MGA Faculty Q&A: Should Students Major in AI?

Author: Sheron Smith
Posted: Monday, March 9, 2026 12:00 AM
Categories: School of Computing | Faculty/Staff | Pressroom | Students

Macon, GA

Middle Georgia State University (MGA) was the first institution in the University System of Georgia to offer a Bachelor of Science in Applied Artificial Intelligence. What sort of student is the degree best suited for? Dr. Kevin Floyd and Dr. Myungjae Kwak of the University’s School of Computing weigh in on the realities of the job market, the pace of change in an evolving discipline, and how students can make thoughtful, future-focused decisions about an AI degree.

AI is one of the fastest-growing fields right now. Who should consider majoring in AI? Who might not be a good fit?

Artificial Intelligence (AI) is one of the fastest growing fields right now because it has major impacts on almost every major field. Some industries are being transformed faster than others because AI can automate tasks, analyze large datasets, and assist decision-making. Major fields affected by AI include health care, agriculture, customer service, banking, retail, education, and manufacturing. While AI has been around for nearly 70 years, it has become extremely visible and widely used after 2022 with millions of people using generative AI tools.

Since AI impacts all major disciplines, it is a great fit for students in any discipline. Every sector is grappling with these tools, and people who can bridge domain knowledge with AI literacy will be incredibly valuable.

Who might struggle? Students who are drawn purely by salary projections, without genuine interest in the subject matter. The field moves fast and requires continuous learning. If your motivation is purely transactional, that can wear thin quickly. But if you're someone who finds yourself reading about AI on weekends just because it's interesting, you're probably in the right place.

Are entry-level AI jobs as common as headlines suggest? What does the actual job market look like for graduates?

The honest answer is it depends on how you define an 'AI job.' Roles with 'AI Engineer' or 'Machine Learning Researcher' in the title at major tech companies are highly competitive. But that's actually a narrow slice of the opportunity.

The broader demand is for people who can work alongside AI tools — analysts and project managers who can serve as the “middleman” between the computer scientists and software engineers who are developing AI tools and the end users who will use the tools to support and improve organizational processes.

Our graduates tend to find strong footing as data analysts, project managers, software developers, and technology consulting roles, many of which have incorporated AI significantly in the last few years. We’d encourage students to look past the flashy job titles and focus on developing skills that transfer data fluency, critical thinking about model behavior, and the ability to communicate technical concepts to non-technical audiences.

Could the field change so quickly that today's AI degree looks outdated in five years? How should students think about that?

This is the question we get most often, and it's a fair one. Yes, the tools will change — dramatically, probably. What won't change is the need to understand the underlying principles: how models are trained, where they fail, how to evaluate them, and how to apply them ethically.

A good AI education isn't about memorizing today's frameworks; it's about building a mental model that lets you adapt. When we look at how much the field has shifted just in the past three years, the graduates who've adapted best are the ones who understood the foundations, not just the syntax.

We also tell students: your degree is a starting point, not a finish line. The field rewards people who stay curious and keep learning. The half-life of a specific tool might be two years. The value of deep critical thinking and problem-solving skills? That compounds over a career.

Would you recommend pairing an AI degree with another specialty area?

Strongly, yes. In fact, it's one of the most strategic things a student can do. An AI specialist who also understands healthcare operations, or finance, or supply chain logistics, brings something to the table that a generalist programmer simply doesn't.

We see this play out in hiring all the time. Employers aren't just looking for someone who can build a model, they want someone who understands the problem the model is supposed to solve. Domain knowledge closes that gap.

Practically speaking, even a minor in business, communication, or a scientific field can make a meaningful difference. It also gives students more flexibility if the job market in pure tech tightens. Think of it as building a broader foundation rather than a narrower specialty.

What's one thing students and parents often misunderstand about AI as a major?

That it's only for people who want to code all day. Coding is certainly part of it, but AI as a discipline also involves ethics, policy, psychology, and communication. Some of the best work in the field right now is being done by people who think carefully about how AI affects people, not just whether the model performs well on a benchmark.

For parents especially, we’d say: this isn't a gamble. The skills students develop — data analysis, logical reasoning, systems thinking — are valuable regardless of where the industry goes. And for students, we’d say: don't let imposter syndrome hold you back. The field is young enough that there isn't one 'type' of person who belongs in it.

For students who are on the fence, what's the best first step?

Talk to someone in the field — not a recruiter, but an actual practitioner or faculty member. Get a realistic picture of day-to-day work, not just the career ceiling.

We’d also suggest taking one introductory course before committing to the major. Most programs offer something accessible that doesn't require advanced math upfront. See how you respond to it. If you find yourself wanting to dig deeper after that first course, that's a good signal.

And don't underestimate the value of the community around a program. The students you learn alongside, the faculty you can ask tough questions of, the projects that push you — that ecosystem matters as much as the curriculum on paper.

Learn more about MGA’s B.S. in Applied AI and all degree programs offered through the School of Computing at https://www.mga.edu/computing/programs.php.