At Clarkson University, Artificial Intelligence (AI) is no longer confined to a single department or discipline. It is integrated across campus and across the curriculum, reflecting the way AI is reshaping sectors of the global economy — from healthcare and finance to engineering and environmental science. For international students exploring their academic options, one question comes up repeatedly: “Do I need a degree with “Artificial Intelligence” in the title to succeed?”
In most cases, the answer is no.
Understanding the difference between the two main types of AI professionals can help students make informed decisions. AI Developers design and build AI systems. They typically hold degrees in Computer Science, Data Science, Engineering, or related technical fields and have deep mathematical and programming preparation. Meanwhile, AI-Enabled Professionals use AI tools to enhance their work in areas as diverse as engineering design, business analytics, healthcare innovation, environmental modeling, supply chain optimisation, public policy, and research.
Most students fall into this second category. Their careers do not require building AI systems from scratch. Instead, they require the ability to use AI responsibly, critically, and effectively within a professional context.
“In terms of use of AI to conduct your tasks — analytics, writing code, drawing insights — our approach is that it’s a tool. We don’t discourage it,” notes Boris Jukic, Professor of Information Systems and Director of Clarkson’s Master’s in Applied Data Science.
“But our working principle can be summarised in this phrase: human in the loop. When you use AI tools, you don’t use them in a black-box fashion. Ask a question, get an answer, proceed. Make sure you understand what AI generated for you and that it fits what you expect.” AI is built on strong foundations
AI is built on strong foundations
AI is built on academic foundations that universities like Clarkson have been teaching for decades. These include Computer Science, Data Science, Electrical and Computer Engineering, Mathematics, and Software Engineering.
Students in these programmes acquire the technical building blocks that power AI systems: programming and systems design, algorithms and computational theory, machine learning, data modeling and analytics, and linear algebra and probability.
Rather than isolating AI into a single, labelled degree, Clarkson integrates AI and machine learning into its core technical programmes — ensuring students gain both depth and flexibility. University President Michelle Larson recently hosted a campus-wide forum on AI and machine learning focusing on the importance of looking outside one’s own discipline for collaboration and inspiration.
“We try to look at the entire value chain of data science — what happens before, during, and after the analysis of data. That includes data acquisition, processing, management, analysis, visualisation, interpretation, and storytelling. That holistic view is really what sets our programme apart,” says Professor Jukic.
Data science and computer science graduates already power the AI workforce
Across the US, the fastest-growing AI-related roles are rooted in established disciplines and share positive employment potential. According to projections from the US Bureau of Labor Statistics, employment of Data Scientists is projected to grow 35% from 2022–2032. Computer and Information Research Scientists, who often work in AI and advanced computing, are projected to grow 23% over the same period. Software Developers, many of whom work on AI- enabled systems, are projected to grow 25%.
These roles rarely require a degree focused entirely on AI. Instead, based on online job postings, employers consistently seek: strong coding ability (especially Python and related tools), experience with machine learning models, applied data analysis skills, and real-world project portfolios.
Clarkson students in Computer Science, Data Science, and Engineering graduate with exactly these competencies. They also have the internship and research experience needed to demonstrate their readiness for applying AI in the workplace.
“Our students gain experience well beyond theory. They collaborate with faculty on research, partner with industry on applied analytics projects, and complete internships where they solve meaningful problems using AI tools,” says Michelle Crimi, Dean of the Graduate School and Interim Vice Provost for Research & Technology. “Employers consistently tell us that Clarkson graduates stand out because they understand both the technical foundations and the practical application of AI.”
The global market rewards skills
For international students and families, degree titles can feel important. But employers assess capability, not branding. They evaluate whether you can identify problems, apply appropriate technologies, and deliver results. A Computer Science graduate with machine learning research and internship experience may be more competitive than a student whose diploma simply reads “Artificial Intelligence” but lacks applied practice.
At Clarkson, students build strong technical foundations while gaining opportunities to specialise. So rather than focusing on programme names, students and their families should explore whether a curriculum includes machine learning and advanced data coursework, research opportunities with faculty working in AI applications, internships with technology- driven companies, and chances to participate in applied technical projects. The bottom line is that success in an AI-driven economy depends less on a programme’s title and more on technical rigour, practical experience, and the ability to apply AI responsibly within a discipline.
At Clarkson University, that preparation is embedded across programmes –– equipping tomorrow’s professionals to build systems, apply them thoughtfully and adapt quickly as technologies evolve.
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