Morgan State won Maryland's first public AI degree in months by building it first
Morgan State University launched Maryland's first public AI bachelor's degree by converting an existing cloud computing program into a full AI curriculum, completing the regulatory approval in months rather than years. The program prioritizes foundational computer science alongside AI-specific coursework and hands-on reinforcement learning projects, positioning graduates to design and extend AI models rather than simply use them.
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Key takeaways
Morgan State built 18 AI courses and embedded 8 completed projects into the curriculum before formally proposing the degree, enabling regulatory approval in days once evidence of the running program was presented.
Every student learns classical programming and data structures first, then progresses through problem-based courses scaled by difficulty level, culminating in a capstone with no lectures where the instructor acts as support.
The program emphasizes reasoning grounded in reinforcement learning and the Markov decision process rather than statistical analysis alone, preparing students to extend AI models and control technology instead of being directed by it.
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New degree programs at public universities typically take a year or two to move from proposal to approval. Morgan State University's Bachelor of Science in Artificial Intelligence, the first at a public university in Maryland, took months. It moved that fast because the department had already built the program under a different name and was asking the state to recognize what was running.
Paul Wang, chair of computer science at Morgan State and technical director of the university's CEAMLS AI Center, traces the story back to a cloud computing program the department started in 2020. Within two or three years, he says, the case for a standalone cloud degree had weakened. Cloud infrastructure had become stable and ubiquitous, something he describes as a utility. Few students needed to learn how to build it, while nearly everything running on it, from robotics to model training, had become AI work. So the department began shifting the program's substance without waiting for a new label.
That shift was not incremental. The faculty developed 18 AI courses, far more than the roughly ten Wang says would normally constitute a major, and embedded eight completed AI projects into the curriculum. By the time the department proposed formally converting the degree, the cloud computing program was an AI program in everything but name.
Data, not a pitch, carried the approval
The proposal met objections inside the university, with colleagues questioning whether it could pass the Maryland Higher Education Commission. Wang says he treated the pushback as useful. Rather than argue the merits of a future plan, the department presented what already existed: the course list, the finished projects, and graduate outcomes, including students hired into AI model work at companies such as Microsoft, Google and Meta. "We are not a sounds good," Wang said. "We are more aligned with the program running now." Convincing MHEC, he says, took days once the evidence was on the table.
Wang is explicit that other institutions could take the same route, and he offered to share Morgan State's AI courses with any university or community college in Maryland or beyond that needs them. Building courses, he notes, takes money, expertise and time, and the department has already spent all three.
What the degree teaches, and what it refuses to skip
The fast approval does not mean a thin curriculum. Every student still learns programming and data structures the classical way before moving on. "Not, 'AI can do it for you. We don't learn.' No," Wang said. "You have to learn this structure, programming, all the classical parts, build a foundation, then you can do better." After that, most courses start from a problem rather than an assignment, with the same problem scaled from basic functions and libraries for lower-level students to open-ended critical thinking for advanced ones. Lectures dominate early. By the capstone, Wang says he gives none; he hands students a problem and acts as support.
Wang also draws a line between what he considers real AI and what much of the field practices. Most work, in his view, is statistical analysis: pulling data, running functions, producing a result. The program instead pushes students toward reasoning, grounded in reinforcement learning and the Markov decision process mathematics underneath it. That focus shows up in the department's two AI labs, where students work with humanoid robots and robotic dogs on reinforcement learning tasks, have built a CBT-based counseling model that students under stress can use, and train models to surface threats in cybersecurity data too voluminous for a human analyst to review efficiently.
The stakes for students, in Wang's telling, come down to position in the labor market. He acknowledges AI will hit entry-level programming jobs, and he does not soften it. His answer is that students must be more than users of tools like ChatGPT: they need to understand models well enough to extend them, and to control the technology rather than be directed by it. That mindset, and the willingness to convert an existing program rather than wait for a new one, is what Morgan State is betting will define the first graduates of Maryland's first public AI degree.
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About the author
With 20 years of experience at the intersection of higher education and edtech, Darin Francis brings a wealth of knowledge and a deep passion for driving meaningful change in the sector. Having led teams, crafted go-to-market (GTM) strategies, and worked closely with institutions, Darin is uniquely positioned to help edtech companies navigate the complexities of U.S. and Canadian higher education. Darin Francis, based in Detroit, MI, US, is currently a Managing Partner and CEO at Harbinger Lane Consulting.