Taylor Launches New Bachelor's Degree in AI and Machine Learning

When artificial intelligence became the defining conversation in higher education, Taylor University was not scrambling to catch up. It was already there.

Taylor's Computer Science & Engineering Department has been engaged with AI since the mid-1980s. Faculty members have taught courses in introductory AI, expert systems, machine learning, computer vision, and natural language processing across four decades and multiple cycles of the technology's rise and retreat. This fall, that long commitment will take a new form: Taylor is launching a Bachelor of Science in Artificial Intelligence and Machine Learning, a dedicated major designed to prepare students not just to work in AI but to lead its development with technical depth, ethical clarity, and a distinctly Christian understanding of what the technology should—and should not—do.

"AI isn't new," said Dr. Art White, Computer Science & Engineering professor emeritus who taught at Taylor for 39 years. "In the mid 1980s, the Computer Science Department was experimenting with expert systems, and machine learning was first taught at Taylor in the early 1990s. In fact, many of the foundational machine learning concepts were present in the 1990s. Eventually, the prevalence of the Internet and GPUs provided data and computing power and opened up doors to do even more.”

That history is precisely the point. Where other institutions have rushed to build academic AI programs in response to the sudden visibility of large language models, Taylor's program emerged from two years of deliberate conversations among faculty, board members, donors, alumni, and external advisors such as Pat Gelsinger, current CEO of Gloo and former CEO of Intel. The result is a program shaped by people who have watched AI evolve across decades, not one assembled to meet a moment.

The new major requires 90 credit hours and builds from a rigorous core in computer science and mathematics into specialized, upper-level work in machine learning, data science, and AI systems. New courses include a two-semester machine learning sequence with integrated labs that give students hands-on time working with complex data sets and building models from scratch. A new Foundation Models and Generative AI course puts students directly in conversation with the technology reshaping entire industries. A two-semester research sequence pairs students with faculty mentors for sustained investigation into open questions in the field.

Grounded in Christian Ethics

Ethics is not an elective. It is a requirement.

That is not incidental to the program. Taylor's approach to AI asks students to wrestle with questions that go beyond code: what AI can do, what it cannot do, what it should not do, and how it might serve purposes beyond productivity. One framing that has shaped the program's development is whether AI could become a tool for accelerating the Great Commission—reaching more people with fewer resources, in more places, with greater care.

Among other Christian peer institutions, Taylor's program is the most AI-specialized. Taylor's major dedicates more of its required credit hours to AI and machine learning coursework and is the only one to require a two-semester research sequence. The required Computational Linear Algebra course, built specifically for the program, addresses a gap in AI training across peer institutions by integrating the mathematical underpinnings of machine learning algorithms directly with computational implementation.

Against secular comparisons, including programs at Carnegie Mellon, UC San Diego, Purdue, Penn State, and Illinois Institute of Technology, Taylor's AI-specific depth is competitive. The Foundation Models and Generative AI course is not required at any of the five secular programs reviewed. The two-semester research sequence matches Penn State’s senior design project in duration.

Taylor has held an AI concentration within its computer science major for years. This new degree is the next step: a standalone major with increased rigor, additional coursework, and a clearer line of preparation for students headed into AI research, software development, data science, or graduate study in the field.

"Anyone can learn to use an AI tool. We're preparing students to build the next one,” Dr. Fola Ayano, assistant professor of Computer Science, said. “Our curriculum takes students beneath the surface into the mathematics, architecture, and algorithmic design that power modern AI, so they graduate with a deep understanding of how these systems are constructed, not just how they're applied. They'll leave Taylor with the skills to build intelligent systems from the ground up and the ethical grounding to build them responsibly."