Online AI Degrees

You can earn an AI degree online at the bachelor's or master's level: Western Governors University, Indiana University Online, and Arizona State University top our online bachelor's ranking, while Concordia University, St. Paul, Maryville University, and Georgia Institute of Technology lead our online master's list. We track online or hybrid AI and machine learning programs at roughly 253 schools, and where programs publish totals, the median is about $38,035. Online programs cover the same math, coding, and projects as campus degrees, so judge them on course depth, not format.

Choose your level

Each degree level has its own ranking, and a program appears on only one of them.

Who online AI degrees fit—and who should wait

Good fit: working professionals adding ML to a CS, stats, or engineering base; career switchers who can handle linear algebra and Python; students who need flexible online schedules more than a campus research lab.

Wait or choose differently if you need heavy lab robotics, tight faculty mentorship for research careers, or you lack required math or coding courses—bridging courses first beat enrolling in a soft “AI” master's. Online bachelor's paths remain fewer (~50 schools in our catalog, including American Public University, Angelo State University, and Arizona State University); online master's dominate (~168 schools).

Bachelor's vs master's online

Online bachelor's options are growing but still scarce as standalone AI majors—many are CS degrees with AI concentrations or applied AI/business tracks. Median published tuition among priced online bachelor's rows in our catalog is about $48,708.

Online master's are the crowded market: MSAI, MSML, MEng in AI/ML, and CS master's with ML specializations. Median published tuition among priced online master's rows is about $29,850. A master's is usually the better online bet if you already hold a quantitative bachelor's and want specialization without a second four-year degree.

Certificates sit between: useful for skill stacking, weaker alone for research-scientist tracks.

AI major vs concentration vs CS (what the title hides)

“AI degree” can mean very different credit loads, so read past the title.

• Standalone AI major: large block of required AI/ML courses (often 40+ AI-related credits at dedicated programs)
• CS + AI concentration: core CS plus a smaller AI elective block (sometimes ~15–25 credits)
• Applied / business AI: tools, ethics, and product use-cases with lighter algorithms depth

Neither major nor concentration is automatically better. Match the credit mix to the job: production ML leans toward stronger algorithms/systems; product or analytics roles can thrive in applied tracks. Read the course list, not the marketing H1.

For the full debate, see AI vs computer science.

Online vs campus—beyond flexibility

Online wins on schedule and often on average published list price—but watch in-state vs out-of-state pricing. Some schools charge a flat online rate that is higher than in-state campus tuition. Campus still wins for research labs, robotics hardware, and dense peer cohorts.

Judge online quality by: graded projects (not quiz-only), live or office-hour faculty access, proctored integrity policies, and whether alumni show up in roles you want on LinkedIn.

Admissions and prep

Online AI master's programs typically expect a quantitative bachelor's, programming experience (Python/Java/C++), and comfort with calculus and linear algebra. Many waive GRE; some require bridging modules. Bachelor's admissions look like standard undergrad portals plus any portfolio or math placement rules.

If your background is non-STEM, plan a prep sequence (discrete math, calc, intro CS) before paying graduate tuition.

Careers online AI grads actually chase

Common outcomes: ML/AI engineer (with software depth), data scientist, applied scientist at product companies, and software engineer with ML ownership. Research scientist roles still lean PhD. An online credential is respected when the school is regionally accredited and you can show deployed projects—employers rarely care about “online” once the work sample is strong.

Frequently asked questions