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Key takeaway: An online artificial intelligence degree teaches you to build systems that learn from data and act on it – machine learning models, neural networks, natural language and vision systems, and the software engineering that puts them into production. A degree titled BS in Artificial Intelligence is a newer offering, and many students reach the same skill set through a computer science degree with an AI concentration. There is no programmatic accreditor for AI degrees, so institutional accreditation is the check that matters. Compare accredited programs below.
Artificial intelligence is not a separate discipline from computing so much as a specialization within it. AI coursework sits on a computer science foundation – programming, data structures, algorithms – and adds linear algebra, probability, statistics, and the modeling techniques that make learning systems work. What distinguishes a dedicated AI degree from a general computing degree is how early and how deeply you get into that modeling layer. Accredited online programs generally deliver the same curriculum and degree titles as campus programs; browse the best accredited online colleges to compare schools that offer them.
These accredited schools offer online programs. Request information to compare programs, costs, and formats.
Every school list on this site is ordered by the BOC Score, computed from the most recent school-level data published by the U.S. Department of Education (College Scorecard and IPEDS). To qualify, a school must be currently operating and accredited by an agency recognized by the U.S. Department of Education. Each eligible school is then scored on five measures, percentile-ranked against schools at the same credential level:
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Source:IPEDSCollege Scorecard
Source:Accreditor: Western Association of Schools and Colleges Senior Colleges and University CommissionIPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
AI graduates work across software companies, healthcare systems, financial services, logistics, defense contracting, and research labs. The occupations below are the ones most commonly associated with AI coursework. Median annual wages come from the Bureau of Labor Statistics Occupational Employment and Wage Statistics program.
Entry requirements differ sharply across these occupations, and that difference should shape your degree planning. Computer and information research scientist roles, at a median annual wage of $140,300 (BLS OEWS, May 2025), are typically research positions that expect a graduate degree, often a doctorate. Data scientist roles, at $120,230 (BLS OEWS, May 2025), commonly expect a master’s degree or substantial applied experience. Software developer roles, at $135,980 (BLS OEWS, May 2025), and computer systems analyst roles, at $105,850 (BLS OEWS, May 2025), are more commonly entered with a bachelor’s degree. A bachelor’s in AI positions you well for the developer and analyst side of that list; the research titles usually require going further. Individual outcomes vary by employer, geography, and experience.
Online AI programs cover programming, data structures and algorithms, linear algebra and probability, machine learning, neural networks and deep learning, natural language processing, computer vision, and AI ethics and governance. Most conclude with a capstone project that builds and evaluates a working model.
Both routes teach the same core material. A dedicated AI degree gets you into machine learning coursework earlier and devotes more electives to it. A computer science degree with an AI concentration gives you a broader and more widely recognized credential, with AI as a specialization inside it, which is useful if your plans might shift toward general software engineering. If both options are available to you at accredited schools, compare the required course lists rather than the degree titles.
Machine learning degrees are typically narrower and more often offered at the master’s level, concentrating on the modeling and statistical learning core. AI bachelor’s programs are broader undergraduate degrees that cover machine learning as one component alongside search and reasoning, natural language processing, vision, robotics, and ethics. If you are choosing between them, see the machine learning program guide.
No. There is no recognized programmatic accreditor specific to AI degrees. ABET accredits some computing programs, but AI-titled programs are rarely ABET-accredited, so its absence tells you little here. Verify that the institution holds accreditation from a recognized institutional accreditor such as HLC or SACSCOC through the U.S. Department of Education database.
Most bachelor’s programs assume no prior calculus or linear algebra and teach both in sequence. What they do generally expect is comfort with algebra and a willingness to work through quantitative material, since probability, statistics, and linear algebra are load-bearing for everything in the machine learning core rather than optional background.
AI curricula generally move through four layers. The first is programming and computing foundation: a language (nearly always Python, sometimes alongside C++ or Java), data structures, algorithms, and enough systems knowledge to understand where computation actually happens. The second is mathematical foundation: calculus, linear algebra, probability, and statistics. This layer is where AI programs diverge most from general IT degrees, and it is the layer students most often underestimate. Matrix operations and gradients are not background trivia in machine learning coursework; they are the mechanics of the material.
The third layer is the AI core itself: supervised and unsupervised machine learning, neural networks and deep learning, and then the applied branches – natural language processing, computer vision, reinforcement learning, and knowledge representation and search. Programs differ in which branches they treat as required and which as electives, and that choice tells you a lot about a program’s real emphasis.
The fourth layer is application and consequence: model evaluation, deployment and MLOps practices, and AI ethics and governance. The ethics coursework in a well-built AI program is not a formality. Bias in training data, model explainability, privacy, and the emerging regulatory picture are all working concerns in hiring conversations, and a program that treats them as a single elective is telling you something about how current its curriculum is.
Most programs finish with a capstone that carries a project from problem framing through data preparation, model training, evaluation, and a presented result. That artifact matters more in AI hiring than in many fields, because interviews often ask what you have actually built.
This is worth being direct about. A BS in Artificial Intelligence is a comparatively new degree title. A small number of universities have offered one for several years; many more have added AI concentrations, certificates, or tracks inside existing computer science and data science departments instead. That means the applicant pool you will be competing with contains far more computer science graduates with AI coursework than AI-titled graduates.
That is not an argument against the degree. It does mean the degree title alone will not do much work for you, and that you should evaluate a program on its course list and its faculty rather than on the name over the door. A rigorously built BS in Artificial Intelligence with a full mathematics sequence, several machine learning courses, and a substantial capstone is a strong credential. An AI-titled program that is a general IT degree with two machine learning electives bolted on is not, regardless of what it is called.
Two practical consequences. First, if you are already partway through a computing degree, adding an AI concentration is usually the better move than restarting under a new title. Second, if you are choosing from scratch and both options are open, weigh how certain you are about the specialization – a computer science degree keeps more doors open, an AI degree gets you deeper into the specialization sooner. For a fuller treatment of whether the credential pays off, see is an online AI degree worth it.
AI graduates rarely hold a job title containing the words “artificial intelligence.” Far more often the title is machine learning engineer, data scientist, software engineer, research engineer, or analyst attached to a specific function – a fraud detection team, a clinical imaging group, a recommendation or search team, a demand forecasting unit.
The work is usually less about inventing new model architectures than about the surrounding engineering: understanding the problem well enough to know whether a model is even the right tool, getting data into usable shape, choosing an approach, evaluating honestly whether it works, and getting it running reliably in production. Graduates consistently report that the data preparation and evaluation parts of the job take more of the week than the modeling.
Degree level shapes which of these paths is open. Research-track roles generally expect graduate study; engineering-track roles are commonly entered with a bachelor’s and grown into. For general labor-market context on the occupations AI graduates commonly enter, see the Bureau of Labor Statistics Occupational Outlook Handbook.
Artificial intelligence or a related field? Choose AI for building systems that learn from data. Consider computer science for a broader computing foundation with AI as one possible specialization, machine learning for a narrower focus on the modeling core, data analytics if your interest is drawing and communicating conclusions from data rather than building models, or technology for a general IT foundation. If you are weighing two of these directly, see AI degree vs data science degree.
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