So you're interested in potentially getting a formal graduate degree in artificial intelligence.

After looking at literally hundreds of AI master's programs, I want to help you decide which program you should pursue. I'll also touch a little on whether a degree makes sense for your specific career goals, because it doesn't make sense for everybody.

But for the most part, I'm going to assume you've already basically decided you want to get a master's degree in artificial intelligence, and you're just deciding between different options. So I'm going to skip the criteria that apply to any degree and focus on the one thing that matters most when you're comparing AI-first degrees (programs that are truly about artificial intelligence): the curriculum itself.

So let's dive right in.

Why the Usual Criteria Don't Work for AI Master's Degrees

For a lot of programs in other fields, you can look at their history, hiring outcomes, and academic reputation. With AI degrees, that's very difficult, because the number of AI degrees has exploded recently.

So the likelihood is that the program you're looking at is very new. That's not necessarily a bad thing. It just means there isn't as much of a track record as there would be if you went into, let's say, cybersecurity or another technical field.

Because of that, you really need to do your homework when it comes to evaluating the curriculum and looking at what you're actually going to be learning while you're in there.

Not All AI Graduate Degrees Focus on the Same Thing

This is important because master's in AI degrees vary a lot in terms of what they focus on.

We've evaluated hundreds of AI master's programs in our own database, and we noticed a wide range in what the curriculum actually concentrates on. I don't mean concentrations in the formal sense, like a degree with a concentration. I mean what the coursework emphasizes, even among degrees that have "AI" right in the name.

Broadly speaking, they fall into two camps.

Camp #1: Technical AI Programs

Some programs are very technical. They get into the weeds of how large language models work, a lot of machine learning, and other technical topics.

These might be a great fit if you're a technical person with a technical background (for example, information technology, cybersecurity, or coding), or if you want to get into that side of things.

There are a ton of these programs out there. For example:

  • The University of Texas at Austin's online Master of Science in Artificial Intelligence covers natural language processing, reinforcement learning, computer vision, and deep learning.
  • Johns Hopkins University's online MS in Artificial Intelligence (through its Engineering for Professionals division) covers areas like robotics, natural language processing, and image processing.

Programs like these will help you build and refine models and work with the technology itself.

Camp #2: Applied AI Programs

But there's this whole other subfield of AI education that surrounds the impact of the technology. Think AI ethics, AI education (teaching people how to use AI), and implementing AI inside a business.

A good example is Arizona State University's online MS in Artificial Intelligence in Business. Its courses include AI Business Strategy and Data and Technology Governance in Business, and ASU says prior programming experience isn't necessary.

Every AI Degree Has a Technical Component

Now, in order to do these things, you need to know how AI works under the hood, for sure. So every AI degree you take will have a technical component, which is good. (Even ASU's business-focused program includes a Machine Learning in Business course.) That core usually includes machine learning, statistics, Python, and neural networks.

It's not a matter of either/or. It's a matter of how much each program emphasizes each side, and whether that fits your career goals.

The Applied Side Is an Underrated Career Path

That matters because we're seeing a large number of job postings for people to implement AI in a business, teach people how to use AI responsibly, judge AI outputs, get the most out of the tools, and set up AI agents.

So there's this whole subfield within AI where you need technical knowledge, but you're not actually going to be building models and tweaking weights. You're mostly going to be helping people implement technology that other people built.

In fact, that may be an untapped, underrated career path for a lot of people who are interested in AI but aren't as technical.

The Bottom Line

Before you apply anywhere, pull up the full course list for every program on your shortlist and look at how much of it is technical versus applied. Then be honest with yourself about which kind of work you actually want to be doing day to day. The right AI master's is the one whose curriculum matches that answer, not just the one with "AI" in the name.