Artificial Intelligence (AI) as an Undergraduate Major: What You Need to Know
Artificial intelligence (AI) tools are being widely applied in nearly every aspect of our modern world. Yet, very few colleges and universities offer a formal AI major or degree. Indeed, according to the National Center for Education Statistics, only ten US institutions are currently offering a formal major in artificial intelligence. The first American institution of higher education to provide an undergraduate degree in AI is my alma mater, Carnegie Mellon University (CMU), whose Bachelor of Science degree in Artificial Intelligence (BSAI) became available to students in the fall of 2018. Beginning in the fall of 2022, students at Massachusetts Institute of Technology (MIT) could pursue a Bachelor of Science in Artificial Intelligence and Decision Making. The most recent institution to announce a formal AI undergraduate degree is the University of Pennsylvania. On February 13, 2024, UPenn’s School of Engineering and Applied Science announced a new Bachelor of Science in Engineering in Artificial Intelligence (BSAI) program, making it the only Ivy League institution to offer a degree in AI.
What do students study when they major in AI?
Typically, an AI degree includes:
Foundational math courses such as calculus, probability and statistics, linear algebra, and discrete math;
Foundational computing concepts, including introduction to programming, data structures, algorithm design, and programming languages;
Advanced computing concepts like robotics, machine learning, signals and systems, big data, optimization and control, symbolic computation, computer graphics, human-computer interaction, decision-making, and vision, perception, and language processing; and
Related electives from the disciplines of ethics, social responsibility, cognitive science, linguistics, psychology, and philosophy.
What are some focus areas within an AI degree?
Students may be asked to focus their studies within the curriculum. Some examples of possible focus areas include:
Robotics: design, construct, and program physical machines capable of performing tasks autonomously or semi-autonomously.
Computer vision: enable computers to interpret and understand visual information from the real world.
Language processing: enable computers to understand human language as it's spoken and written.
Machine learning: develop algorithms which allow computers to learn from data and improve performance over time without explicit programming.
Deep learning: subset of machine learning which uses artificial neural networks with multiple layers to learn intricate patterns in large data sets.
Data and society, including ethics: study of the societal impacts and ethical considerations surrounding the collection, analysis, and application of AI and data-driven technologies.
Cognitive computing: develop systems that mimic human thought processes, such as perception, reasoning, and decision-making.
Neural networks: work with computational models inspired by the structure and function of the human brain, utilized in various AI applications for pattern recognition and decision-making.
Specialized domains such as healthcare, education, or transportation: develop and apply AI techniques tailored to specific industries or fields, such as healthcare (medical diagnosis), education (personalized learning), or transportation (autonomous vehicles).
Do you have to major in artificial intelligence to learn about AI and be prepared for careers in AI?
No! Students can often be extremely well prepared through a typical undergraduate computer science (CS) degree by making course selections to ensure they are learning key AI concepts. Foundational math and computing concepts are not a concern because such courses are required for both CS and AI degrees. While the two degrees -CS and AI- may diverge at the upper levels and outside of core science, engineering, and math classes, in most BSCS degrees, students can choose how to fulfill many of their course requirements. So, a student pursuing a BSCS degree who wants to become an expert in AI should choose these elective courses carefully to include important AI concepts like robotics, machine learning, signals and systems, big data, optimization and control, symbolic computation, computer graphics, human-computer interaction, decision-making, and vision, perception, and language processing. Similarly, they should use the freedom they have to select humanities, arts, and social sciences electives to include courses on ethics, social responsibility, cognitive science, linguistics, psychology, and philosophy.
Indeed, of the ten top AI undergraduate programs, only CMU and MIT offer formal AI degrees. Eight do not: Stanford University, the University of California, Berkeley, Georgia Institute of Technology, the University of Illinois Urbana-Champaign, the University of Washington, Seattle, Cornell University, California Institute of Technology, and the University of Texas Austin!
Some CS programs have a formal track, thread, concentration, or minor to support specialization in AI. For instance, Stanford’s Computer Science department offers ten tracks, one of which is AI. Likewise, GA Tech’s Computer Science department outlines seven threads, one of which is Intelligence. A third example is UT Austin where students can pursue one of seven concentrations, including Machine Learning & Artificial Intelligence. At Case Western Reserve University, BSCS students may elect one of six areas of specialization, one of which is AI. And at the University of Southern California, students can minor in Artificial Intelligence Applications.
What majors besides AI and CS can prepare you to for a career in AI?
Other majors that can similarly prepare you well for a career in AI include:
data sciences
robotics
mathematics and applied mathematics
statistics
cognitive science
electrical engineering
computer engineering
Again, students majoring in something other than AI must choose their coursework carefully to include important AI topics in their studies. If you are considering attending a school to study AI through its computer science, data sciences, robotics, mathematics, statistics, cognitive science, electrical engineering, or computer engineering major, look closely at the major and degree requirements to see how much freedom you would have to choose courses. Also, examine the school’s course catalog to see the available choices and if they include AI-specific options.
Besides CMU, UPenn, and MIT, at what other institutions can students pursue a formal degree in AI?
Dakota State University - Artificial Intelligence (BS) degree through the Beacom College of Computer and Cyber Sciences.
Drake University - Proposed Artificial Intelligence degree through the College of Arts and Sciences (which houses the CS department). Students pursuing an AI degree may choose to concentrate their studies on CS, humanities, or business.
Ferris State University - Artificial Intelligence (BS) degree focussed on business through the College of Business.
Illinois Institute of Technology - Artificial Intelligence (BS) degree through the College of Computing.
Indiana University-Bloomington - Artificial Intelligence (BA) degree through the Luddy School of Informatics, Computing, and Engineering.
Keiser University-Ft. Lauderdale - Artificial Intelligence (BS) degree offered on the Pembroke Pines Campus. It includes concentrations in Machine Learning and Data Science.
Long Island University - Artificial Intelligence (BS) degree through the College of Science. The program is supported by a cutting-edge learning and design center in partnership with Fortune 500 Engineering Company, Dassault Systems.
Purdue University - Artificial Intelligence (BS) degree from the College of Science and an Artificial Intelligence (BA) degree from the College of Arts.
Purdue is worth looking at more closely. At Purdue, there are two options to earn an undergraduate degree in AI: an Artificial Intelligence (BS) degree from the College of Science and an Artificial Intelligence (BA) degree from the College of Arts. The BS in AI degree is a typical AI degree along the lines of those offered by MIT, CMU, and UPenn. The BA in AI degree is offered within the Philosophy Department and “is for people who are science-curious and want to work in a space where innovative technology is developing yet don’t want to engineer the system or people who want to explore the human aspects of machine learning, artificial intelligence, and big data. It is also for the people building the technology and writing the code who recognize their obligation to use technology and data responsibly.” The new major includes eight specialized philosophy courses and five computer science courses (like a CS minor.) Students will gain foundational programming and data analysis skills and a deep understanding of the field's current central ethical and epistemological issues. Purdue also offers a third set of paths to studying and pursuing a career in AI: the BS in Electrical Engineering degree w/ Artificial Intelligence and Machine Learning Concentration and the BS in Computer Engineering degree w/ Artificial Intelligence and Machine Learning Concentration.
What else is valuable to learn when studying AI?
A recent article, Universities Have a Computer-Science Problem, raises good questions about the consequences of placing computer science within an engineering school or making it an independent college, as CMU, GA Tech, MIT, and Cornell have done (in 1988, 1990, 2019 and 2020, respectively) and UC Berkeley recently announced it would do. It argues that computer science students, including those studying AI, should NOT be isolated from other disciplines.
The author, Washington University in St. Louis Professor of Film & Media Studies and Professor of Computer Science & Engineering Ian Bogost, poses a critical question, “whether computing ought to be seen as a superfield that lords over all others, or just a servant of other domains, subordinated to their interests and control?” The answer to this question has important implications for students and, of course, society at large! A stand-alone CS department can define its own requirements, such as the extent to which a student will be broadly educated about the context of their work - or not.
Professor Bogost spoke with Charles Isbell, a former dean of Georgia Tech’s College of Computing and now the provost at the University of Wisconsin-Madison, who warns that “setting up colleges of computing “absolutely runs the risk” of empowering a generation of professionals who may already be disengaged from consequences to train the next one in their image.”
I am reminded that when I was a professor of CS at Wellesley College in the 2000s, CS enrollments were at a nadir. It is hard to believe given the state of CS enrollments now, but the department struggled to fill CS classes, and CS students asked us for help talking with their parents about their choice to major in CS - parents were worried that their children would not have a job upon graduation due to outsourcing jobs to other countries! We created materials about our graduates’ job placement rates for students and the valuable skills learned through a liberal arts education to share with their parents: oral and written communication, critical and quantitative thinking, advocacy, ethical judgment, and working effectively in teams. We reassured them that the technical jobs that required these skills would not be the ones outsourced. Also, these skills would be valuable in any future field, including technical ones. If anyone needs evidence of this, look to MIT’s president, Sally Kornbluth, who has a BA degree in political science from Williams College!
Are there non-technical pathways to contribute to AI?
Absolutely. As AI becomes a more significant part of our society, there will undoubtedly be a massive need for people of all skill sets and academic backgrounds who understand how AI works, its limitations, and how to use AI responsibly. We are already seeing lawyers who specialize in AI, AI ethicists in business and medicine, non-technical AI researchers, experts in using AI for sales, marketing, and social media, AI artists and writers, prompt-engineers, and more.
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In conclusion, there are multiple academic paths to a career in AI, not just a formal AI major. Students interested in studying AI from a technical perspective should not overly focus on the degree itself. Instead, they should focus on the courses they will take and the skills they will acquire through their degree, which will arm them to responsibly contribute to AI confidently, ethically, and with technical competence.
For support crafting a competitive college application for specialized Artificial Intelligence programs, contact Jennifer Stephan at jennifer@lanterncollegecounseling.com
