What It Really Looks Like to Study Artificial Intelligence Online
A decade ago, most universities treated artificial intelligence as a single elective buried deep inside computer science. It was the kind of class you took after surviving data structures, algorithms, and a heavy dose of calculus. That began to change in 2018, when Carnegie Mellon University launched the first dedicated undergraduate AI degree in the United States. The idea caught on quickly. Within a few years, programs at Penn, Arizona State, and other universities followed, and AI became recognized as its own discipline rather than just a specialization.
Today, nearly every large university and many smaller ones offer an AI degree online, especially after ChatGPT took the world by storm in late 2022. Most promise to prepare the next generation of AI professionals, but the content is surprisingly consistent from school to school. Students start with math and programming refreshers, usually in Python, then move through courses on machine learning, computer vision, natural language processing, deep learning, and AI ethics. Most end with a capstone or applied project that ties everything together.
Online programs typically run one or two courses per term, with recorded lectures, a human professor who acts more as a guide than a traditional instructor, assignments, quizzes, group projects, and weekly discussions designed to make the experience feel more like a real classroom. Many lack formal exams or finals, relying instead on continuous projects and participation to measure progress.
That structure gives online programs a useful rhythm, but it can also feel a bit artificial. Instructor videos, discussion boards, and scheduled check-ins create a sense of interaction, yet most of the real work happens alone. For students who are independent and self-driven, this flexibility is ideal. For others, it can feel disconnected. What keeps it meaningful are the deadlines, projects, and feedback loops that encourage you to stay engaged and take ownership of your learning.
A common question is whether you need a strong background in math or programming before starting. Officially, most schools list calculus, linear algebra, statistics, and programming as prerequisites. In practice, enforcement varies. Some universities verify prerequisites strictly through transcripts, while others treat them more as recommendations and admit students who show potential through work experience or a technical portfolio. Several programs now offer bridge or preparatory courses to help students build those skills before or during their first term. In truth, universities risk little by being flexible. Online programs can enroll large numbers of students without the same capacity limits as traditional classrooms, and tuition remains a strong incentive to broaden admissions. As anyone who has applied can tell you, the moment you submit an application, you can expect a wave of “advisors” eager to help you “get started.”
When I was searching for a master’s program, I had a career in technology but not a recent record of college-level math or active programming. Many schools dismissed my application outright, but a few were more flexible. Once admitted, I learned the math and coding as I went and discovered that success depended less on where I started and more on effort and curiosity. Still, not every student will find that approach comfortable. The safest path is to look carefully at each program’s admissions policies and be honest about your time, budget, and the importance of having a recognizable university name on your resume.
One of the most interesting developments in education has been the arrival of GPT-based tools. When large language models first became public, universities scrambled to decide what counted as cheating. Some banned them outright. Others saw opportunity. Around that time, Arizona State University made headlines by partnering with OpenAI and becoming one of the first major universities to formally integrate ChatGPT into coursework and research guidance instead of prohibiting it. Stanford’s faculty soon followed with policies allowing instructors to decide how AI tools could be used while discouraging blanket bans. Since then, many schools have adopted similar approaches, encouraging transparent and responsible use of AI in assignments.
These changes reflect a growing understanding that generative models are now part of professional life. Students are expected to know how to use them not to let them do the work, but to enhance understanding, analyze data, and generate code more efficiently. The real challenge is using these tools to learn rather than to shortcut the learning. It is easy to produce polished results with GPT and feel like you’ve mastered something when you’ve only skimmed it. At the same time, when used thoughtfully, these models can accelerate learning. GPTs act like a coach who is always available, if not always right, guiding you through complex material at your own pace.
Choosing where to study AI often comes down to readiness, budget, and reputation. Top-tier universities usually cost more and enforce prerequisites more tightly. Lesser-known schools may be more affordable and flexible but lack the same brand recognition. In reality, much of the curriculum overlaps, and the difference in instructional quality is often smaller than expected. What changes most is the experience, the networking, feedback, and sense of community that higher-priced programs promise to provide more consistently. Many programs also rely on shared or publicly available materials, so what truly matters is how deeply you engage with the content.
There is also a unique sense of urgency around AI education. The field is advancing at a pace that feels unmatched, and the job market is shifting with it. Waiting two or three years to complete a degree can be difficult when layoffs, new roles, and emerging tools are redefining what “AI expertise” even means. A few years ago, only a handful of people in technology called themselves AI specialists. Today, nearly everyone in the industry seems to claim some level of AI experience. The flood of new learners, online certifications, and self-taught practitioners has made it easier to enter the field but harder to stand out. It is easy to talk about AI knowledge, but proving it takes far more than buzzwords. A degree can help establish credibility, but it is only part of the picture.
There are now countless books, courses, and online resources that make it possible to learn AI quickly and affordably. Many professionals build their foundation through self-study long before entering a formal program. A degree can validate that effort, but it cannot replace the curiosity, persistence, and obsessive focus it takes to truly learn. The most successful students are those who approach AI like an obsession rather than a credential.
If you are considering this path, start learning now. Experiment with Python, explore open datasets, and take free math or programming refreshers. By the time you begin a program, you will already have the foundation to succeed. Combine formal education with self-directed learning and real-world experimentation. The degree provides structure and credibility, but your personal drive is what creates lasting expertise.
Finally, be smart about how you pay for it. Maximize tuition assistance from your employer, use military education benefits if available, and apply for scholarships wherever possible. Avoid taking on significant debt if you can, because the AI-driven disruption that is reshaping jobs today will continue to reshape them in the years ahead. For many, borrowing heavily for a degree in a rapidly changing market could turn into a burden that is difficult to recover from.
For my part, I found the University of San Diego offered the right combination of program quality, cost, flexibility, and credibility for someone like me, with limited formal math and programming experience. Today there are even more options available to match each student’s background, schedule, and goals, which is a welcome sign that the doors to AI are opening wider than ever.