The Best Majors for Students Interested in AI and Emerging Technology

Artificial intelligence is becoming one of those fields that students hear about constantly but do not always fully understand.

A lot of high school students know they are interested in AI, emerging technology, robotics, or innovation broadly. The harder part is figuring out what that actually means academically. Should you major in computer science? Data science? Electrical engineering? Something interdisciplinary?

The answer is usually more nuanced than students expect.

One of the biggest misconceptions about AI is that there is one “correct” major for it. In reality, AI touches almost every industry now, from healthcare and finance to engineering, psychology, design, and public policy. The strongest students in this space are often the ones who understand both the technical side and the human side of technology.

That is part of why colleges are increasingly interested in students who can think across disciplines, especially in rapidly evolving fields like AI and emerging technology.

Computer science is still the foundation for many AI careers

For most students interested in AI, computer science is still the most direct path.

Computer science gives students a strong foundation in programming, algorithms, systems, machine learning, and computational thinking. Most AI-related careers still require some level of technical fluency, even when students eventually specialize in another area.

At the same time, computer science has become one of the most competitive majors in college admissions. Schools like Stanford, Carnegie Mellon, MIT, UIUC, and Georgia Tech receive enormous numbers of applications from students pursuing CS every year.

That is why understanding How Competitive Majors Affect Admissions Chances matters so much for students planning to apply into AI-related programs.

Students interested in CS should also understand that not every program approaches technology the same way. Some schools lean heavily theoretical, while others emphasize entrepreneurship, interdisciplinary learning, or industry experience. Blogs like Computer Science at Cornell University: A College Apps Strategy Guide and Computer Science at Harvard vs Yale: Which Is Better? show how different universities approach tech education differently.

Data science is becoming increasingly important

AI is driven by data. That is part of why data science has exploded in popularity over the last few years.

Students interested in machine learning, predictive modeling, analytics, or large-scale systems often find themselves naturally drawn toward data science programs. These majors usually combine statistics, programming, and mathematical modeling with real-world applications.

Data science can be especially appealing for students who enjoy math but also want practical applications beyond traditional engineering pathways.

At some universities, students can even combine computer science and data science through interdisciplinary tracks. That is part of why understanding Decoding the Differences in the World of Tech Majors can help students figure out which path actually fits their interests instead of simply choosing the most recognizable title.

Electrical engineering and AI are becoming deeply connected

A lot of students interested in AI overlook electrical engineering entirely, even though it plays a huge role in the future of emerging technology.

AI systems still rely on hardware, semiconductors, embedded systems, robotics, and computing infrastructure. Students interested in robotics, autonomous systems, computer vision, or advanced hardware often find electrical engineering to be one of the strongest long-term foundations.

The connection between these fields is becoming even stronger as AI systems continue expanding into physical technologies rather than staying purely software-based. That is explored further in Why Electrical Engineering and AI Work Perfectly Together.

Students who enjoy both hardware and software often benefit from exploring combinations like computer engineering, electrical engineering, or robotics-focused programs.

Interdisciplinary majors are growing quickly

One of the most interesting shifts happening right now is the rise of interdisciplinary technology majors.

AI is no longer isolated to one department. Universities are increasingly building programs that combine computer science with cognitive science, linguistics, psychology, biology, economics, ethics, or design.

That matters because many of the biggest questions surrounding AI are not purely technical anymore. They involve communication, ethics, healthcare, education, creativity, and human behavior.

Programs like Computation and Cognition at MIT reflect this shift particularly well. Students interested in both technology and human behavior often thrive in these kinds of interdisciplinary spaces.

This is also why students should not panic if they have multiple interests. Colleges are increasingly open to applicants who combine technical and non-technical interests thoughtfully. In fact, blogs like Do Top Colleges Prefer Focus or Interdisciplinary Students? and How to Choose a Double Major That Strengthens Your College Application can help students think more strategically about combining different academic interests.

AI students should think beyond coding

A lot of students assume that being interested in AI means they need to spend all of high school coding.

Coding certainly helps. Strong technical skills matter.

At the same time, the strongest applicants are usually the ones who explore technology in a broader way. That might mean:

• Research projects
• Robotics competitions
• AI ethics discussions
• Data analysis projects
• Engineering competitions
• Startup or entrepreneurship work
• Healthcare technology initiatives
• Human-computer interaction projects

Students who combine technical ability with initiative and creativity often stand out more than students who only follow traditional academic pathways.

That is part of why programs like FIRST Robotics Competition: What Students Gain from the Experience and Engineering Competitions: Why They Matter and When They Actually Help can be so valuable for students interested in emerging technology.

Research opportunities can also help students explore niche interests within AI much earlier. Students looking for more advanced opportunities often start with resources like 39 Must-Explore Research Programs for Ambitious High Schoolers or 10 of the Best STEM High School Summer Internships.

The best major depends on the type of problems you want to solve

This is the part students often skip.

The “best” major depends less on prestige and more on what kinds of problems you actually want to work on.

Students interested in:

• Machine learning systems may lean toward computer science or data science
• Robotics and hardware may lean toward electrical or mechanical engineering
• Healthcare AI may combine CS with biology or neuroscience
• AI ethics and policy may combine technology with philosophy, sociology, or public policy
• Human-computer interaction may involve psychology, design, or cognitive science

There is no single formula.

Some students are purely technical. Others are drawn toward interdisciplinary applications. Both can be incredibly valuable in the future of AI.

The takeaway

AI and emerging technology are changing quickly, which means students do not need to have every detail figured out immediately.

What matters most is building genuine curiosity, strong foundational skills, and meaningful experiences connected to your interests.

Computer science remains one of the strongest pathways into AI, but it is far from the only one. Data science, electrical engineering, cognitive science, and interdisciplinary technology programs are all becoming increasingly important.

The students who stand out long term are usually not the ones chasing whatever sounds trendy. They are the ones who understand how their interests connect to real-world problems and who actively explore those interests beyond the classroom.

That is what colleges notice too.

Frequently Asked Questions

Is computer science the best major for AI?
It is one of the strongest and most direct pathways, but not the only one. Data science, electrical engineering, and interdisciplinary technology majors can also lead to AI careers.

Do I need to know how to code before applying to college for AI-related majors?
Not necessarily, though having some coding experience can help. Colleges are also looking for curiosity, initiative, and problem-solving ability.

Are AI majors becoming more competitive?
Yes. Many AI-related fields, especially computer science, are becoming extremely competitive at top universities.

Can students combine AI with non-technical interests?
Absolutely. AI increasingly intersects with healthcare, psychology, business, public policy, design, and ethics.

What extracurriculars help students interested in AI stand out?
Research projects, robotics, coding projects, competitions, internships, engineering projects, and interdisciplinary passion projects can all strengthen an application.

How PathIvy Helps Students Interested in AI and Emerging Technology

Students interested in AI often know they enjoy technology but are not always sure how to turn that into a cohesive academic direction.

At PathIvy, students work closely with counselors to explore different technology-related pathways based on their actual interests rather than simply chasing whatever major sounds the most impressive.

For some students, that means pursuing advanced research or engineering competitions. For others, it means combining technology with healthcare, business, psychology, or entrepreneurship through interdisciplinary projects and internships.

The goal is not just to help students apply to competitive programs. It is to help them build a clear, authentic direction that reflects both their technical interests and the kinds of problems they genuinely want to solve.

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