The Master of Science in Computer and Information Science (MCIS) with an Artificial Intelligence at Southern Arkansas University is a specialized graduate program that prepares students to design, develop, evaluate, and deploy intelligent computing systems.
The program combines a strong foundation in programming, modern database systems, with advanced coursework in machine learning, artificial intelligence principles, generative AI, and agentic AI.
Students learn to apply artificial intelligence across fields such as healthcare, business, engineering, education, the life sciences, and the social sciences. The option is designed for computer science graduates seeking advanced AI expertise and for professionals who want to integrate AI and data-driven technologies into their disciplines and organizations.
Program Format & Flexibility
The MCIS program offers a flexible, student-centered learning environment designed for full-time students and working professionals.
- Online: Complete your degree from anywhere in the United States with 100% virtual coursework.
- On-Campus: Engage in hands-on labs and face-to-face collaboration in Magnolia.
- Hybrid: Utilize a blend of both formats to balance your career and education.
Program Duration & Start Dates
- Duration: Typically completed in 18–24 months.
- Start Dates: Rolling admissions with intakes available for Fall, Spring, and Summer.
- Deadlines: * Fall: July 20 | Spring: December 1 | Summer I: April 1 | Summer II: June 1
- Accelerated Master’s Option: High-achieving SAU undergraduates can begin earning AI credits during their senior year, shortening the path to graduation
Why Choose the Artificial Intelligence Option at SAU?
- Advanced and Emerging AI Curriculum: The curriculum progresses from established machine-learning methods to rapidly developing areas such as generative AI and autonomous, agent-based systems.
- Strong Computing Foundation: Students develop the programming, database, networking, and information-security knowledge required to build reliable and secure AI applications.
- Applied, Project-Based Learning: Coursework emphasizes implementation, experimentation, research, and the application of AI techniques to realistic organizational and societal problems.
- Cross-Disciplinary Applications: Students can connect AI with professional or academic interests in business, healthcare, engineering, education, science, and the social sciences.
- Responsible AI Development: The program addresses privacy, security, ethical, governance, bias, transparency, and reliability considerations involved in deploying intelligent systems.
- Flexible and Affordable Graduate Education: SAU offers online, hybrid, and on-campus study options at a competitive graduate tuition rate.
- STEM-Focused Preparation: The MCIS program provides advanced technical preparation for domestic and international students. Eligibility for any immigration or practical-training benefit remains subject to current federal rules and the student’s individual circumstances.
Skills You Will Gain
- Develop, train, test, and evaluate machine-learning models.
- Apply foundational artificial intelligence principles to real-world computing problems.
- Design applications that use large language models (LLM) and other generative AI technologies.
- Develop AI agents that can plan, reason, use tools, and complete multistep tasks.
- Integrate AI capabilities with databases, networks, cloud platforms, and enterprise applications.
- Evaluate model performance, reliability, security, privacy, bias, and ethical implications.
- Communicate AI methods, results, limitations, and recommendations to technical and nontechnical audiences.
- Apply and deploy intelligent computing methods within specialized professional and academic domains.
Faculty and Digital Learning Environment
- Research-Active Faculty: Students learn from faculty with expertise in artificial intelligence, machine learning, data science, cybersecurity, cloud computing, software development, and related areas.
- Modern Computing Environment: Students use virtualized computing environments, software platforms, and laboratory resources that support AI development and experimentation.
- Projects and Professional Experience: Courses incorporate implementation assignments, applied projects, and research activities. Internship and independent-study opportunities may allow students to explore AI applications relevant to their career goals.
Curriculum
Core Courses (12 hours)
MCIS 5103 – Advanced Programming Concepts
or MCIS 5163 – Python Programming
MCIS 5133 – Data Base Management Systems
MCIS 6163 – Computer Networking
MCIS 6173 – Information and Networking Security
or MCIS 5363 – Generative AI and Language Models
Artificial Intelligence Courses (12 hours)
MCIS 5333 – AI Principles and Applications
MCIS 5353 – Deep Learning
MCIS 6273 – Data Mining
MCIS 6283 – Machine Learning
Elective Courses (6 hours)
Select 6 hours from the following:
MCIS 5013 – The UNIX Operating System
MCIS 5103 – Advanced Programming Concepts
MCIS 5113 – Web Programming: Client Side
MCIS 5163 – Python Programming
MCIS 5313 – Data Structures and Algorithms
MCIS 5363 – Generative AI and Language Models
MCIS 5383 – Privacy, Security, and Ethics
MCIS 5413 – Web Programming: Server Side
MCIS 6123 – Decision Science
MCIS 6133 – User Interface Design
MCIS 6153 – Software Engineering
MCIS 6173 – Information and Networking Security
MCIS 6183 – Special Topics
MCIS 6193 – Special Topics
MCIS 6213 – Applied Cryptography
MCIS 6223 – Vulnerability Analysis and Risk Assessment
MCIS 6233 – Traceable Systems and Computer Forensics
MCIS 6243 – Wireless and Mobile Security
MCIS 6253 – Privacy Compliant Systems Design
MCIS 6263 – Big Data
MCIS 6293 – Special Topics
MCIS 6313 – Internet of Things
MCIS 6323 – Cloud Computing
MCIS 6333 – Data Visualization Programming
MCIS 6913 – Thesis
MCIS 6983 – Internship in Computer and Information Science
Total Hours – 30
Admissions
- Online Application: Submit your Graduate Studies Application.
- Transcripts: Official transcripts from all post-secondary institutions.
- GPA Requirement: A minimum cumulative undergraduate GPA of 2.5 is generally required. Conditional admission may be available to applicants with a GPA of at least 2.2, subject to Graduate Studies and program requirements.
Prerequisites:
A bachelor’s degree in computer science, information technology, data science, mathematics, engineering, or a related field is preferred.
Applicants with undergraduate degrees in other disciplines are encouraged to apply. Depending on academic preparation, students may be required to complete foundational bridge courses in programming and computer science concepts before beginning advanced coursework.
Previous coursework in artificial intelligence or machine learning is not required. Students should have, or be prepared to develop, competency in programming, quantitative reasoning, and fundamental computing concepts.
Career Outcomes
Graduates will be prepared for technical, analytical, research, and leadership positions involving artificial intelligence and intelligent software systems.
AI expertise is increasingly relevant across technology, healthcare, finance, manufacturing, education, government, retail, logistics, scientific research, and other sectors. Graduates are prepared for:
- Artificial Intelligence Engineer
- Generative AI Developer
- Agentic AI Developer
- Intelligent Systems Developer
- Natural Language Processing Specialist
- AI Product or Technical Consultant
- Data and Analytics Specialist
- Machine Learning Engineer
- AI Application Developer
- Data Scientist
- AI Solutions Architect
- AI Automation Specialist
- Applied AI Researcher
- AI-Enabled Software Developer
Employment outlook
The U.S. Bureau of Labor Statistics projects employment of data scientists to grow 34 percent from 2024 to 2034. Employment of computer and information research scientists is projected to grow 20 percent during the same period.
