The 5 Best AI Masters Degrees in Virginia

Virginia earns its reputation as the “Silicon Valley of the East” faster than ever, with over 300,000 tech professionals working in the state. This growth has created a need for artificial intelligence graduate programs, especially in Northern Virginia’s tech corridor. You can find diverse options whether you’re seeking an online masters in AI or a campus-based program. This list gets into five AI masters degrees in Virginia. We cover curriculum details, admission requirements and tuition costs that help you make a decision about career prospects.

1. George Mason University – Master of Science in Artificial Intelligence

Program Overview and Curriculum

George Mason University launched Virginia’s first public university standalone Master of Science in Artificial Intelligence in fall 2025, following approval from the State Council of Higher Education for Virginia. This AI masters degree program in Virginia requires 30 credit hours, split between 18 credits of required core coursework and 12 credits of electives hosted across four thematic tracks.

The curriculum spans three core domains within AI. Machine learning foundations enable systems to improve from data. Planning and decision-making algorithms support AI-driven systems that navigate complex environments. Deep learning techniques power applications such as image recognition, natural language processing and generative AI models. Core courses have machine learning foundations and practice, planning and decision-making for intelligent agents, and deep learning fundamentals.

Students complete the entire AI operations pipeline, from model development to deployment across embedded systems, edge computing and cloud platforms. The program emphasizes assessing AI risks across computing platforms to ensure secure and responsible applications. You’ll develop expertise in training and fine-tuning AI models for optimized system performance while managing the full machine learning operations lifecycle.

The program is designed for completion in 1.5 to 2 years for full-time students taking 3-4 courses per semester. Courses are offered in the evenings at 4:30-7:10pm and 7:20-10pm. This allows working professionals to balance career and study commitments. Some courses are offered only once per year, so students need to plan their course schedules.

Specialization Areas and Topic Focus

Students must select at least one course from each of four specialized tracks, with advisor approval. The AI Policies, Ethics, and Society track has courses such as Law and Ethics of Big Data, National Security Technology and Policy, and AI Design and Deployment Risks. This track ensures graduates understand the ethical and societal considerations of AI technologies.

The Advanced Artificial Intelligence track features Interactive Machine Learning and Artificial Intelligence, Natural Language Processing with Deep Learning, Bayesian Artificial Intelligence, and Reinforcement Learning. Students in the Scalable and Secure AI Infrastructures track study Cloud Computing Security, IoT and Edge Systems, Machine Learning for Embedded Systems, and AI Safety and Assurance.

The Use-inspired Artificial Intelligence track offers Interpretable Machine Learning, Artificial Intelligence Methods for Cybersecurity, Probabilistic Machine Learning, and Artificial Intelligence in Health. This interdisciplinary approach blends foundational theory with ground application.

Each student begins with the foundations in the AI course (AII 600) in the first semester, which covers fundamental AI and machine learning concepts whatever prior experience. An assessment process determines student readiness for the program and reviews knowledge in mathematics, statistics, computer science, programming languages, data structures, algorithms and linear algebra. Students may take additional non-credit short courses or complete self-study materials based on results.

Faculty Expertise and Research

The program is led by Amarda Shehu, the university’s inaugural vice president and chief AI officer, associate dean for research in the College of Engineering and Computing, and a professor in the computer science department. Faculty members bring extensive experience in AI research and applications. Many publish in peer-reviewed journals and serve as leaders in national AI organizations.

The interdisciplinary teaching team draws from Computer Science, Statistics, Operations Research, Cyber Security and Public Policy departments. Dr. Jana Kosecka specializes in computer vision and machine learning, while Dr. Zhengdao Wang focuses on the intersection of AI and cybersecurity. Faculty expertise has generative AI, differential privacy and statistical machine learning through researchers like Dr. Anand Vidyashankar and Dr. Daniel Barbara, plus specialists in Bayesian statistics and deep learning like Dr. Vadim Sokolov.

Research collaborations extend across multiple domains. Keren Zhou, an assistant professor in the Department of Computer Science, maintains a partnership with NVIDIA spanning more than a decade. Zhou received access to NVIDIA’s DGX B200 platform equipped with eight advanced graphics processing units recently. This enables students to experiment with large-scale models and cutting-edge infrastructure. His work focuses on training large models that generate efficient code for AI systems.

The Information Sciences and Technology department conducts research across natural language processing, computer vision, cognitive assistants and human-in-the-loop machine learning. Faculty advances foundational work in deep neural networks, ensemble learning, multimodal AI, semantic networks and knowledge graphs, and transfer learning.

Admission Requirements

Admission is competitive. Strong candidates will have a BS in an engineering or quantitative discipline. Specific application requirements and deadlines can be found through the College of Engineering and Computing application portal.

Tuition and Financial Information

Graduate tuition costs are based on 9 credits per semester. For the 2026-27 academic year, the rates are:

Books cost about $1,380 per year. Graduate students pay a one-time new student fee of $60. Financial aid in the form of graduate assistantships may be available for full-time degree-seeking students. Students can complete the Free Application for Federal Student Aid (FAFSA) using George Mason University’s code 003749.

Career Outcomes and Industry Connections

George Mason graduates from the Class of 2025 reported a 92% positive outcome rate within six months of graduation. The median starting salary reached $73,000, with 81% remaining in the Washington, D.C., region and 74% hired in positions that match their career goals. Half of graduates remained hired with the same organization they worked for while students.

Northern Virginia’s strategic location provides exceptional opportunities for industry engagement. The College of Engineering and Computing maintains research collaborations with major companies like Amazon and General Dynamics Information Technology, government entities like NIST and the FDA, and numerous tech firms and startups. These partnerships provide research funding, support joint projects and create opportunities through internships and problem-solving initiatives.

George Mason was awarded a $1 million grant to establish the nation’s first Center for AI Innovation for Economic Competitiveness. The center focuses on helping small and medium enterprises adopt AI technologies. The university launched an AI-in-Gov Faculty Fellows Program to advance responsible AI use in the public sector.

General Dynamics Information Technology expanded its partnership with George Mason to create greater access to career development opportunities for students in cybersecurity, artificial intelligence, cloud and software development. Large employers hiring George Mason graduates have the Department of Defense, Fairfax County Public Schools, Inova Health System, Amazon Web Services, Capital One, Google, IBM, Deloitte, Booz Allen Hamilton and Northrop Grumman.

Career Services offers one-on-one advising, resume and interview preparation, internship and job search tools, career fairs and industry-specific events. The Industry Partner Program connects companies with students and faculty through recognition, event access and academic engagement.

2. University of Mary Washington – MS in Artificial Intelligence in Business

Program Overview and Curriculum

The University of Mary Washington opened enrollment for Virginia’s first Master of Science in Artificial Intelligence in Business after receiving approval from the State Council of Higher Education for Virginia in May. This STEM-designated AI masters degree program Virginia distinguishes itself by merging advanced AI technologies with business leadership, strategy, ethics and governance.

The fully online, asynchronous format provides flexibility for working professionals and recent graduates who balance career, family and education commitments. Students complete 30 credit hours through a combination of core courses, a capstone project and electives. Full-time students can finish the program in one year, while part-time enrollment takes two years.

The curriculum structure has seven required courses totaling 21 credits, followed by nine elective credits selected from GBUS courses numbered 506 or above.

Coursework emphasizes applied AI through business functions such as marketing, operations, finance, strategy and entrepreneurship. Students work with machine learning techniques, statistical modeling and analytical decision-making while developing skills to translate AI-driven insights into actionable business strategies. The program combines hands-on learning through industry collaborations, real-life case studies and applied projects.

Rooted in UMW’s liberal arts tradition, the curriculum merges communication, ethical reasoning and critical thinking with technical expertise. This interdisciplinary approach prepares graduates to guide complex societal and organizational implications of emerging technologies. Students develop the knowledge and skills needed to review AI opportunities, lead organizational change and bridge the gap between technical teams and executive decision-makers.

Specialization Areas and Topic Focus

The program covers key areas that have predictive analytics, language models, reinforcement learning and AI-driven business intelligence. Students become skilled at artificial intelligence techniques and business analytics methodologies while learning to lead and manage AI-driven initiatives within organizations.

The curriculum addresses five core student learning outcomes. Graduates demonstrate mastery of AI techniques and business analytics methodologies, lead and manage AI-driven initiatives within organizations, review ethical implications of AI applications in business contexts and communicate analytical findings to diverse stakeholders with clarity.

The program emphasizes practical applications of machine learning, data analytics and ethical AI in industries of all types, from finance and marketing to healthcare and consulting. Students learn to redesign workflows, manage organizational change, communicate across disciplines and ensure responsible and ethical use of AI in complex environments.

Faculty Expertise and Research

The MS in AI in Business program faculty has Professors Julius N. Esunge, Christopher J. Garcia and Mukesh Srivastava, along with Assistant Professors Prashant Chandrasekar and Evan C. Coleman. Professor of Communication and Digital Studies Anand Rao leads the Center for AI and the Liberal Arts.

The Center for AI and the Liberal Arts (CAILA) launched in fall 2025. It brings together students and faculty from computer science, humanities, social sciences and the arts to explore what AI can do and what it should do. The center merges AI with UMW’s liberal arts curriculum so students develop technical fluency alongside skills to examine AI’s ethical, cultural and societal dimensions.

Faculty receive research grants, AI teaching workshops, access to guest lecturers and support for merging AI tools into any discipline. Through partnerships with COPLAC institutions and other universities in Virginia, UMW shares innovative AI integration strategies, best practices and collaborative research opportunities.

CAILA provides students with coursework, funded research projects, mentorship from visiting experts and an annual AI pitch contest. The center positions UMW as a national leader among public liberal arts universities in addressing the challenges and opportunities of AI.

Admission Requirements

Admission requires completion of a baccalaureate degree from a regionally-accredited college or university. The application process has a completed application form and official transcripts showing degree completion. There is no application fee.

Students can transfer a maximum of six graduate credits into the program. Courses must come from a regionally accredited institution within the last six years with a minimum grade of B and must directly relate to one of the MS program courses to qualify for transfer credit. Transfer credit is not given for internship or practicum experiences.

All course work must be completed within six years of matriculation into the program. Students must complete all required core and elective courses with a cumulative grade-point average of 3.0 or higher to meet degree requirements.

Tuition and Financial Information

The MS in AI program follows UMW’s graduate tuition and fees schedule. UMW charges $15,304 for in-state students and $28,810 for out-of-state students for undergraduate programs.

The University of Mary Washington offers financial assistance to graduate students enrolled at least half-time in degree programs. Half-time enrollment for graduate students is five graduate level credit hours per semester. Students interested in applying for financial aid must file the Free Application for Federal Student Aid (FAFSA).

Graduate students qualify for Federal Direct Graduate Loans and may access graduate departmental scholarships. The Office of Financial Aid determines eligibility after acceptance into the program. Awarding of financial aid for the Fall term begins in April and in November for the Spring term.

Career Outcomes and Industry Connections

Graduates leave the program able to bridge technical teams and broader business, policy and social questions that employers prioritize more and more. Students are prepared for leadership roles in an AI-driven economy in a variety of industries.

UMW’s College of Business has a partnership with the University of Virginia that accelerates the graduate’s path to a master’s degree in accounting from the McIntire School of Commerce. Preferred candidates require a 3.0 or higher GPA and grades of B or better in all prerequisite coursework. The partnership waives the $85 application fee and offers a minimum guaranteed scholarship of $10,000 to qualified candidates who apply before January 5.

Students can begin their studies for the MS in AI in Business online in fall 2026. The program offers a distinctive degree option for professionals who seek advancement in a faster evolving technology landscape in Virginia and the D.C. region.

3. Regent University – MS in Cybersecurity with AI Concentration

Program Overview and Curriculum

Regent University offers a Master of Science in Cybersecurity with an Artificial Intelligence concentration that addresses the intersection of two fields evolving faster than ever. This AI masters degree program Virginia integrates state-of-the-art AI technologies into cybersecurity strategies and explores how artificial intelligence reshapes the cybersecurity scene. The concentration covers development, implementation and maintenance of AI-powered defense systems and addresses emerging threats unique to AI technologies.

The program targets technical professionals seeking to deepen expertise in both cybersecurity and artificial intelligence. It combines hands-on training in AI algorithms and tools with security planning. The online format accommodates both on-campus and distance learners, with access to Regent’s state-of-the-art, multimillion-dollar cyber range located in Virginia Beach. The 30-credit program follows Regent’s broader MS in Cybersecurity structure and allows students to specialize in AI applications within cybersecurity contexts.

The cyber range serves as a cybersecurity simulation and training facility that prepares students for ground scenarios. This 3,000-square-foot facility accommodates 20 people hosted into four teams of five individuals. Each team member assumes different roles during training simulations, including security information and management, event log monitoring, firewall operations and endpoint-level malware investigation. The range generates complex attacks that require entire team efforts to block threats and protect networks.

The program presents technical knowledge and skills from a Christian worldview and anchors them on Christian principles and values. Faculty members combine advanced degrees with ground experience in cybersecurity and artificial intelligence and bring state-of-the-art industry knowledge to instruction. Students receive mentorship from faculty who hold the highest degrees in their field.

Specialization Areas and Topic Focus

The AI concentration focuses on five core competency areas. Students learn to understand and implement AI algorithms for threat detection, anomaly identification and predictive security analytics. The curriculum teaches use of AI technologies and tools to boost security operations, automate incident response and strengthen defense capabilities. Students master automation of cybersecurity operations with AI-powered solutions for continuous monitoring, threat hunting and vulnerability assessment.

The program addresses ethical implications and regulatory requirements for AI deployment in security contexts. Students develop skills to integrate machine learning models into existing security infrastructure and maintain system integrity. The curriculum enables students to architect and implement AI systems with built-in security from the ground up and address vulnerabilities unique to machine learning models and AI-powered applications.

Students sharpen skills through hands-on training available to both on-campus and online students. The cyber range, built with Cyberbit technology, delivers cybersecurity simulations as close to real attacks as possible, according to university leadership. Regent University and Cyberbit developed attack scenarios for the range, with plans to add enhanced scenarios as cyber threats evolve and become more sophisticated.

Faculty Expertise and Research

Faculty members bring combinations of advanced degrees and practical cybersecurity experience to the classroom. The program benefits from Regent’s designation as a National Center of Academic Excellence in Cyber Defense (CAE-CD) for the B.S. in Cybersecurity by the National Security Agency (NSA). Only four percent of the 4,000 institutions nationwide have received this merit.

Regent’s Institute for Cybersecurity prepares Christian technology and cybersecurity leaders through exceptional ground relevant training and instruction in ethical responsibilities. The institute involves students in multiple degree programs beyond engineering and computer science. Faculty expertise extends to addressing over 700,000 unfilled cybersecurity positions in the nation, with many professionals in the field lacking required expertise for the dynamic threat environment.

Admission Requirements

The application process follows three steps. Applicants submit applications using the Regent University Online Application. Students then submit unofficial transcripts to regent.edu/items. Students receive an email requesting authorization for Regent University to get official transcripts from U.S. degree-granting institutions upon application submission. International transcripts require evaluation by NACES or NAFSA approved agencies.

The final step requires submission of a government-issued ID to ensure academic integrity. Students submit scanned copies or photographs to regent.edu/items. The admissions office provides reasonable accommodations for applicants unable to complete applications due to disabilities, available by contacting 757.352.4990 or admissions@regent.edu.

Tuition and Financial Information

Tuition costs $695.00 per credit hour for the 2026-27 academic year. The financial structure is different for part-time and full-time enrollment:

Online students pay a University Services Fee of $750.00. All students check in for the semester two weeks before the session start date. Students should apply, gain acceptance, enroll in first courses and confirm payment plans prior to this date.

Regent’s annual tuition runs $24,000.00 less than other private institutions. The university ranks among the top five percent of most affordable private Christian colleges and universities for bachelor’s programs. Students qualify for generous scholarships and financial aid.

Career Outcomes and Industry Connections

Graduates gain skills to design secure AI systems, automate threat detection and pursue roles as AI security engineers or architects. Career opportunities include AI Cybersecurity Specialist, Machine Learning Engineer focused on security applications, Security Analyst positions, AI Engineer roles and Risk and Ethics Specialist positions in organizations deploying AI for security purposes.

The program prepares students for positions in network and systems administration, digital forensics engineering, digital security project management, information technology assurance, computer programming, cyber risk and strategy analysis and cyber intelligence. The median annual wage for network and computer systems administrators provides strong earning potential in these fields.

U.S. News & World Report (2026) ranked Regent among Top National Universities. The university received recognition as a Military Friendly Top 5 School by Military Friendly for 2024-25 and ranked among the Top 10 Best for Vets Colleges: Online & Nontraditional by Military Times in 2020. These recognitions reflect strong outcomes for veterans and active service members pursuing artificial intelligence graduate programs.

4. Old Dominion University – Online Masters in AI and Machine Learning

Program Overview and Curriculum

Old Dominion University delivers a Master of Science in Data Science and Analytics with a concentration in Artificial Intelligence and Machine Learning through both on-campus and online formats. This AI masters degree program in Virginia requires 30 credit hours and gives students a foundation to use state-of-the-art programming tools and software packages to develop machine learning models. The program addresses relevant data analytics topics such as text analytics, visualization, and algorithms and data structures.

Students learn computational data analysis, data visualization, and natural language processing while selecting four courses in consultation with a faculty advisor. The curriculum structure has 15 credits of Data Science and Analytics core courses, nine credits of concentration electives, and either a three-credit capstone project plus one elective or a six-credit thesis. Core courses cover Introduction to Data Science and Analytics, Data Analytics and Big Data, Data Visualization, Probability Models for Data Science and Analytics, and Advanced Statistical Concepts in Data Science.

Specialization Areas and Topic Focus

The concentration focuses on design and application of machine learning models, neural networks, and AI systems. Students develop competency in deep learning frameworks and apply AI techniques to large-scale data problems in industry and research settings. Concentration electives have Programming for Data Science, Generative AI, Introduction to Machine Learning, Machine Learning, Natural Language Processing, Introduction to Artificial Intelligence, Computer Vision, and Information Retrieval.

You complete either a capstone project tied to your specialization or a thesis research sequence. The capstone allows you to apply learning to a ground problem or scenario in your focus area. You gain experience with the type of work you’ll do as a data professional.

Faculty Expertise and Research

ODU launched MonarchSphere powered by Google Cloud, a first-of-its-kind AI incubator for higher education. This partnership positions the university as a national leader in AI and a model for how universities can prepare students, give faculty technological tools, and support communities for a digital future. The university is recruiting 25 faculty members with AI expertise in clusters aligned with institutional strengths that have Accelerator Science, Engineering, and Machine Learning, and Trustworthy Artificial Intelligence.

Faculty pilot Google Colab Enterprise in advanced AI courses and give students access to GPUs powerful enough to train deep learning models. Genomic AI research training that previously took 24-40 days on on-premise clusters can be completed in 2-3 days using Google Cloud, a 12-16x improvement.

Admission Requirements

Admission requires a baccalaureate degree in computer science, electrical and/or computer engineering, mathematics, statistics, information system and technology, or a related field from a regionally-accredited institution. Students holding a bachelor’s degree in an unrelated field need competency in topics related to simple statistics and computer science. International applicants must submit TOEFL scores of at least 230 on the computer-based test or 79 on the TOEFL iBT, or IELTS 6.5 overall. Students can transfer a maximum of 12 elective graduate credit hours from previously completed work at regionally-accredited institutions if the admission committee approves.

Tuition and Financial Information

The standard tuition rate for online graduate students without Virginia domicile is $1,361 per credit hour for the 2026-2027 academic year. Online students pay a mandatory auxiliary tuition of $162 per credit hour, a course technology fee of $30 per credit hour, and an innovation fee of $8 per credit hour. The general services fee is $18 per 16-week term, and the ID card fee is $4 per 16-week term.

Career Outcomes and Industry Connections

ODU is an early member of the Google AI for Education Accelerator and integrates AI learning opportunities in curriculum, faculty development, and workforce programs. The collaboration introduces Google Career Certificates to degree programs and ensures graduates earn both a degree and industry-recognized credentials. ODU maintains transformative partnerships with industry, government, education, and nonprofit organizations that provide direct access to emerging talent and trailblazing solutions.

5. Virginia Tech – Artificial Intelligence Graduate Programs

Program Overview and Curriculum

Virginia Tech’s Institute for Advanced Computing in Alexandria hosts Master of Engineering programs in Computer Engineering and Computer Science and Applications, designed for completion in one year. The curriculum emphasizes quantum computing, machine learning, and chip design. Virginia Tech offers an AI & Machine Learning Bootcamp spanning 23 weeks part-time, covering advanced machine learning, deep learning, NLP, GenAI, Agentic AI, and MLOps using 25+ real-life tools. This comes in addition to degree programs.

Specialization Areas and Topic Focus

The bootcamp curriculum addresses Python programming, supervised and unsupervised learning, deep neural networks, MLOps principles, NLP models, generative AI with LLMs, and agentic AI systems with multi-agent workflows. Graduate programs cover AI/ML, wireless technology, quantum information science, and intelligent interfaces.

Faculty Expertise and Research

The Sanghani Center for Artificial Intelligence and Data Analytics houses 23 academic and research faculty and over 120 full-time graduate students. Research focuses on network analysis, explainable AI, natural language processing, adversarial AI, and human-AI collaboration. Virginia Tech maintains the Amazon-Virginia Tech Initiative for Efficient and Robust Machine Learning, providing doctoral fellowships and industry internships.

Admission Requirements

Applicants need baccalaureate degrees in computer science, engineering, mathematics, or related quantitative fields from regionally accredited institutions.

Tuition and Financial Information

Graduate tuition for Alexandria campus residents costs $17,959 per year, with non-residents paying $34,915. The bootcamp ranges from $4,995 to $5,995 after institutional discounts.

Career Outcomes and Industry Connections

AI/Machine Learning Engineers in Virginia earn an average salary of $159,795. Virginia Tech ranks 31st for graduate engineering programs at the national level.

Next Steps

Virginia’s position as an emerging AI hub makes it an ideal location to pursue your artificial intelligence graduate education. Each of the five programs offers distinct advantages: George Mason provides Virginia’s first standalone public AI master’s, UMW blends AI with business strategy, Regent focuses on cybersecurity applications, ODU emphasizes data science integration, and Virginia Tech delivers intensive one-year engineering programs.

Your choice ends up depending on your career goals and learning priorities. Whether you need flexible online learning or prefer campus-based networking opportunities, Virginia’s program offerings ensure you’ll find an AI master’s degree program that fits your professional aspirations and schedule requirements.