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Artificial Intelligence Masters

Launching in Fall 2025, Master of Science in AI

#1 in Virginia
Public university to offer a stand-alone MS in Artificial Intelligence

Become an AI Innovator

Enroll in the Master of Science (MS) in Artificial Intelligence, a cutting-edge graduate program in the College of Engineering and Computing. Designed to equip the next generation of AI innovators, this program offers a solid, interdisciplinary education that blends foundational theory with real-world application, preparing graduates to address complex challenges in industry, government, and beyond.

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Tailor your learning with a specialized focus in four areas

AI Policy, Ethics, and Society

Learn about the intersection of technology, policy, and ethics. The topic area focuses on the societal impacts and governance challenges of emerging technologies using artificial intelligence in contexts such as economics and policymaking.

Advanced AI

Discover specializes in artificial intelligence techniques and applications specific to a domain of artificial intelligence. It focuses on human-faced interactive tools, building artificial intelligence models, and language structure.

Scalable and Secure AI Infrastructures

Learn about secure and scalable implementations of artificial intelligence technologies across computing environments and platforms. Focus on protecting against potential threats and adapting to increasing computational demands.

User-Inspired AI

Explore the practical applications of artificial intelligence technologies. Obtain specialized knowledge of artificial intelligence in areas such as interpretable machine learning in decision-making, cybersecurity, and health.

Topic Areas

Students must select at least one course from each of the following four topic areas, with advisor approval:

1. Artificial Intelligence Policy, Ethics, and Society Sample courses:

  • AIT 679: Law and Ethics of Big Data
  • BIOD 760: National Security Technology and Policy
  • GCP 604: New Technologies in the Global Economy
  • ME 575: AI Design and Deployment Risks

2. Advanced Artificial Intelligence Sample courses:

  • AIT 616: Interactive Machine Learning and Artificial Intelligence
  • AIT 726: Natural Language Processing with Deep Learning
  • OR 664: Bayesian Artificial Intelligence
  • OR 774: Reinforcement Learning

3. Scalable and Secure Artificial Intelligence Infrastructures Sample courses:

  • AIT 670: Cloud Computing Security
  • AIT 687: IoT and Edge Systems
  • ECE 554: Machine Learning for Embedded Systems
  • CS 695/SWE 699: AI Safety and Assurance

4. Use-inspired Artificial Intelligence Sample courses:

  • AIT 636: Interpretable Machine Learning
  • CYSE 689: Artificial Intelligence Methods for Cybersecurity
  • STAT 646: Probabilistic Machine Learning
  • HAP 774: Artificial Intelligence in Health

*These courses are subject to change

Program Highlights

Expert Faculty

Led by the College of Engineering and Computing at George Mason University, this program brings together world-class faculty with extensive experience in AI research and application. Our instructors include professors from Computer Science, Statistics, Operations Research, Cyber Security, Public Policy who have over many years of combined teaching and research experience in AI and related fields. Many have published extensively in peer-reviewed journals specific to their disciplinary areas and AI, and serve as leaders in national organizations focused on artificial intelligence.

The program draws on interdisciplinary expertise from multiple George Mason colleges, including the School of Computing, the College of Humanities and Social Sciences, the Schar School of Policy and Government, and the Donald G. Costello College of Business. This collaborative approach ensures students benefit from diverse perspectives on AI’s technical foundations, business applications, and societal impacts.

Faculty members like Dr. Jana Kosecka specialize in computer vision and machine learning, while Dr. Zhengdao Wang focuses on intersection of AI and cybersecurity. The program also features experts in generative AI, differential privacy, and statistical machine learning like Dr. Anand Vidyashankar and Dr. Daniel Barbara, and researchers in Bayesian statistics and deep learning like Dr. Vadim Sokolov.

Industry Connections

George Mason University’s strategic location in Northern Virginia-a hub for technology innovation and government agencies-provides unparalleled opportunities for industry engagement. The College of Engineering and Computing maintains robust industry partnerships that support student success through:

  • Research collaborations with faculty on cutting-edge AI problems
  • Access to industry mentors and guest speakers
  • Opportunities for internships and job placement
  • Collaborative problem-solving through Engineering Artificial Intelligence course projects

Recently, George Mason was awarded a $1 million grant to establish the nation’s first Center for AI Innovation for Economic Competitiveness (CAIIEC), focused on helping small and medium enterprises adopt AI technologies. The university has also launched an AI-in-Gov Faculty Fellows Program to advance responsible, impactful use of AI in the public sector.

These initiatives, along with George Mason's Industry Partner Program, create a vibrant ecosystem where students can connect with potential employers and gain real-world experience implementing AI solutions. This network provides graduates not only with technical skills but also with professional relationships that accelerate their careers in the rapidly evolving field of artificial intelligence.

By joining George Mason’s Master of Science in Artificial Intelligence program, you’ll become part of a community dedicated to addressing the AI skills gap while developing the next generation of ethical, innovative AI leaders.

Frequently Asked Questions

In this an in-person program?

For the 2025-2026 academic year, the program will be offered in-person. The program may be offered online in the future. Although some of the courses may be offered online, the program is not designed to be fully online.

Is there is a part-time option?

The program is designed to be completed in 1.5-2 years, if you take 3-4 courses per semester (full-time). Students can take courses at their own pace, but the program is not designed to be fully self-paced. Note, that some of the courses are offered only once a year

I work full-time, can I do this program?

Yes, the program is designed to accommodate working professionals. The courses are offered in the evenings (4:30-7:10pm and 7:20-10pm), allowing you to balance your work and study commitments.

What is the cost of the program?

The tuition and fees for the program are subject to change. For the most up-to-date information, please visit the George Mason University tuition and fees page.

What is the application process?

The application process for the program is managed by the George Mason University Office of Admissions. For detailed information on how to apply, please visit the George Mason University admissions page or the MS in AI admissions page.

My background in Math, Statistics and CS is weak, will I be able to succeed in this program?

The program is designed to accommodate students with diverse backgrounds. However, some foundation in mathematics, statistics, and computer science will be required for your success in the program. Prior to starting the program, each student will go through the assessment process to determine their readiness for the program. Think of this as an informal math-placement test. The assessment will include a review of your knowledge in mathematics, statistics, and computer science, including programming languages (Python, R, etc.), data structures, algorithms, and linear algebra. Based on the results, we will recommend to take additional non-credit and free short courses or complete self-study materials to strengthen your foundation in these areas. Further, every student will be taking the foundations in AI course (AII 600) in the first semester, which will cover the foundations of AI and machine learning. This course is designed to help students build a strong foundation in AI and machine learning concepts, regardless of their prior experience.

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