Artificial intelligence is changing almost everything. But there's a big difference between using AI and understanding how to build it. 

American University's new Master of Science in Artificial Intelligence is designed to bridge that gap. 

The 30-credit program will prepare students not just to work with today's AI tools, but to develop the systems behind them—and make those systems more reliable, responsible, and effective. Combining mathematics, statistics, and computer science, the degree provides a rigorous technical foundation for careers in AI and advanced research. 

“Our students learn the mathematics, statistics, and computational principles that make AI work,” says Zois Boukouvalas, Professor of Mathematics and Statistics and Inaugural Director of the program. “That foundation enables them not only to build intelligent systems, but to critically evaluate, improve, and responsibly shape the future of AI.” 

As AI advances, the program reflects AU's commitment to preparing students who can both develop new technologies and critically assess their impact. 

“AI is advancing faster than almost any technology we've seen, and universities have a responsibility to prepare people who understand both its possibilities and its limitations,” says Linda Aldoory, dean of AU's College of Arts and Sciences. “At the College, we're bringing rigorous technical training together with the critical thinking and ethical considerations needed to understand the impact of AI in society.” 

Building AI from the Ground Up 

The program is designed for students who want to move beyond using AI tools to understanding, developing, and evaluating the technology itself, says Boukouvalas. “It welcomes aspiring AI engineers and researchers, as well as graduates from mathematics, statistics, computer science, data science, engineering, and related quantitative or interdisciplinary fields who want to deepen their expertise in AI,” he explains. “Students with different academic backgrounds may be considered, provided they have the necessary mathematical and programming preparation.” 

Students will explore technologies including generative AI, large language models, deep learning, and high-performance computing. 

They can choose between two specialized pathways: Mathematical Foundations of AI, which emphasizes the mathematics and statistics behind modern AI systems, and AI Methods and Advanced Applications, which focuses on advanced techniques and emerging applications. 

Students complete the program with a six-credit capstone experience that combines an advanced elective with a research thesis, applied AI project, or approved internship, allowing them to connect their technical training with their academic and professional interests. 

Making AI More Responsible 

Students won't just learn how to build AI systems. They'll learn how to test them, identify their weaknesses, and make them more reliable. 

That means examining how AI systems perform, identifying potential biases and limitations, evaluating their reliability and robustness, and understanding uncertainty in their predictions. “Responsible AI is integrated throughout the program, encouraging students to consider not only technical performance but also the ethical, social, and environmental implications of developing and deploying AI systems,” says Boukouvalas. 

He and fellow Computer Science Professor Nathalie Japkowicz are coauthors of Machine Learning Evaluation: Towards Reliable and Responsible AI, published by Cambridge University Press. Boukouvalas's research focuses on reliable and responsible AI, including how systems can combine information from multiple sources to tackle complex problems in healthcare, natural language processing, and the physical sciences. 

The program draws on a broader community of faculty advancing AI research across disciplines. Michael Robinson, Mathematics and Statistics Professor and Department Chair, investigates the mathematical foundations of large language models, including research on the geometry of token embeddings accepted at NeurIPS 2026. Leah Ding, Provost Associate Professor of Computer Science, develops trustworthy AI systems for scientific discovery, including NSF-funded research using AI and machine learning to improve wildfire detection and forecasting. Computer Science Professor Roberto Corizzo develops adaptive machine learning methods for evolving data, with applications spanning healthcare, cybersecurity, finance, and scientific discovery. 

An AI Education in Washington, DC 

According to the World Economic Forum's Future of Jobs Report 2025, AI and machine learning specialists are among the fastest-growing occupations worldwide through 2030. 

AU's new degree prepares students for careers ranging from AI and machine learning engineering to data science, scientific research, and responsible AI policy, as well as for doctoral study. 

For students interested in pursuing advanced research, AU also offers a PhD in Computational Science and Artificial Intelligence. The interdisciplinary doctoral program brings together computer science, physics, mathematics, and statistics, preparing students to use AI and advanced computational methods to tackle complex scientific problems through faculty-mentored research. 

Washington, DC, offers another advantage. With federal agencies, research laboratories, technology companies, nonprofits, and policy organizations nearby, students have opportunities to connect their classroom learning with research, internships, and careers in a rapidly evolving field. 

Students can enroll full or part time, with fall and spring admission. All applicants are automatically considered for merit scholarships. 

Learn more: MS in Artificial Intelligence