Yes, the full-time, In-Person Master’s in Applied AI program is listed as a STEM-designated degree by the U.S. Department of Homeland Security for the purposes of the STEM OPT extension, allowing eligible students to apply. However, approval of STEM OPT is at the discretion of U.S. Citizenship & Immigration Services.

No. MS-AAI does not accommodate a CPT internship, and you should not plan around one.

F-1 students generally must complete a full academic year of study before becoming eligible for Curricular Practical Training. MS-AAI is three quarters from start to finish, so there is no point in the program where an eligible window opens up. This is a direct consequence of the compressed nine-month format, not an oversight.

If you are weighing programs specifically because you want to intern while you study, the MS in Applied Data Science is structured for that. It runs longer, and most of its courses meet in the evening precisely so students can hold an internship or job during the program.

What MS-AAI offers instead is speed. You finish in nine months rather than five or more quarters, and pursue employment authorization after you complete the degree.

Please refer to the English Language Proficiency guidelines.

The University of Chicago’s Office of International Affairs (OIA) advises students on immigration matters throughout their time in the program, including visa status, employment authorization, and travel. OIA provides MS-AAI students with a dedicated advisor and runs CPT, OPT, and other webinars for international students.

The Master of Science in Applied Artificial Intelligence (MS-AAI) is the University of Chicago’s first AI-focused master’s program. The degree is granted by the Physical Sciences Division (PSD) in affiliation with the Data Science Institute (DSI).

MS-AAI is a full-time program: 9 advanced courses completed in 9 months. It is built for people who already have a solid technical foundation in data science and machine learning and who want to move quickly into full-time roles, venture startups, or targeted internships. Rather than building from the fundamentals up, the curriculum begins with advanced topics and drills down into foundational concepts.

The program is 9 months long and spans three quarters: Autumn, Winter, and Spring. You will complete 9 courses total, taking 3 courses per quarter. There is no summer quarter.

Total tuition for the program is $67,500, based on 9 courses at $7,500 per course. Additional costs, including books, materials, and fees, vary by student.

No. MS-AAI does not require a thesis or a culminating capstone project. The degree is completed through coursework. Course projects are designed to be portfolio-ready, meaning the work you produce is intended to be something you can show employers, for example on GitHub. Students who want a substantial team-based build experience can elect the AI Innovation Practicum, which carries a project from problem framing through prototype and investor-style pitch.

If a thesis is important to you, the MS in Applied Data Science (MS-ADS) offers a two-year thesis track. 

Yes. MS-AAI is full-time and fully in person. All coursework takes place at the University of Chicago’s NBC Tower location in downtown Chicago. There is no online, hybrid, or part-time option, and the curriculum is not designed to be completed while working full-time.

All MS-AAI coursework is held at NBC Tower on UChicago’s downtown Cityfront campus, in state-of-the-art, recently updated classrooms. The downtown location puts you close to employers, off-site experiences, and the broader opportunities of Chicago, a global hub for business, technology, and innovation.

You are also a University of Chicago student with access to the resources and amenities of the Hyde Park campus and its research ecosystem. The program is designed as a dual-campus experience: rigorous technical coursework in downtown Chicago, connected to the deep research ecosystem of the Hyde Park campus.

Getting between the two campuses is straightforward. The University runs a shuttle between the Hyde Park campus and the Gleacher Center downtown, which sits directly across the street from NBC Tower.

The program is full-time and in person, with three courses per quarter over nine months. It is not designed to be completed alongside a full-time job, and we would strongly discourage attempting it.

Admitted students typically hold a bachelor’s degree (BS, BEng, or BTech, and in some cases a BA) in data science, computer science, artificial intelligence, mathematics, or a computationally focused engineering field.

More important than the specific major is demonstrated competency in machine learning  and deep learning fundamentals. That competency can be demonstrated through coursework, publications (peer-reviewed conference or journal).

Competitive applicants have at least one year of relevant full-time work experience in AI, machine learning, or data science. Two or more years is ideal. Internship experience in these areas also counts meaningfully.

Work experience matters here more than it does in many master’s programs. The curriculum starts at an advanced level, the cohort is small, and much of the learning happens through peer collaboration, so we look for people who can contribute professionally from day one.

Graduates are prepared for applied AI roles across industry. Representative titles in the current market include AI engineer, machine learning engineer, applied scientist, LLM engineer, AI platform engineer, solutions architect, and AI product and enablement roles, along with founding and early-stage startup positions.

The curriculum is deliberately built around what these roles require: production engineering fluency, agentic and multimodal systems, responsible deployment, and the ability to communicate with both technical and non-technical stakeholders.

You will earn a Master of Science from the University of Chicago, granted by the Physical Sciences Division. The University’s reputation, its PSD and Data Science Institute affiliations, and its substantial institutional investment in AI all travel with the credential.

It is a fair question to ask of any newer degree title, and the practical answer is that employers hire on demonstrated capability. The program is structured so that you graduate with portfolio work, not only a transcript.

The 9-course curriculum is made up of 4 required core courses and 5 electives.

Autumn 2027: three core courses. Artificial Intelligence I: Foundations and Principles; Value Creation and Leadership with Artificial Intelligence; and Artificial Intelligence Security, Safety, and Governance.

Winter 2028: one core course, Artificial Intelligence II: Advanced Architectures and Applications, plus two electives.

Spring 2028: three electives.

• Artificial Intelligence I: Foundations and Principles. Fluency across machine learning, probabilistic reasoning, search, and optimization, developed through practical use cases spanning supervised, unsupervised, and semi-supervised approaches.
• Value Creation and Leadership with Artificial Intelligence. How organizations move beyond experimentation to become AI-native: evaluating AI opportunities, redesigning workflows, leading organizational change, and applying Return-on-AI-Investment frameworks.
• Artificial Intelligence Security, Safety, and Governance. Model robustness, alignment, risk assessment, and auditability, alongside the ethical, regulatory, and societal dimensions of LLMs, autonomous agents, and multimodal systems.
• Artificial Intelligence II: Advanced Architectures and Applications. CNNs, LSTMs, transformers, diffusion models, and GNNs, together with transfer, reinforcement, causal, and contrastive learning, taken from theory to state-of-the-art practice.

You choose 5 electives from 8 options offered in year one. The electives are organized into three pillars, and you are expected to take at least one course from each pillar. That structure is intended to keep your training broad enough to be adaptable while still letting you concentrate where your interests are.

• Pillar A, AI in Practice: AI in Biomedicine; AI Innovation Practicum; AI in Asset Management.
• Pillar B, Frontier AI: Enterprise AI Systems: Generative AI, Agents, and World Models; Multi-Modal Agents
• Pillar C, Performance AI: Production AI in the Cloud; Quantum Computation and Applications; Agentic AI Ops.

Yes. Students may submit an Academic Advising Course Substitution Form to take at least one elective in an outside program, including non-STEM courses, to round out their leadership perspective. This flexibility is deliberate: the program is built for people who will lead cross-functional teams, not only write code.

The eight electives listed above are the year-one offerings. As a new program in a fast-moving field, the elective catalog will evolve.

Course offerings are subject to change and are dependent upon enrollment numbers. A course may be cancelled if enrollment is too low. We will communicate any changes to enrolled students as early as we are able, and you will always have enough elective options available to complete the degree on schedule.

No, and this is one of the most important things to understand about MS-AAI. Every course in the program is considered advanced and assumes you arrive with a strong foundation in data science and machine learning. The program begins rather than ends with advanced topics.

This design exists to minimize redundancy with your bachelor’s degree and prior professional training. If you’re looking to build a stronger technical foundation, UChicago’s MS in Applied Data Science offers a broader path across data science, machine learning, and AI, with opportunities to apply your skills through hands-on coursework and a capstone experience.

No. Course projects are a central feature, several courses are built around hands-on production work, and the AI Innovation Practicum is entirely team-based and project-driven. The program is designed so that what you build is something you can show.

Classes are offered as a mix of daytime and evening sessions, with some courses potentially scheduled on Saturday mornings. During the autumn and winter quarters, students move through the core curriculum together as a cohort, taking courses in sequence. Electives begin in the winter quarter and continue through spring, when students have the most flexibility to explore topics aligned with their interests and goals.