Universities Must Rethink AI Education for the AI Economy
Artificial intelligence (AI) is rapidly transforming the workforce, from ambient note‑taking and AI‑assisted drug discovery to automated cybersecurity testing and product design. As employers increasingly seek workers who can apply AI alongside domain expertise, universities face growing pressure to
Artificial intelligence (AI) is rapidlytransformingthe workforce, from ambient note‑taking and AI‑assisted drug discovery to automated cybersecurity testing and product design. As employers increasingly seek workers who canapply AI alongsidedomain expertise, universities face growing pressure to prepare graduates for this changing labor market. Many institutions are expanding their AI offerings, but the strongest programs move beyond standalone AI or computer science degrees by integrating AI across the curriculum and into fields such as health care, business, engineering, and the social sciences. Universities should follow these models to equip graduates with the skills to succeed in the modern workforce. Institutions across the country offer examples for others to follow. The University of North Dakota’s new Bachelor of Science in Artificial Intelligence combines technical foundations—including machine learning, data science, and programming—with opportunities toapply AIin areas such as bioinformatics, computational chemistry, quantum computing, and AI ethics. Rather than training students solely to build AI systems, the program emphasizes using AI to solve practical problems across scientific and technical fields. The Texas university system shows how AI education can scale across multiple institutions by tailoring programs to regional workforce needs. The University of Texas at Austin integrates AI into engineering, business, and public policy through itsGood Systems initiative, a campus‑wide research effort developing human‑centered, socially responsible AI for challenges such as misinformation, smart‑city design, and public‑interest technology. Texas A&M Universityincorporates AIacross engineering, agriculture, health care, and manufacturing research, aligning AI training with the state’s major industries. Together, these institutions create a statewide model for distributed, discipline‑specific AI education. Syracuse University has launched a comprehensive AI education model built around a university‑wide portfolio that integrates AI across academic programs, research, and student development. Beginning inFall 2026, Syracuse will offer new bachelor’s and master’s degrees in AI, an integrative AI degree, seven interdisciplinary AI minors, and expanded undergraduate research opportunities through its student‑led United AI organization. A unique feature of the program is a peer‑ledAI bootcampthat gives students hands‑on experience with real tools and workflows from their first days on campus, an early‑immersion model that prepares students to use AI before they even choose a major. The bootcamp also offers stackable micro-credentials and project‑based learning that help studentsdevelop applied AI skillsand gain practical experience through research and industry partnerships. The University of Maryland’snew AI programsrecognize that AI adoption requires both technical expertise and domain knowledge. While one degree focuses on building AI systems, the other prepares graduates to evaluate and deploy AI withinorganizational, legal, and policy settingswhere adoption decisions are increasingly made. The University of Washington launched aninterdisciplinary AI minoropen to students across all majors. Set to begin in Spring 2027, the program blends technical coursework with instruction on ethics, societal impacts, and real‑world decision‑making. A defining element is acapstone-style projectin which students use AI tools to address a problem in their own discipline and then assess how the AI‑enabled solution differs from what they could have produced without it. This applied requirementhelps students understandboth the strengths and limits of AI in context, giving them a clearer understanding of when the technology adds value and when traditional methods may be more effective. Across these universities, a common pattern emerges: The strongest AI programs extend well beyond standalone computer science or AI degrees. This interdisciplinary approach reflects thegrowing demandfor professionals who can evaluate, integrate, and responsibly use AI within existing workflows. By embedding AI throughout existing academic programs and pairing AI education with domain expertise, these universitiesprepare studentsto apply the technology within the industries where they will work rather than treating AI as a niche specialty. This approach also reaches a broader student population, producing graduates who combine field expertise, such as public health or urban planning, with practical AI skills. To succeed in an AI-enabled economy, workers in virtually every industry will need to develop AI fluency. Universities should move beyond isolated AI majors and integrate AI literacy across their curricula, so graduates learn to apply AI within their chosen professions. But preparing the workforce also means helping today’s workers adapt. Policymakers should support competitive grants for universities to develop interdisciplinary AI curricula, encourage partnerships between higher education and industry, and expand support for stackable AI microcredentials and certificate programs that allow workers to build AI skills throughout their careers. Building an AI-ready workforce requires more than training the next generation of AI engineers—it requires giving students and incumbent workers alike the opportunity to develop practical AI skills that complement their existing expertise. Image credit: Generated with DALL-EDavid KertaiDavid Kertai is a research assistant specializing in cybersecurity at ITIF. He holds a B. A. in European studies and French from the University of Washington and a Master of Arts in Security Policy Studies from George Washington University.