Top 25 AI Degrees: The Best Bachelor’s Programs in the US (2026)

Why Get an AI Degree?

There are many data-backed reasons why you may consider pursuing an AI bachelor’s degree:

• Meaningful workplace differentiator: Comparing similar workers who differ only in AI skills, PwC found that those with the AI skills commanded a 56% wage premium.
• Rapid salary growth potential: The median salary for an AI professional is $160,000, with the top 1% earning over $1,000,000. And wages are rising 2x faster in AI-exposed industries.
• Developing hugely in-demand skills: There is a 35 percentage point gap between AI skills and the availability of talent. Lack of talent (46%) is the single biggest barrier to employer AI readiness, and over 90% of global enterprises risk running into critical skills shortages as early as this year.
• Networking with potential employers: Many of the best AI degree programs (e.g. UC Berkeley) feature internships, co-ops or networking events. These offer brilliant opportunities to make valuable connections. Across all sectors, 62% of internships in the US convert to job offers, rising to 72% for in-person internships.
• Cross-industry appeal: An AI degree doesn’t commit you to working directly in AI. It will make you more employable across all sectors: 88% of all companies report regularly using AI in at least one business function.
• Early adoption: AI is fast becoming ubiquitous, but you can be among the first to emerge with an AI bachelor’s degree. UPenn was the first Ivy League school to offer an AI undergraduate degree, with the first cohort admitted in fall 2024.

What Makes an AI Bachelor’s Degree Program Elite?

Lots of institutions are rushing to introduce AI programs, but quality is highly variable.  

The best AI degrees combine a thoughtful curriculum with expert faculty, institutional prestige, affordable tuition and attainable admissions. In broad terms, the top AI bachelor’s degree programs will feature:

• R1 research status, with faculty on the cutting edge of AI developments
• A purpose-built AI curriculum that does not borrow too heavily from pre-existing courses and programs (as opposed to a hastily-assembled rebrand designed purely to meet growing demand)
• Internship opportunities and/or other collaborations with industry partners
• Elite computing facilities
• Hands-on labs and one or more capstone projects

Any AI degree that combines these features with total resident tuition below ~$50,000 offers a significant value. This guide will take you through the top 25 AI bachelor’s degree programs, taking into account all of the factors that make a program great.

The Different Types of AI Degrees

Before you decide on an AI bachelor’s degree program, it’s vital that you understand the different types of program you may encounter.

Especially in comparison to more established majors, AI is an open-ended topic. Programs range from highly technical (working toward building AI systems) to highly applied (learning how to use AI, often within a particular industry). 

Our list is focused on more technical AI degree programs. You can identify these by looking for courses like:

• Machine learning
• Deep learning
• Computer vision
• Neural networks
• Natural language processing
• Robotics

For example, AI majors at UC San Diego have the choice of all these courses (and 30+ others).

But you may be looking for a non-technical AI degree. This is likely to be the case if you don’t have a background or strong interest in computing, but still want to leverage the AI boom to boost your opportunities and earning potential within a different field.

Examples of less technical AI programs include:

• Artificial Intelligence in Business at Arizona State
• BA in Artificial Intelligence Marketing and Design at Academy of Art University
• AI Governance minor at Lake Forest

Applied AI degrees have their place. The number of early-career job listings asking for AI skills has almost doubled in the last year. Which means the ability to work with AI (as opposed to building it) is a valuable skill in the job market.

But whichever route you take, it is critical that you know what you are signing up for, or else you will set yourself up for dissatisfaction.

AI Degree vs AI Concentration

In this guide and in your own research, you will encounter lots of computer science degrees with an AI concentration, focus, or track. But are these the same thing as “AI degrees”?

Functionally, they may often be essentially the same. But there are some potential differences to be aware of:

AI major

• Highly likely to cover the key AI topics
• You’ll graduate with a degree in AI (very clear to employers)
• May borrow a lot from CS curriculum in practice
• Very new, so limited data on student experiences and outcomes

Computer Science major with AI concentration

• Often covers all of the same AI content as an AI major
• Potentially wider focus
• More data on proven graduate success
• “AI” may not show up on your final diploma

Our top 25 contains both kinds of programs, but each is marked clearly as either an outright AI program or an AI concentration. 

Methodology

In order to rank the top AI degrees, we scored programs based on six weighted factors.

• Top weight (25%) went to hands-on practice, where we rewarded strong internship schemes, real-world projects, and capstone assessments.
• Curriculum relevance comprised 20% of the score for each program. This assessed each AI bachelor’s degree for “2026-readiness”: topics like generative AI and natural language processing, plus the use of modern tools.
• We also gave affordability 20% weighting. We adjusted this to make it a value-for-money indicator: programs judged to be delivering world-class quality were penalized less harshly for high tuition. We also made small regional adjustments to reward strong in-state value.
• Academic quality was worth 15%. Top marks went to institutions with impressive employer reputations and top-tier AI research.
• 10% of the final scores came from focus on AI. This penalized misleadingly named or marketed programs, crediting those with a clear and consistent AI-led curriculum.
• Admissions decided the final 10%. We did not want to unduly punish highly-selective institutions, but highly accessible programs get a slight edge. Credit was also given to practical elements like rolling admissions.

Estimated tuition figures are based on published per-year or per-semester tuition costs, extrapolated across the full four-year degree.

How to Choose an AI Bachelor’s Degree

Your first step in choosing an AI degree is devising a personal plan. That means asking yourself some basic questions:

• Do I want to study in person or get an online AI degree?
• Is there a particular state/region I want to study in?
• Am I looking for a more technical or applied AI degree?

You’ll also need to consider your personal circumstances. Think about the following:

• What is my high school GPA? Do I need to prioritize less selective schools?
• What costs am I able to take on? Will I be eligible for financial assistance?
• Am I looking to study full-time?

Before even getting into the substantive qualities of each AI bachelor’s degree program, these questions are likely to significantly trim down your shortlist.

Further study

While you don’t need to make firm decisions at this stage, you’ll also want to think about whether you may wish to pursue further education after you earn your bachelor’s degree (for example, a master’s in AI). This could impact your choice of program. If you are keen on continuing in academia, you might wish to look at:

• Combined/accelerated programs: Some programs let you take a certain number of courses that count toward both a bachelor’s and a master’s degree, allowing you to earn them quicker and for a reduced overall cost. E.g. Arizona State, which allows students to double-count nine credit hours toward an MS in Robotics and Autonomous Systems (AI).
• Academic-slanted programs: Some programs have more of an academic focus than others, giving more opportunities to conduct independent research. For instance, Columbia is unusual in that it gives you the option of writing an undergraduate thesis.

AI Degree Curriculum

Once you have your shortlist of practically viable candidates, you can start looking in earnest at program content. While there is no definitive “best” AI curriculum, top technical programs tend to share a rough structure along these lines:

Year 1 – Foundations

Focus: Programming, math basics, and computational thinking.

Fall Semester

• Introduction to Programming (Python or Java)
• Calculus I
• Discrete Mathematics I
• Introduction to Computer Systems
• First-Year Writing / Composition

Spring Semester

• Data Structures & Algorithms I
• Calculus II
• Linear Algebra (intro)
• Discrete Mathematics II
• Ethics in Technology (intro)

Year 2 – Core Computer Science & Math

Focus: Building the technical backbone for AI.

Fall Semester

• Data Structures & Algorithms II
• Probability & Statistics for Computing
• Computer Organization / Architecture
• Linear Algebra (advanced applications)
• General Education Elective

Spring Semester

• Software Engineering
• Database Systems
• Theory of Computation (intro)
• Numerical Methods or Optimization
• General Education Elective

Year 3 – Core AI & Machine Learning

Focus: Core AI techniques and applied modeling.

Fall Semester

• Introduction to Artificial Intelligence
• Machine Learning (supervised learning, regression, classification)
• Probability & Statistical Modeling (advanced)
• Human-Computer Interaction
• AI Lab I (project-based)

Spring Semester

• Deep Learning
• Natural Language Processing
• Computer Vision
• Reinforcement Learning (intro)
• AI Lab II (team-based project)

Year 4 – Specialization & Practice

Focus: Advanced topics, research, and real-world application.

Fall Semester

Advanced AI electives, e.g:

• Robotics
• Knowledge Representation & Reasoning
• AI for Healthcare / Finance
• AI Ethics & Policy
• Big Data Systems / Distributed Computing
• Generative AI
• Technical Elective
• Capstone Project I

Spring Semester

• AI Systems Design (deployment, scaling)
• Explainable AI / Trustworthy AI
• Technical Elective
• Internship or Research Practicum
• Capstone Project II

5 Things To Look For In An AI Program

Even if you think you already have a clear choice, it’s worth running through these factors as a final check. The best AI degrees should have:

• Respected faculty: Being taught by faculty who have a track record of publishing AI research is a sure sign of program quality. Courses taught by genuine subject-matter specialists are likely to be AI-led rather than merely AI-branded, and are also more likely to be kept up to date with new industry developments. For example, Danqi Chen is listed on the Princeton faculty page as an associate professor researching machine learning and natural language processing; she has numerous papers published within the last two years, including a 2025 paper on the surprising effectiveness of negative reinforcement in LLM reasoning.
• Opportunities for networking: Programs that include internships are really valuable, not only for providing workplace experience but also for building industry connections. A good example is Rensselaer Polytechnic Institute, where students typically work for a semester and a summer at large firms like IBM, Microsoft, and Google. The best schools also provide other networking opportunities, like the fall and spring career expos at Rice University.
• Careers assistance: Schools should provide help and support in making the next step post-graduation, whether that’s applying for jobs or further study. For instance, USC offers advising appointments, workshops, resume reviews and interview prep, as well as a dedicated jobs board.
• Strong graduate outcomes: Many institutions publish graduate outcomes data. When available, it is very valuable: if an AI degree program is demonstrably producing reliable, well-paid job opportunities, that is clearly a point in its favor. For instance, 94% of 2025 AI graduates from Carnegie Mellon are employed or in continuing education, with an average salary of $145,500.
  • Because so many AI programs are newly-offered, program-level data is rare for the time being, but you can still assess school-wide graduation rates and first destinations. For example, 82% of 2025 Berkeley grads across all majors are in work or continuing education, with a median salary of $100,000.
• Good student reviews: It’s easy to overlook anecdotal evidence when deciding on a program, but reviews and Reddit posts are invaluable. Official material is always bound to be promotional, and can sometimes be out of date. By contrast, sites like Rate My Professors provide honest feedback on current faculty from real students.
  • Searching for your desired program + “Reddit” on Google is likely to bring up relevant and helpful forum discussion. You may even be able to find direct comparisons between schools on your shortlist.

Remember to also consider factors that specifically apply to your individual preferences and circumstances, e.g. flexible admissions, delivery formats, thesis options etc. 

Who Should Get an AI Degree?

As with any bachelor’s degree, most applicants for an AI degree are likely to be recent high school graduates. But there are programs available that suit a variety of different circumstances:

• High-school grads aiming for AI/ML roles: If you’re coming out of high school with ambitions to be a machine learning engineer, AI software developer, or data analyst, an AI degree is your most directly relevant option. Many programs will give you direct industry exposure alongside your study.
• “Traditional” computer science students: With all of the hype, students who may otherwise have leaned toward a computer science major may now be wondering about an AI degree instead. There are plenty of excellent CS majors with a concentration in AI that give you elements of both. Just make sure the curriculum strikes the right balance for what you want to get out of it.
• Career changers: If you want to get into AI but you’re coming from a field with minimal relevance, a bachelor’s degree is an efficient way of gaining expertise, and demonstrating it to potential employers.
• Aspiring academics: AI research is evolving all the time, and some bachelor’s programs give you genuine exposure to that research. 4+1 programs can accelerate your route to a master’s, setting you on a path to a career in academia.
• Career-focused non-technical applicants: While the options on this list are unlikely to be right for you if you don’t have a STEM background or interest, some kind of applied AI degree program may be appropriate in helping you to get ahead in your chosen field.

Do I Need a Degree to Get a Job In AI?

You don’t necessarily need an AI degree in order to get a job in the field. After all, dedicated AI bachelor’s degree programs are relatively new, and the vast majority of current practitioners therefore do not hold AI degrees.

On the other hand, the scarcity of graduates means a bachelor’s degree in AI is currently a super-valuable differentiator. That could change as more and more people earn the degree (which is now offered at over 200 US institutions), but for the time being, it’s a reasonably reliable pathway into a booming field.

Nevertheless, you may prefer to specialize later on in your education with an advanced degree. This can keep your options open. Possible alternative routes include:

• Computer science bachelor’s → AI master’s → job in AI
• Computer science bachelor’s → AI-adjacent graduate role + certifications (e.g. AWS Certified AI Practitioner) → job in AI
• Entry-level tech role → progression to software engineering → job in AI (e.g. ML engineer)
• Non-technical bachelor’s → graduate role → AI self-study + certifications → begin to apply AI within your role → job in AI

But a good AI degree will both develop and certify your AI expertise in a single step, setting you on the most direct path to a career in AI. And if you’ve chosen a good program, employers will respect your degree even if you decide to pursue roles in another sector.