Universities are expanding their AI offerings under pressure from a labor market that increasingly demands graduates who can apply artificial intelligence alongside domain expertise. The strongest programs, however, are moving beyond standalone AI or computer science degrees to embed AI across disciplines such as health care, business, engineering, and the social sciences.
Employers are not just looking for engineers who build AI systems. They need professionals who can evaluate, integrate, and responsibly use AI within existing workflows. A handful of institutions are now modeling what that looks like in practice.
Building AI into every discipline
The University of North Dakota's new Bachelor of Science in Artificial Intelligence pairs technical foundations - machine learning, data science, and programming - with applied coursework in bioinformatics, computational chemistry, quantum computing, and AI ethics. The program emphasizes using AI to solve practical problems rather than training students solely to build systems.
The University of Maryland has taken a dual-track approach. One degree focuses on building AI systems. The other prepares graduates to evaluate and deploy AI within organizational, legal, and policy settings where adoption decisions are made. The University of Washington, meanwhile, will launch an interdisciplinary AI minor in Spring 2027 open to students across all majors. It blends technical coursework with instruction on ethics, societal impacts, and real-world decision-making. A capstone-style project requires students to use AI tools to address a problem in their own discipline, then assess how the AI-enabled solution differs from what they could have produced without it.
This kind of applied requirement helps students understand both the strengths and limits of AI in context. They learn when the technology adds value and when traditional methods may be more effective.
Early immersion and hands-on learning
Syracuse University has built a university-wide AI portfolio that integrates the technology across academic programs, research, and student development. Beginning in Fall 2026, Syracuse will offer new bachelor's and master's degrees in AI, seven interdisciplinary AI minors, and expanded undergraduate research opportunities. A peer-led AI bootcamp gives students hands-on experience with real tools and workflows from their first days on campus - before they even choose a major. The bootcamp also offers stackable micro-credentials and project-based learning tied to research and industry partnerships.
The shift reflects a broader rethinking of AI for Education, where technical skills are paired with domain expertise rather than taught in isolation. This approach reaches a wider student population, producing graduates who combine field expertise - public health or urban planning, for instance - with practical AI skills.
Scaling across state systems
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 its Good Systems initiative, a campus-wide research effort focused on human-centered, socially responsible AI for challenges such as misinformation, smart-city design, and public-interest technology. Texas A&M University incorporates AI across engineering, agriculture, health care, and manufacturing research, aligning training with the state's major industries.
Together, these institutions create a distributed, discipline-specific model. AI is not treated as a niche specialty but as a tool embedded within the industries where graduates will work.
Why this matters for education professionals
Faculty and administrators designing curricula should take a hard look at whether their AI offerings reach beyond computer science departments. The programs gaining traction do not silo AI into a single degree. They embed it across existing majors and pair it with domain knowledge. That means education professionals in fields like nursing, business, public policy, and the humanities will need to develop their own AI fluency - and reshape courses so students learn to apply AI within their chosen professions. Policymakers can support this shift through competitive grants for interdisciplinary AI curriculum development, industry partnerships, and expanded access to stackable microcredentials that serve both students and incumbent workers throughout their careers.
Your membership also unlocks:
