Artificial Intelligence

The Penn State Great Valley School of Graduate and Professional Studies offers three distinct degrees in the field of Artificial Intelligence.

Master of Artificial Intelligence (M.A.I.)

The Master of Artificial Intelligence (M.A.I.) is a 30-credit, interdisciplinary graduate program designed to prepare students to lead the design, development, and deployment of Artificial Intelligence (AI) and Machine Learning (ML) products and services across diverse domains. 

The M.A.I. is structured around a Base Program and two specialization options—AI Engineering and Applied & Generative AI—offering flexibility to match students’ professional goals and technical backgrounds.

• The Base Program serves as a generalist pathway providing broad training across foundational AI concepts, data analytics, and ethical and responsible AI practices. This pathway is ideal for students seeking a comprehensive understanding of AI principles and applications across multiple industries.
• ​The AI Engineering Option is a systems-oriented track designed for students with programming experience who aspire to design, implement, and deploy AI and ML systems. This option emphasizes technical rigor, focusing on deep learning, natural language processing, predictive analytics, computer vision, and scalable AI system design to prepare graduates for technical and engineering-focused AI roles.
• The Applied & Generative AI Option is intended for professionals who wish to harness AI technologies to drive innovation, improve decision-making, and lead digital transformation within their organizations. Emphasizing the use of low-code/no-code tools and generative AI platforms, this option develops professionals who can strategically implement AI solutions and bridge the gap between technical AI capabilities and organizational needs to leverage AI for digital transformation, achieving operational efficiencies, and strategic growth through innovation.

The Master of Artificial Intelligence degree is offered both in residence at Penn State Great Valley and online through Penn State World Campus, providing flexible access for working professionals and students nationwide.

Master of Science (M.S.)

The Master of Science in Artificial Intelligence is a comprehensive 30-credit program designed to equip students with advanced knowledge and research skills in AI. This program emphasizes both theoretical foundations and practical applications, ensuring a well-rounded educational experience. The curriculum includes cutting-edge topics such as machine learning, deep learning, natural language processing, computer vision, reinforcement learning. It focuses on research methodology and analytical skills, preparing graduates for successful careers in academia, industry research labs, and advanced technical roles. A key component of the program is a culminating thesis, which allows students to demonstrate their ability to conduct research, engage in scholarly analysis, and effectively communicate their research findings.

Master of Business Administration (M.B.A.)

The MBA in Artificial Intelligence (MBAi) degree is a 30-credit interdisciplinary master’s program that aims to prepare students for careers in the rapidly advancing field of artificial intelligence. The degree combines the principles of business management, finance, marketing with the advanced study of the field of artificial intelligence in machine learning, computer vision, computational linguistics, data analytics and AI ethics. This degree is designed to equip students with leadership, managerial and financial expertise, and the technical proficiency needed to manage, lead and innovate in the rapidly evolving field of AI. Students will also gain soft skills necessary for professional success through experiential learning and active participation in real-world projects and team collaboration. After completing the program, graduates will be prepared to engage in the middle and upper management and innovation of AI-based products, services and programs. They will be positioned to gain a competitive edge in the job market, as they possess the knowledge to leverage AI for strategic decision-making, operational efficiency and ethical and socially responsible AI.

Admissions Requirements

Applicants apply for admission to the program via the J. Jeffrey and Ann Marie Fox Graduate School application for admission. Requirements listed here are in addition to Graduate Council policies listed under GCAC-300 Admissions Policies.

The language of instruction at Penn State is English. English proficiency test scores (TOEFL/IELTS) may be required for international applicants. See GCAC-305 Admission Requirements for International Students for more information.

Master of Artificial Intelligence (M.A.I.)

Admission to the Master of Artificial Intelligence degree will be based on baccalaureate academic records, applicable work experience, and two letters of recommendation from a previous professor or supervisor who can attest to the applicant’s academic potential. Applications must also include a statement of professional goals and a curriculum vita. An undergraduate cumulative grade-point average of 3.0 or better on a 4.0 scale in the final two years of undergraduate studies is required. 

The Master of Artificial Intelligence degree is structured around three distinct pathways to completion, each designed to align with students’ backgrounds, interests, and professional goals, and each with its own admission requirements:

• Base Program (Generalist Path)
• AI Engineering option
• Applied & Generative AI option

Students who wish to change from one option to another will have their application reviewed by the professor in charge of the program to ensure they meet the specific admission requirements associated with the new pathway.

1) Admission to the "Base program"

Applicants to the Base Program with an undergraduate degree in computer science, engineering or mathematics may apply. Applicants from other disciplines will be considered based on prior coursework and/or standardized test scores. 

Applications must include a statement of professional goals, a curriculum vita or resume, and two letters of recommendation.

2) Admission to the "AI Engineering" Option

Applicants to the "AI Engineering" option with an undergraduate degree in computer science, engineering or mathematics may apply. Applicants from other disciplines will be considered based on prior coursework (including the Entrance Requirements for Mathematics and  Entrance Requirements for Programming stated below) and/or standardized test scores.

Applications must include a statement of professional goals, a curriculum vita or resume, and two letters of recommendation.

Entrance Requirement regarding Mathematics:
Applicants must complete Linear Algebra and Calculus I equivalent to Penn State University’s MATH 140 and one semester of probability or statistics.

Entrance Requirement regarding Programming:
Applicants must complete two introductory-level programming courses where both courses used the same language. If an applicant believes his/her work experience satisfy the background, he/she should include a recommendation letter from a technical colleague describing the applicant’s coding contributions at work.

Applicants without mathematics and programming requirements might take the computer science bridge programs which prepare them to acquire basic knowledge in Linear Algebra, Calculus, Statistics, Programming, and Optimization. 

2) Admission to "Applied and Generative AI" Option

Applicants to the "Applied and Generative AI" Option with an undergraduate degree in computer science, engineering or mathematics may apply. Students from other disciplines will be considered based on prior coursework and/or standardized test scores. 

Applications must include a statement of professional goals, a curriculum vita or resume, and two letters of recommendation. 

Master of Science (M.S.)

Admission to the Master of Artificial Intelligence degree will be based on baccalaureate academic records, applicable work experience, and two letters of recommendation from a previous professor or supervisor who can attest to the applicant’s academic potential. An undergraduate cumulative grade-point average of 3.0 or better on a 4.0 scale in the final two years of undergraduate studies is required.

Applicants with an undergraduate degree in computer science, engineering or mathematics may apply. Students from other disciplines will be considered based on prior coursework (including the Entrance Requirements for Mathematics and Entrance Requirements for Programming stated below) and standardized test scores.

Applications must include a statement of professional goals, a curriculum vita or resume, and two letters of recommendation. 
Entrance Requirement regarding Mathematics:Applicants must complete Calculus I equivalent to Penn State University’s MATH 140 and one semester of probability or statistics.
Entrance Requirement regarding Programming:
Applicants must complete two introductory-level programming courses where both courses used the same language. If an applicant believes his/her work experience satisfy the background, he/she should include a recommendation letter from a technical colleague describing the applicant’s coding contributions at work.

Applicants without mathematics and programming requirements might take the computer science bridge programs which prepare them to acquire basic knowledge in Linear Algebra, Calculus, Statistics, Programming, and Optimization. 

Master of Business Administration (M.B.A.)

Admission to the Master of Business Administration in Artificial Intelligence degree will be based on baccalaureate academic records, applicable work experience, and two letters of recommendation from a previous professor or supervisor who can attest to the applicant’s academic potential. An undergraduate cumulative grade-point average of 3.0 or better on a 4.0 scale in the final two years of undergraduate studies is required.

Applicants with an undergraduate degree in computer science, engineering or mathematics may apply. Applicants with undergraduate degrees in business and social sciences or STEM degree will be considered based on prior coursework (including the Entrance Requirements for Mathematics and Programming stated below) and standardized test scores. 

Applications must include a statement of professional goals, a curriculum vita or resume, and two letters of recommendation. 
Entrance Requirement regarding Mathematics:
Applicants must complete Linear Algebra and Calculus I equivalent to Penn State University’s MATH 140.
Entrance Requirement regarding Programming:
Applicants must complete an introductory-level programming course. If an applicant believes his/her work experience satisfy the background, he/she should include a recommendation letter from a technical colleague describing the applicant’s coding contributions at work.

Applicants without mathematics and programming requirements might take the computer science bridge programs which prepare them to acquire basic knowledge in Linear Algebra, Calculus, and Programming. 
 

Degree Requirements

Requirements listed here are in addition to Graduate Council policies listed under GCAC-600 Research Degree Policies and GCAC-700 Professional Degree Policies.

Master of Artificial Intelligence (M.A.I.)

The Master of Artificial Intelligence degree is conferred upon students who earn a minimum of 30 credits at the 400, 500, or 800 level while maintaining an average grade-point average of 3.0 or better in all course work, including at least 18 credits at the 500 level or 800 level.
The program curriculum consists of 9 credits of common core courses, 9 credits of prescribed courses within either a selected specialization option or the Base Program, and 9 credits of elective courses chosen from an approved list of courses maintained by the program office. 

In addition, all program paths culminate in A-I 894 capstone (A-I 894) a 3-credit culminating course that allows students to integrate and apply the theories, methods, processes, and tools of artificial intelligence acquired throughout their studies in a comprehensive, hands-on project experience;;

• The choice of capstone project and exact form will be mutually determined by the instructor and each student.
• Students will collaborate in teams to develop real-world AI products and services, fostering practical experience in project management, teamwork, and the application of AI solutions to industry challenges.
• A written paper based on the applied project is required and must contain project description, analysis, and interpretation of its findings.
• Students will be encouraged to upload their capstone work products to be available publicly via ScholarSphere and to participate in the World Campus Graduate Capstone Exhibition.

Collectively, the M.A.I. prepares graduates to confidently enter the workforce with the expertise needed to design, develop, and deploy ethical and responsible AI technologies across various sectors.

Base Program

The Base Program within the Master of Artificial Intelligence (M.A.I.) degree offers a flexible and comprehensive pathway for students who wish to complete the program without declaring a formal specialization. This generalist track provides a broad foundation across the full spectrum of artificial intelligence—from data collection and preparation to modeling, interpretation, and responsible deployment—equipping graduates with the versatility to apply AI principles across multiple industries and professional roles.

Designed for individuals seeking a balanced mix of technical depth and practical application, the Base Program emphasizes the integration of data analytics, AI methodologies, and ethical decision-making.

Through the prescribed courses such as DAAN 822 – Data Collection and Cleaning, DAAN 545 – Data Mining, and A-I 840 – Responsible AI, students gain hands-on experience in data acquisition, preprocessing, and preparation for modeling while developing the ability to apply statistical reasoning and data-driven insights to solve complex problems. Students also learn to identify, clean, and integrate diverse datasets, perform data mining and predictive analytics, and apply machine learning techniques to uncover meaningful patterns and trends. 
The prescribed courses in the Base Program utilize no-code tools for assignments and learning activities. In addition, code snippets are provided as supplementary materials to support the development of basic programming skills for students who wish to enhance their programming proficiency.

The curriculum is intentionally broad and interdisciplinary, allowing students to select from a range of approved electives to tailor their studies according to professional interests—whether in computer vision, generative AI, data visualization, or advanced analytics. Learning experiences emphasize both conceptual understanding and applied skills through case-based and project-based assignments that mirror real-world AI challenges.

The Base Program culminates in the A-I 894 Capstone, a 3-credit integrative project that allows students to synthesize and apply the theories, methods, and tools of AI to a practical problem. Working individually or in teams, students design, develop, and evaluate a complete AI solution.

The Base Program is particularly suited for students who aspire to roles such as Data or AI Analyst, Applied Machine Learning Generalist, Product or Data Strategist, Analytics Lead, or Responsible AI Coordinator, as well as for professionals exploring AI applications across diverse domains before pursuing a more specialized focus.

Overall, the Base Program provides a broad yet rigorous education in artificial intelligence, preparing graduates to navigate the rapidly evolving AI landscape with the technical fluency and ethical awareness needed to succeed in a variety of professional contexts.

AI Engineering Option

The AI Engineering Option within the Master of Artificial Intelligence (M.A.I.) program is a systems-oriented pathway designed for students with prior programming experience who aspire to build, deploy, and optimize AI systems at scale. This option emphasizes technical rigor, mathematical foundations, and software engineering principles, preparing students to create advanced AI architectures and integrate them into real-world applications.

Students in this option develop a deep understanding of the theoretical and applied aspects of artificial intelligence, with a focus on designing, implementing, and maintaining AI-driven systems and infrastructure. Coursework in Deep Learning (A-I 570) and Natural Language Processing (A-I 574) equips students with advanced knowledge of neural networks, representation learning, and model architectures such as convolutional and transformer-based networks, enabling them to create complex AI models for image, text, and multimodal data. In Predictive Analytics (A-I 575) and related applied courses, students learn data-driven modeling, statistical learning, and performance optimization, providing them with the analytical framework needed to transform raw data into actionable insights.

Beyond algorithmic understanding, the AI Engineering option emphasizes production-grade engineering skills essential for developing and deploying AI systems in modern environments. They learn to manage and preprocess large datasets efficiently, design scalable AI systems, and implement robust data pipelines that support real-time or batch AI workloads.

The option culminates in the A-I 894 Capstone, a comprehensive, project-based experience that challenges students to design, implement, evaluate, and deploy a fully functional AI system. Through this culminating experience, students demonstrate their ability to integrate theory, coding proficiency, data management, and deployment skills in addressing real-world challenges.

Graduates of the AI Engineering Option are equipped to pursue high-level technical and leadership roles such as Machine Learning Engineer, AI Scientist, Computer Vision Engineer, AI Platform Engineer, Software Engineer (ML), Technical Architect, or Chief Engineer. They emerge as professionals capable of advancing the field of AI through innovation, systems thinking, and responsible design—building the technologies that power the next generation of intelligent systems.

Applied and Generative AI Option

The Applied and Generative AI Option within the Master of Artificial Intelligence (M.A.I.) program is designed for professionals who wish to harness the power of AI to drive innovation, improve decision-making, and enhance operational efficiency across diverse industries. This pathway focuses on developing the ability to implement and manage AI solutions using intuitive low-code/no-code platforms and generative AI tools, empowering students to become strategic leaders and translators between technical AI systems and organizational objectives.

This option emphasizes technical rigor, mathematical foundations, and strategic perspective on artificial intelligence. Rather than emphasizing coding and system development, it prepares students to conceptualize, evaluate, and deploy AI solutions that align with business needs, social challenges, and digital transformation goals. Students gain a solid theoretical foundation in AI concepts and data literacy, while learning how to apply these principles using modern tools that simplify AI implementation. 

Through courses such as Artificial Intelligence in Practice (A-I 810), Generative Artificial Intelligence (A-I 820), and Applied Machine Learning (A-I 830), students gain hands-on experience with the latest low-code/no-code and generative AI platforms. They explore technologies such as no coding tools, and generative AI suites to design and deploy AI-enabled applications, content, and workflows. 

The program culminates in the A-I 894 Capstone, a 3-credit integrative project where students collaborate to design, prototype, and present AI-driven products or solutions using the low-code/no-code and generative tools mastered throughout the program. This experience allows them to demonstrate comprehensive understanding, creativity, and strategic application of AI technologies to real-world challenges.

Graduates of the Applied and Generative AI Option are equipped to lead AI adoption initiatives, manage cross-functional innovation projects, and drive digital transformation within their organizations. Typical career outcomes include roles such as AI Application Analyst, AI Product Lead or Manager, Innovation Consultant, Digital Transformation Manager, and Operations Analytics Lead.

Master of Science (M.S.)

The M.S. in Artificial Intelligence degree is conferred upon students who earn a minimum of 30-credits at the 400, 500, or 800 level while maintaining an average grade-point average of 3.0 or better in all course work, including at least 18 credits at the 500 level or 600 level . The program curriculum includes 21 credits of required courses, 3-credits of electives and a 6-credit thesis research.

The M.S. in Artificial Intelligence provides a rigorous academic foundation in both the theory and practice of AI. The program begins with core courses in foundational AI, quantitative analysis, and predictive analytics, followed by advanced topics such as deep learning, reinforcement learning, and natural language processing. Students also receive formal training in research design to prepare for their thesis work. Elective courses such as Data Mining and Machine Vision allow students to specialize in areas aligned with their research interests and professional goals.

All students will complete their program of study with the 6-credit research thesis (A-I 600), through which they contribute novel ideas, applications, or insights to the field under the mentorship of faculty. The research thesis project provides students with an opportunity to apply their knowledge of the theories, methods, processes, and tools of AI, learned throughout their program, in a culminating and summative experience. The choice of project topic and exact form will be mutually determined by the instructor and each student. A written paper based on the applied project is required and must contain project description, analysis, and interpretation of its findings. Students will be encouraged to upload their thesis reports to be available publicly via ScholarSphere and to participate in research poster competitions.

This degree positions students for research roles in academia, government, and industry, or for continuation to doctoral programs in AI and related disciplines

Master of Business Administration (M.B.A.)

 The Master of Business Administration in AI (MBAi) degree is conferred upon students who earn a minimum of 33-credits at the 400, 500, or 800 level while maintaining an average grade-point average of 3.0 or better in all course work, including at least 18-credits at the 500 level or 800 level (with at least 6 credits at the 500 level). The program curriculum includes 18-credits of required courses, 9-credits of electives, and a 3-credit capstone course.

The MBAi degree bridges the gap between technical and managerial domains, enabling students to apply artificial intelligence in solving complex business challenges. Core courses such as Artificial Intelligence in Practice and Ethics of Artificial Intelligence introduce students to the operationalization and governance of AI in enterprise settings. Foundational business courses in marketing, finance, management, and accounting ensure students gain fluency in strategic decision-making and organizational leadership. The elective options allow students to customize their learning path, exploring topics such as deep learning, natural language processing, generative AI, or venture start-up, corporate innovation strategies and operations management. these electives are carefully selected to reflect the most relevant tools and technologies shaping business in the AI era.
All students will complete their program of study with a capstone project (A-I 894), where interdisciplinary teams collaborate to design, implement, and present a comprehensive AI solution tailored to a real-world organizational challenge. This experience allows students to synthesize their learning, apply AI methodologies in business contexts, and demonstrate their capabilities to potential employers or stakeholders. Graduates of the MBAi will be prepared to lead AI-enabled business transformation, oversee technical portfolios and AI strategy, translate business objectives into data-driven, AI-powered solutions and operate at the intersection of innovation, ethics, and leadership in the AI economy.

Minor

A graduate minor is available in any approved graduate major or dual-title program. The default requirements for a graduate minor are stated in Graduate Council policy GCAC-218 Minors.

Student Aid

World Campus students in graduate degree programs may be eligible for financial aid. Refer to the Tuition and Financial Aid section of the World Campus website for more information.

Courses

Graduate courses carry numbers from 500 to 699 and 800 to 899. Advanced undergraduate courses numbered between 400 and 499 may be used to meet some graduate degree requirements when taken by graduate students. Courses below the 400 level may not. A graduate student may register for or audit these courses in order to make up deficiencies or to fill in gaps in previous education but not to meet requirements for an advanced degree.

Learning Outcomes

1. KNOW: Graduates will be able to demonstrate appropriate breadth and depth of interdisciplinary knowledge, and comprehension of the major issues in artificial intelligence and machine learning.
2. APPLY/CREATE: Graduates will be able to acquire relevant datasets and identify and develop appropriate AI/ML algorithms to solve contemporary challenges.
3. COMMUNICATE: Effectively communicate the major issues of artificial intelligence and its applications including theories, approaches, findings, and implications both technical and ethical.
4. THINK: Graduates will be able to discriminate between state-of-the-art techniques in neural network architecture, machine learning, deep learning, and collective intelligence to determine the appropriate approach for a given problem.
5. PROFESSIONAL PRACTICE: Know and conduct themselves in accordance with the highest ethical standards, values, and, where these are defined, the best practices of the (as expressed in SARI training modules).

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