Artificial intelligence is transforming nearly every industry, leading universities to expand their offerings of AI-related degrees and STEM programs. Students face the decision of which degree will best prepare them for long-term success. However, choosing the right major is not simply about finding “AI” in the title.
The ideal degree combines technical expertise with the flexibility to adapt as the technology evolves. For instance, many professionals enter the field through computer science, mathematics or engineering and then apply those skills to AI.
Students who understand both AI and the industries where it is applied are likely to have the greatest flexibility as technology and hiring needs continue to evolve.
The Best Degrees For AI Careers
There is no single “best” AI degree. The right choice depends on your career goals, interests and the type of work you hope to do in artificial intelligence. Students pursuing highly specialized AI careers may also benefit from pairing a technical major with mathematics, statistics or business.
Choosing an AI-related major isn’t only about preparing for one job. New LinkedIn data suggests that graduates with versatile majors who can transfer across industries may have a competitive advantage in today’s slower hiring market. That can make degrees like computer science attractive because they can lead to careers both inside and outside traditional AI roles. At the same time, students shouldn’t assume that earning an AI-related degree guarantees employment. As more universities introduce AI programs and interest in the field grows, competition for internships and entry-level positions is also increasing, making practical experience and transferable skills more important than ever.
The degrees below were selected based on employer demand, long-term career outlook and technical versatility. Some focus directly on AI; others provide the mathematical, engineering and business foundations behind today’s AI systems. A bachelor’s degree is sufficient for many entry-level AI careers, though research positions often require graduate study.
1. Computer Science
Computer science provides one of the strongest foundations for an AI career by teaching programming, algorithms, software engineering, databases and data structures. Many programs also include coursework in machine learning, computer vision and artificial intelligence.
Common careers: AI engineer, machine learning engineer, software developer, research scientist
Industries: Technology, healthcare, finance and manufacturing
Best for: Students seeking the broadest range of AI career opportunities.
Potential challenges: Computer science programs are often rigorous in mathematics and require ongoing learning as programming languages, frameworks and AI technologies evolve rapidly.
2. Artificial Intelligence
Degree programs in artificial intelligence emphasize machine learning, neural networks, computer vision, natural language processing and ethics in AI. Students acquire practical experience in developing intelligent systems and AI models.
Common careers: AI engineer, AI researcher, machine learning specialist, robotics developer
Industries: Technology, healthcare and autonomous systems
Best for: Students who know they want to specialize in AI.
Potential challenges: Because AI degree programs are relatively new, curriculum quality, research opportunities and industry partnerships can vary significantly between universities.
3. Data Science
Data science teaches students how to collect, organize and analyze large datasets through statistics, programming, machine learning and data visualization. These skills help organizations make data-driven decisions using AI.
Common careers: Data scientist, AI analyst, machine learning specialist
Industries: Finance, healthcare, retail, government and technology
Best for: Students interested in analytics and business decision-making.
Potential challenges: Data science requires strong statistical reasoning and programming skills and graduates must keep up with the evolving AI tools and data platforms.
4. Machine Learning
Machine learning programs emphasize predictive modeling, statistical learning and algorithm development. Students learn how AI systems improve by identifying patterns and making predictions from data.
Common careers: machine learning engineer, AI engineer, research scientist
Industries: Technology, finance, cybersecurity and healthcare
Best for: Students who want to build intelligent AI models.
Potential challenges: Machine learning is a highly mathematical field within AI, necessitating a solid understanding of calculus, linear algebra and probability.
5. Mathematics
Mathematics provides the theoretical foundation for artificial intelligence through calculus, linear algebra, probability and optimization. These concepts underpin machine learning and advanced AI systems.
Common careers: AI researcher, quantitative analyst, machine learning engineer
Industries: Technology, finance, healthcare and research
Best for: Students who enjoy abstract problem-solving.
Potential challenges: Mathematics programs often emphasize theory over application, so students may need additional programming experience or internships to compete for AI roles.
6. Statistics
Statistics focuses on probability, predictive modeling and analyzing uncertainty, making it essential for AI development. Students learn to identify patterns and build reliable models from complex data.
Common careers: data scientist, AI researcher, quantitative analyst
Industries: Healthcare, finance, insurance, and technology
Best for: Students who enjoy working with data.
Potential challenges: Students may need to supplement their coursework with programming languages like Python or R to prepare for many AI positions.
7. Software Engineering
Software engineering teaches students to design, build and maintain large-scale applications while integrating AI into real-world products. Coursework emphasizes programming, systems design and collaborative development.
Common careers: Software developer, AI engineer, cloud engineer
Industries: Technology, healthcare, finance and manufacturing
Best for: Students who enjoy building practical technology solutions.
Potential challenges: While software engineering provides solid development skills, students may need additional coursework in machine learning or artificial intelligence to specialize in AI.
8. Computer Engineering
Computer engineering combines hardware and software to develop intelligent devices, embedded systems and autonomous technologies. Students learn programming alongside electronics and computer architecture.
Common careers: Robotics engineer, embedded systems engineer, AI hardware engineer
Industries: Robotics, semiconductor manufacturing and autonomous vehicles
Best for: Students interested in AI-powered hardware.
Potential challenges: Programs typically include advanced coursework in engineering, physics and electrical systems, which can be more demanding than in software-focused majors.
9. Robotics And Autonomous Systems
Robotics and autonomous systems combine engineering, computer science and AI to create machines that can sense, learn and make decisions. Students often complete hands-on robotics projects.
Common careers: Robotics engineer, automation engineer, autonomous systems developer
Industries: Manufacturing, aerospace, healthcare and logistics
Best for: Students interested in physical AI systems.
Potential challenges: Robotics often requires interdisciplinary expertise in mechanical engineering, electronics and software, making it one of the broadest technical fields.
10. Cybersecurity
Cybersecurity programs educate students on securing networks, protecting sensitive data and defending AI systems against emerging threats. AI is increasingly playing a vital role in modern cyber defense.
Common careers: Cybersecurity analyst, AI security Specialist, digital forensics analyst
Industries: Government, healthcare, finance and technology
Best for: Students interested in protecting AI systems.
Potential challenges: Cybersecurity professionals must continually adapt to rapidly changing threats, regulations and AI-powered attack methods.
11. Computational Linguistics And Natural Language Processing
These programs explore how computers understand and generate human language through machine learning, linguistics and speech recognition. They power chatbots, translation tools and generative AI.
Common careers: NLP engineer, AI engineer, Language technology specialist
Industries: Technology, education, healthcare and customer service
Best for: Students interested in language and AI.
Potential challenges: Many programs combine computer science with linguistics, requiring students to develop expertise across two very different disciplines.
12. Cognitive Science
Cognitive science combines psychology, neuroscience, linguistics and computer science to understand how people think and learn. These insights help improve human-AI interaction.
Common careers: UX researcher, AI product developer, human-computer interaction specialist
Industries: Technology, healthcare and research
Best for: Students interested in both people and technology.
Potential challenges: Because cognitive science is interdisciplinary, students often need to build technical programming skills for AI-specific careers intentionally.
13. Biomedical Engineering
Biomedical engineering applies engineering principles to healthcare technologies while increasingly incorporating AI into diagnostics, imaging and personalized medicine.
Common careers: Biomedical engineer, healthcare data scientist, medical AI specialist
Industries: Healthcare, biotechnology and medical devices
Best for: Students interested in healthcare innovation.
Potential challenges: Students should expect rigorous coursework in biology, chemistry and engineering, along with evolving healthcare regulations that shape AI adoption.
14. Information Systems
Information systems focus on using technology to improve business operations through databases, systems analysis and business intelligence. Many graduates help organizations implement AI solutions.
Common careers: Business analyst, AI consultant, systems analyst
Industries: Business, finance, healthcare and consulting
Best for: Students who enjoy combining business and technology.
Potential challenges: Information systems programs typically emphasize implementation over AI model development, so students interested in technical AI roles may need additional programming experience.
15. Business Analytics
Business analytics teaches students to use data, predictive modeling and AI insights to improve business performance. Coursework combines statistics, visualization and strategic decision-making.
Common careers: Business analyst, analytics manager, AI strategy specialist
Industries: Finance, retail, consulting and technology
Best for: Students interested in using AI to solve business challenges.
Potential challenges: Business analytics focuses on applying AI rather than building it, making technical certifications or additional programming skills valuable for students pursuing engineering-focused AI careers.
