write computer code, increasingly matching and sometimes exceeding human capabilities. Mark Zuckerberg, CEO of Meta, predicted in early 2025 that A.I. would soon perform at the level of a midlevel software engineer, signaling a seismic shift in the nature of software development.
Faced with this changing landscape, computer science programs nationally are scrambling to understand and respond to these transformations. They question what core knowledge must endure and how their teaching methods should evolve. Some experts argue for reducing the emphasis on mastering specific programming languages, which may become less relevant given A.I.’s ability to write code. Instead, there is growing advocacy for focusing on computational thinking and developing A.I. literacy, enabling students to understand and effectively utilize these powerful tools rather than merely coding by hand.
Mary Lou Maher, director of the Computing Research Association, highlights that future computer science education will likely emphasize broader skills such as problem-solving with A.I. and understanding algorithmic logic. Moreover, educators are exploring hybrid courses that infuse computational skills into other professions, recognizing that A.I. will impact a variety of fields beyond traditional tech roles.
As universities adapt, the larger question looms: what will the jobs of the future look like in an A.I.-driven economy? The answers remain in flux, but what is certain is that the era of generative artificial intelligence demands a fundamental rethink of how computer science is taught. This transformation will shape the competencies of the next generation of technologists and, ultimately, the future of innovation itself.
