AI and Computer Science: How a CS Master’s Prepares You

If it feels like talk of artificial intelligence (AI) is everywhere lately, it's not your imagination — from being used in everyday tools to changing the way we work, it's everywhere. What may surprise you, though, is that AI has been changing the world for years.

"Across the last 2.5 decades, I’ve worked at the intersection of science, engineering and data," said Dr. Swapnil Chhabra, an assistant professor of computer science at Southern New Hampshire University (SNHU). "One theme has stayed constant throughout: AI and machine learning have been indispensable tools across every domain I’ve worked in."

Today, AI shows no signs of slowing down.

"AI spans nearly every industry now," said Tim Hogg, an adjunct computer science instructor at SNHU, who's also led a team of AI engineers. "You're starting to see a fusion of those general engineering or developer roles with specialties in AI and machine learning now."

And as AI continues to evolve, computer science (CS) programs are creating new ways to enter or advance in this field. If this sparks your interest, you might consider earning a master's degree in computer science with a focus on AI.

Can I Work In AI With a Computer Science Degree?

A computer science degree can help prepare you for several types of AI jobs. Chhabra noted roles such as:

• Applied AI engineer: Practitioners who integrate AI tools into real products and workflows. This includes building LLM-powered applications, designing agentic AI systems, creating conversational assistants or developing domain-specific AI solutions in robotics, autonomous systems, healthcare or business analytics.
  
  
• AI ethics, policy and governance specialist: An increasingly important pathway focused on ensuring AI systems are fair, explainable, safe and aligned with organizational or societal values.
  
  
• AI/ML researcher: Innovators who push the boundaries of what AI can do. They experiment with new neural-network architectures, work on foundational model research, design more efficient algorithms or explore areas like reinforcement learning, computer vision and natural language processing.
  
  
• AI product manager: Hybrid roles for people who understand both technology and strategy. They define product vision, translate user needs into technical features, and guide the development of AI-powered products.
  
  
• Data engineer: The architects behind the scenes. They build the pipelines, databases, and large-scale data systems (such as data lakes and data warehouses) that make modern AI possible. They ensure data is clean, accessible and ready for downstream modeling.
  
  
• Data scientist: Professionals who analyze structured and unstructured data to generate insights, build predictive models, support decision-making and help organizations understand patterns hidden in their data. These roles exist across healthcare, finance, retail, biotech and virtually every data-rich industry.
  
  
• Machine learning engineer: Specialists who take models from the research stage into real-world production. They optimize algorithms, deploy models at scale and build systems that make real-time decisions — for example, fraud detection in finance, recommendation engines in e-commerce or predictive maintenance in manufacturing.

Hogg also highlighted jobs like computer vision engineer, machine learning engineer and NLP (natural language processing) engineer — further emphasizing the depth of possibility in the field.

In addition to these AI-specific roles, Hogg noted that there are jobs that combine traditional software development with AI skills. "(What) we're starting to see more is these hybrid roles that are increasing in demand," he said.*

Being a software engineer or business intelligence developer, Hogg said, now often requires AI or machine learning knowledge as part of the job.

Salaries can vary based on role and experience, according to the U.S. Bureau of Labor Statistics (BLS).* Some jobs in AI might offer higher pay than another, but it can depend on where you work, your skills and your level of education.*

Learn more about some of the top jobs with a master's in computer science.

How is AI Related to Computer Science?

At its core, AI is built on the same computer science principles used across many tech fields. When you study computer science, you're not just learning how to code. You're learning how systems work, how data flows and how to design tools that solve real-world problems — all essential skills for working with AI, Hogg said.

Chhabra noted another major similarity between the 2 disciplines: "Computer science teaches you how to build software; AI teaches you how to build software that learns," he said. "Together, they form a powerful combination for modern technical careers."

It's this combination of developer skills with machine learning and AI capabilities that has made its way into the curricula of many schools. At SNHU, for example, you can take an introduction to AI literacy course that is a beginner-friendly gateway to starting your learning in this area. Or, if you're looking for something deeper, you could consider a graduate program, adding a concentration in AI to your computer science master's degree.

In this concentration, you can go beyond theory, and focus on real tools and techniques used in the field. The objective? Not just to use AI, but to understand it — and apply it responsibly. SNHU's AI concentration was designed with the goal of helping you gain a deep comprehension of foundational AI concepts, Hogg said.

Beyond that, by combining an AI and computer science education, you can gain practical skills to help you excel in the field. Not just for today's jobs, but for future ones, too.

"An AI concentration doesn’t just prepare you for the jobs that exist today; it prepares you for roles that are still emerging," said Chhabra. "As AI reshapes sectors like robotics, biotech, sustainability and personalized digital experiences, professionals with strong CS foundations and AI expertise will be at the forefront of innovation."

Find out how you can learn more about artificial intelligence, including key skills and courses.

Will AI Replace Computer Science Degrees?

A question Hogg hears often is: Will AI replace jobs in tech? He believes that while some simple coding tasks might be done by AI, many computer science jobs will still need real people.

"Computer science is more than just about writing code," Hogg said. "What humans do really well that AI doesn't is understand problems, right? So it's more than writing code. It's about problem-solving, system design, ethical reasoning and understanding the why behind technology."

While AI tools can speed things up, he noted they still need human input. "They can accelerate productivity for engineers, but they still require human oversight, creativity, and contextual understanding," he said. "As AI becomes more powerful, the need for skilled professionals to build, govern and apply it responsibly, increases."

And as for "vibe coding" — in other words, using natural language to replace manual coding? Chhabra noted that, even with generative AI, fluency with coding languages like Python, C++ and Java can be a major differentiator.

"AI is not all coding — but coding gives you superpowers," he said.

That growing need for skilled professionals is one reason a master's degree in computer science — especially one with an AI focus — could be a consideration for you.

Is a Computer Science Degree Still Worth It for AI?

If you're serious about pursuing a future in AI, a master's degree in computer science can help you deepen your expertise and stand out in a competitive field. Hogg noted that AI doesn't just expand your skill set — it can also lead to new and more specialized opportunities in the tech field.*

An AI education can allow you to experiment hands-on with various industry tools. "(You'll) use tools like Python, PyTorch, cloud-based machine learning platforms, fine-tuning, pre-training models from Hugging Face," Hogg said.

But you'll also be able to take on real-world projects, Hogg noted. You can work with building recommendation systems, image classifiers and chatbots to help create a portfolio of work that you can bring with you into the AI field, he said.

And the best part? By having both an AI and computer science education under your belt, you can feel ready to grow your career with confidence.

"A CS degree with an AI focus gives you the best of both worlds: the stability of a timeless computing foundation and the momentum of a cutting-edge, future-driven skillset," Chhabra said.

*Cited job growth projections may not reflect local and/or short-term economic or job conditions and do not guarantee actual job growth. Actual salaries and/or earning potential may be the result of a combination of factors including, but not limited to: years of experience, industry of employment, geographic location, and worker skill.

Nicholas Patterson ’22 ’25MFA is a writer based in West Michigan with several years of experience as a content creator in higher education. He’s an alumnus of Southern New Hampshire University (SNHU), where he earned both his bachelor’s in English and creative writing and his Master of Fine Arts in Creative Writing. When his head’s not in novels, you can find him outside dreaming up his own stories. Connect with him on LinkedIn.