2 AI Masters Degree Programs in Delaware

AI masters degree programs in Delaware prepare graduates for careers in a field that is changing healthcare and finance. Artificial intelligence technologies reshape these sectors, and the need for skilled professionals who can implement and manage AI solutions grows.

A masters in artificial intelligence provides the theoretical knowledge and practical skills to remain competitive in today’s job market. This applies whether you’re pursuing an AI masters degree to advance your career or entering the field for the first time.

This piece explores Delaware’s top two programs offered.

1. University of Delaware M.S. in Artificial Intelligence

The University of Delaware’s Department of Computer and Information Sciences houses a specialized M.S. in Artificial Intelligence program. The program trains high-level professionals who will lead AI breakthroughs in industries of all types. This masters in artificial intelligence goes beyond teaching students to use existing software libraries and apply out-of-the-box solutions. Students build a deep understanding of the principles that govern machine learning, neural networks, optimization, and algorithm design.

Program Overview and Specializations

The M.S. in AI requires 30 credits total. Students with different career objectives can choose from two distinct pathways. The non-thesis track serves students who view the degree as a terminal professional credential. The thesis track targets those interested in research and potentially pursuing a PhD. All students begin following the non-thesis track. Admission does not guarantee entry to the thesis option. Entry to the thesis track occurs only after the student’s advisory committee receives departmental approval.

This structure is different from the broader M.S. in Computer Science program at UD, which also offers optional specializations in autonomous driving and artificial intelligence. The dedicated AI program provides more focused training in areas where pre-packaged solutions cannot address complex challenges. Students develop expertise in natural language processing and computer vision, working on problems that require custom algorithms and optimized AI models for specific applications.

The program trains professionals capable of advancing AI capabilities beyond what tools in broad use can accomplish. These experts become essential for creating sophisticated solutions tailored to specific organizational needs. They develop new AI methodologies and handle authentic challenges that demand breakthroughs rather than standardized approaches.

Current UD undergraduate students in computer science can access an accelerated pathway through the 4+1 program. They earn both a B.S. in computer science and an M.S. in artificial intelligence in five years. This option allows motivated undergraduates to overlap graduate coursework during their senior year, which saves time and tuition compared to pursuing the degrees separately.

Curriculum and Core Courses

The coursework structure has five distinct categories. Each serves a specific purpose in building detailed AI expertise. Category 1, Fundamentals of AI and Computing, will give all students the technical background they need for AI and advanced studies that follow. Students must complete 12 credits in this foundational category.

Category 2 focuses on Topics in Mathematics and Theoretical Computer Science Closely Related to AI and requires 3 credits. This component builds a deeper understanding of mathematical methods that underlie AI and machine learning algorithms. It provides the theoretical foundation needed for advanced work.

Category 3, Topics in AI, contains fundamental technical courses focused on one or more AI subfields that apply across different domains. Students must complete 9 credits in this category, with at least 3 credits taken at the 8xx level (advanced graduate courses). These courses develop specialized knowledge in core AI areas.

Category 4 covers Electives and allows 6 credits of either fundamental technical computer science courses applicable to AI domains or application-specific courses where AI plays a central role. Students following the thesis option must use all six credits as research credits under CIS faculty supervision.

Category 5 requires enrollment in CISC890 Departmental Seminar each semester. This seminar component prepares students for degree completion and exposes them to broader issues. These include professional and ethical responsibilities and the impacts of AI on society.

The curriculum maintains strict requirements for course selection. All courses in Categories 1-4 must use standard letter grades. Courses in Category 2 may satisfy Categories 3 and 4, and Category 3 courses may satisfy Category 4. But the same course cannot count toward more than one category.

The coursework adjusts to accommodate research for the thesis track. Students complete 12 credits for breadth, 3 credits in mathematics and theory, and 9 credits in AI topics. They also complete 12 credits of electives (compared to 6 for non-thesis students). The thesis component adds 6 research credits and will give dedicated time for original research work.

Beyond the AI-specific program, students can also pursue AI specializations through the M.S. in Computer Science. This program offers autonomous driving and artificial intelligence as optional focus areas. The computer science program requires 30 credits with non-thesis and thesis options.

Admission Requirements and Prerequisites

The admission process assesses candidates on multiple dimensions. Competitive standards exceed minimum thresholds. A bachelor’s degree from a recognized institution forms the baseline requirement, with a minimum 3.2 grade average in the major field and 3.0 overall cumulative GPA.

Scholarly competence in mathematics and computer programming is a non-negotiable prerequisite. Applicants must show knowledge equivalent to at least one undergraduate course in each of these computer science topics: structured high-level language programming, data structures, computer architecture, operating systems, and analysis of algorithms.

Mathematical preparation requires completion of at least four undergraduate courses from this list: calculus, discrete mathematics, probability and statistics, mathematical logic, and comparable formal subjects such as Theory of Computation. This mathematical foundation supports the theoretical components of the AI curriculum.

What distinguishes the AI program from the general computer science graduate admissions is an additional requirement. Applicants must have completed at least one undergraduate course in machine learning, data mining, AI, or data science. This prerequisite will give incoming students familiarity with AI concepts before beginning graduate-level work.

Standardized testing requirements include minimum GRE scores of 153 for the verbal section, 155 for the quantitative section, and 4.0 for the analytical writing section. The GRE subject test is not required. But recent policy updates show that for computer science graduate programs, GRE test scores have become optional and are not required for admission.

International applicants face specific English proficiency requirements. The university requires an official TOEFL score of at least 79 on the Internet-based test or an IELTS score of 6.5 overall with no individual speaking score below 6.0 for admission without financial aid. Higher scores apply for teaching assistantship consideration: TOEFL of at least 100 or IELTS of 7.0 overall.

Three letters of recommendation complete the application package. These should come from professors when possible, though employers or others who can assess the applicant’s potential for graduate success may also provide recommendations. Applicants must also submit a graduate application essay and curriculum vitae.

Applicants who have completed graduate courses in computer science beyond the bachelor’s degree will have those grades reviewed as part of the admission decision. A minimum grade of 3.0 (B) in each such course is required.

The competitive nature of admission means meeting stated minimums does not guarantee acceptance. Failing to meet all requirements does not necessarily preclude admission if the applicant shows other appropriate strengths.

Career Outcomes and Job Prospects

The University of Delaware tracks career outcomes for graduates following the National Association of Colleges and Employers (NACE) First-Destination Survey model. The data gather information about career plans secured within six months of graduation. The data cover four graduating terms (summer, fall, winter, spring) of each academic year.

Career pathways span multiple high-demand roles for AI and computer science graduates. The AI engineering program prepares graduates for positions that include AI engineer, machine learning systems designer, data scientist, computational scientist, biomedical AI specialist, autonomous systems engineer, software engineer with AI/ML focus, and robotics and embedded systems engineer.

Employment projections for the field show strong growth. The Occupational Outlook Handbook states that employment of computer and information research scientists is projected to grow 23% from 2022 to 2032. This means that it exceeds the 3% average for all occupations. Computer science jobs rank among the highest-rated occupations according to Indeed and Glassdoor.

The broader job market for AI master’s degree holders shows robust demand. Industry projections show a 35% growth in AI and machine learning specialist roles by 2032. This vastly outpaces the average 5% growth across all occupations. This surge stems from AI’s expanding presence in healthcare, finance, automotive, and cybersecurity sectors.

Salary data for AI master’s graduates shows strong compensation potential. The average starting salary for AI master’s graduates in the US falls between $120,000 and $130,000, according to Glassdoor’s AI Career Report 2024. Salaries vary based on location, employer, and specialty. Metropolitan tech hubs like San Francisco, Seattle, and New York offer the highest compensation.

Master’s degree holders in AI earn about 35-40% more than bachelor’s degree holders in equivalent roles. Median salaries for AI master’s graduates reach $180,000 versus $130,000 for those with bachelor’s degrees. This earnings gap reflects the advanced skills that master’s programs provide and enables graduates to fill higher-level positions with greater responsibility and specialized knowledge.

So an AI engineer with a master’s degree often leads complex machine learning projects or designs sophisticated algorithms. These roles command premium salaries. Bachelor’s graduates start in junior positions such as AI analyst or data technician, with less immediate salary growth.

Demand for AI master’s graduates by 2030 will focus on candidates skilled in machine learning algorithms, natural language processing, and computer vision. Employers value those who can build flexible AI models and apply ethical AI practices.

The university reports that after graduation in 2024, 97% of master’s-level students were hired and earned an average salary of $90,870 for UD graduates. This data comes from the Lerner College of Business and Economics, which also offers AI-related programs. The career outcomes website provides detailed information about where graduates work and the salaries they earn.

The computer science alumni network provides support through mentorship and networking opportunities. It also gives insights into industry trends. This professional community receives backing from an exceptional career services team at UD. The team offers personalized career counseling, interview techniques, and exclusive job fairs featuring top employers.

Tuition Costs and Financial Aid

The 2025-2026 UD graduate student tuition rate is $1,116 per credit hour for the M.S. in Artificial Intelligence. Total tuition amounts to $33,480 before additional fees for the complete 30-credit program. Some programs have higher or lower rates based on competitive market research and showed success at their existing price points.

Full-time graduate students face mandatory fees beyond base tuition. The published graduate student tuition base rate for 2026-27 is $1,149 per credit hour. Full-time students enrolled in traditional on-campus programs pay additional mandatory costs. These include a Student Wellbeing Fee of $734, Student Center Fee of $268, and Graduate Recreation Fee of $100. International students pay an additional International Service Fee of $360.

Students in online programs and select programs designed for working professionals are not required to pay the wellbeing, recreation, and student center fees. Health insurance is a mandatory requirement for all full-time graduate students and international students. The UD Health Plan for students not on contract costs $3,887.54, though students with existing qualifying coverage can waive this fee.

Financial aid opportunities offset these costs for many students by a lot. Nearly 80% of full-time graduate students at UD receive merit-based financial awards. The Department of Computer and Information Sciences offers various funding types, though admission to the graduate program does not entitle applicants to financial aid. Aid is awarded from the pool of admitted applicants on a competitive basis, with most awards going to PhD students and occasionally to strong MS students.

University fellowships represent the most prestigious funding. The department awards them based on nominations to students with regular, full-time status and high academic standing. Fellowships usually provide full tuition and a stipend, though only a limited number may be available to the department in any given semester.

2. Wilmington University AI Masters Degree Program

Wilmington University structures its artificial intelligence graduate education through a Master of Science in Computer Science program that offers specialized concentrations aligned with workforce demands. The program provides two distinct pathways: an Artificial Intelligence concentration and a Software Engineering concentration, each designed to equip students with focused skill sets matching current industry requirements.

Program Overview and Specializations

The 30-credit MS in Computer Science program at Wilmington University responds directly to high-demand emerging areas of computing, preparing graduates to handle complex real-life computing challenges as data scientists or software engineering professionals. This structure is different from standalone AI degrees by embedding artificial intelligence training within a broader computer science framework, giving students both specialized AI knowledge and foundational computing expertise.

Students pursuing the AI concentration gain a deep understanding of machine learning modeling, deep learning and neural networks, natural language processing, large language models, agentic AI, and ethics in the digital world. This concentration addresses the growing need for professionals who can develop and deploy advanced AI systems across industries of all sizes. The curriculum emphasizes not just theoretical knowledge but practical implementation skills using industry-standard tools and frameworks.

The Software Engineering concentration, by contrast, explores algorithms, data structures, software engineering methodologies, system requirements and information system architecture. While distinct from the AI track, this concentration shares the same foundational core courses, allowing students to pivot between specializations if their career goals evolve during their studies.

Both concentrations refine communication skills and develop critical thinking abilities alongside technical competencies. Students learn to analyze issues methodically, break them into manageable components, and devise efficient solutions. These skills contribute to success in any professional setting, whether working on AI model development, software architecture, or cross-functional technology teams.

Wilmington University’s broader mission centers on providing available, career-focused education. Students can pursue their master’s in artificial intelligence while managing professional and personal commitments through convenient locations and online learning options. Rolling admissions allow students to begin their studies when ready, with classes starting every 8 weeks.

Curriculum and Core Courses

The curriculum architecture divides the 30 credits across three components: core courses, concentration-specific courses, and electives. This structure ensures all students acquire foundational knowledge while developing specialized expertise in their chosen concentration.

Core Computer Science Courses

All students, whatever their concentration, complete 6 credits of core courses that establish shared baseline competencies. These courses address fundamental concerns facing all computing professionals:

• CSC 7006 Ethics in Digital World: Examines ethical frameworks, professional responsibilities, and societal impacts of computing technologies, particularly as they relate to AI and data-driven decision making
• CSC 8101 CSC Capstone Project: Integrates knowledge from throughout the program into a substantial final project demonstrating mastery of key concepts and practical application skills

Artificial Intelligence Concentration Courses

Students in the AI concentration complete seven concentration courses totaling 21 credits. This focused curriculum builds expertise across the major subfields of artificial intelligence:

• CSC 7002 Python Programming: Develops proficiency in Python, the dominant language for AI and machine learning applications, covering libraries and frameworks essential for data science work
• CSC 7020 Theory of Artificial Intelligence: Establishes theoretical foundations of AI systems, including search algorithms, knowledge representation, reasoning under uncertainty, and planning
• CSC 7021 Cloud-Based Machine Learning: Teaches implementation of machine learning models using cloud platforms, addressing scalability, distributed computing, and deployment considerations for production AI systems
• CSC 7022 Deep Learning and Neural Network: Covers neural network architectures, backpropagation, convolutional networks, recurrent networks, and advanced deep learning techniques powering modern AI applications
• CSC 7023 Natural Language Processing (NLP): Explores techniques for processing and understanding human language, including tokenization, parsing, semantic analysis, and language generation
• CSC 7026 Large Language Models and RAG: Addresses cutting-edge developments in large language models and retrieval-augmented generation, reflecting the latest advances in AI capabilities
• MAT 6400 Mathematics for Machine Learning: Builds mathematical foundations underlying machine learning algorithms, including linear algebra, calculus, probability, and optimization methods

The AI concentration concludes with one elective course (3 credits) selected to complement the student’s specific interests or career objectives.

Software Engineering Concentration Courses

The Software Engineering track requires six concentration courses (18 credits) plus two electives (6 credits):

• CSC 7003 Algorithms and Advanced Data Structures: Examines sophisticated algorithms and data structures essential for efficient software systems
• CSC 7040 Software Engineering Methodologies: Covers software development lifecycle models, agile practices, and project management approaches
• CSC 7041 Software Systems Requirements: Teaches requirements gathering, analysis, specification, and validation techniques
• CSC 7042 Usability Engineering/Human-Computer Interaction: Addresses user-centered design principles and interface development
• CSC 7043 Information Systems Architecture: Explores architectural patterns, design principles, and system integration strategies
• CSC 7044 DevOps: Covers continuous integration, continuous deployment, infrastructure as code, and modern software delivery practices

The curriculum covers data structures and algorithms, Python programming, intro to data science, principles of software engineering, ethics in digital world, theories in artificial intelligence, cloud-based machine learning, deep learning and neural networks, natural language processing, data mining, data analytics, software engineering methodologies, software system requirements, usability engineering, information system architecture, and DevOps.

This complete coverage prepares students for diverse job opportunities including computer scientist, computer and information systems manager, computer and information researcher, computer programmer, computer science teacher, software developer, information security analyst, database administrator, computer systems analyst, computer systems administrator, software engineer, and data scientist.

Admission Requirements and Prerequisites

Wilmington University’s admission process for the MS in Computer Science program emphasizes availability while maintaining academic standards. The application requires several components but notably excludes standardized testing requirements that often create barriers at other institutions.

Application Components

Applicants must submit a completed graduate application accompanied by a $35.00 non-refundable application fee. This straightforward application process reflects Wilmington’s commitment to removing unnecessary obstacles for prospective students.

Official transcripts reflecting a conferred bachelor’s degree from an accredited institution form the core academic requirement. The degree should be in computer science or a similar program field, with examples including Information Technology, Cybersecurity, Game Design and Development, Computer Engineering, Data Science, Software Engineering, Programming, Information Science, and Artificial Intelligence.

Transcripts must be sent electronically or by mail directly from the school of attendance in a sealed envelope to: Wilmington University Office of Admissions, 320 N Dupont Highway, New Castle, DE 19720. Any transcripts from institutions outside the United States require an official course-by-course evaluation by a NACES.org approved agency.

Prerequisite Pathway for Non-Computer Science Majors

Recognizing that talented candidates may come from diverse academic backgrounds, Wilmington University provides a clear pathway for students without traditional computer science preparation. Students lacking a Computer Science degree or equivalent must enroll in three prerequisite courses:

• CSC5000 Introduction to Computer Science: Covers fundamental concepts, problem-solving approaches, and basic programming skills
• CSC5001 Theory of Programming Languages: Examines programming language design, syntax, semantics, and comparative analysis of different paradigms
• IST 7000 Data Management: Addresses database concepts, data modeling, SQL, and information management principles

These prerequisites ensure all students enter the graduate-level coursework with sufficient foundational knowledge, whatever their undergraduate major. Career changers and professionals from adjacent fields seeking to transition into AI roles will find this flexibility makes Wilmington University’s AI master’s degree programs in Delaware accessible.

Standardized Testing Policy

Wilmington University does not require SAT, GRE, or GMAT scores for graduate admissions. This policy removes a significant barrier that often discourages qualified working professionals from pursuing advanced degrees. Graduate success depends more on motivation, work experience, and undergraduate preparation than on test performance.

Rolling Admissions Advantage

The program operates on rolling admissions, allowing prospective students to apply and begin whenever they are ready. Classes start every 8 weeks, providing multiple entry points throughout the year rather than restricting admission to traditional fall and spring semesters. Working professionals benefit from this scheduling flexibility when timing their studies around career obligations or personal circumstances.

General Graduate Admission Requirements

Applicants must meet general institutional standards beyond program-specific requirements. Wilmington University requires official bachelor’s degree transcripts from all previously attended regionally accredited colleges or universities, with transcripts showing degree conferral dates. The university maintains a streamlined process designed to ensure smooth application experiences for all candidates.

Career Outcomes and Job Prospects

The MS in Computer Science program with AI concentration prepares graduates for a variety of career paths spanning data science, software development, and AI implementation roles. The curriculum’s practical focus and industry-aligned content directly address employer needs in rapidly evolving technology sectors.

Career preparation extends beyond technical skills to encompass communication abilities and problem-solving approaches valued across professional settings. Students develop capacity to analyze complex issues methodically, breaking them into manageable components and devising efficient solutions. This combination of technical depth and professional competencies positions graduates for advancement into leadership roles as their careers progress.

The program specifically prepares students for positions as computer scientist, computer and information systems manager, computer and information researcher, computer programmer, computer science teacher, software developer, information security analyst, database administrator, computer systems analyst, computer systems administrator, software engineer, and data scientist. This broad range reflects the versatile skill set the curriculum develops.

Students following the AI concentration gain specialized capabilities in machine learning modeling, deep learning, neural networks, natural language processing, and large language models. These skills directly match requirements for AI engineer, machine learning engineer, and data scientist positions experiencing strong growth across industries. The emphasis on large language models and retrieval-augmented generation reflects cutting-edge developments, ensuring graduates possess current knowledge of the most advanced AI technologies.

The curriculum addresses ethics in the digital world. As AI systems increasingly influence critical decisions in healthcare, finance, criminal justice, and other domains, employers prioritize candidates who understand ethical implications and can develop responsible AI solutions. This ethical grounding distinguishes Wilmington graduates as professionals prepared to navigate complex challenges.

The program’s dual concentration model provides career flexibility. Students uncertain about their specific career direction can begin the program, complete core courses, and select their concentration after gaining exposure to both AI and software engineering topics. This flexibility reduces risk for students concerned about committing to a specific specialization before fully understanding their options.

Wilmington’s convenient locations and online learning options enable students to maintain employment while completing their degrees. This continuity allows students to apply classroom learning directly to workplace challenges, building practical experience that complements academic knowledge. Many students use their graduate education to advance within their current organizations or transition to more specialized AI roles.

The rolling admissions and eight-week class schedule accommodate professionals seeking to upskill without career interruption. This availability factor expands opportunities for working adults to pursue AI master’s degree credentials that enhance their competitiveness in the job market.

Tuition Costs and Financial Aid

Financial considerations play a major role in graduate education decisions, and Wilmington University provides multiple resources to help students fund their studies. Tuition rates, fees, and financial aid availability all factor into the total investment required.

Next Steps

Artificial Intelligence is the future for all industries. A masters in AI is a worthy path to consider as it will provide you with the knowledge and skills to be at the forefront. Continue your education with one of these Delaware schools and you will be prepared for the career of your choosing.