Drexel University in Philadelphia has announced a new Bachelor of Science in Artificial Intelligence and Machine Learning, with the first cohort scheduled to begin in fall 2026. The program places Drexel among a small but expanding group of universities offering dedicated undergraduate AI majors, an academic category that has only recently emerged as a distinct discipline despite AI鈥檚 rapid transformation of nearly every industry. For prospective students considering computer science, data science, or engineering pathways, the new degree offers a focused alternative that combines deep technical training with the cross-disciplinary application work the field demands.
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The program is led by Drexel鈥檚 College of Computing & Informatics (CCI) in collaboration with the College of Engineering (CoE) and additional academic units across the university. The interdisciplinary structure reflects how AI actually operates in practice: as a technology that touches health care, manufacturing, infrastructure, the arts, business, and the natural sciences simultaneously, requiring practitioners who can apply technical skills across domain contexts.
Why a Dedicated Undergraduate AI Degree Now
Until recently, students interested in AI pursued the field through computer science majors with AI electives, through data science programs that touched on machine learning as one tool among many, or through specialized graduate study after completing a more general undergraduate degree. The standalone AI undergraduate major is a newer academic concept, reflecting AI鈥檚 evolution from a research subfield within computer science into a foundational technology shaping work across the modern economy.
Drexel鈥檚 decision to launch a dedicated AI/ML undergraduate degree puts the university alongside top research institutions developing similar programs. A focused AI major differs meaningfully from a computer science major with AI electives: the dedicated program provides a deeper sequence in machine learning theory, intelligent algorithms, neural network architectures, and the mathematical foundations of modern AI, sequenced from the start of the student鈥檚 undergraduate path rather than added through senior-year electives.
鈥淲e think it鈥檚 a perfect time to introduce a degree that focuses primarily on this area. The idea of a standalone AI degree is relatively new, but it reflects the reality of how AI has evolved to have an impact on every discipline.鈥� (Dario Salvucci, Professor of Computer Science and faculty member shaping the new program)
Program Design: Technical Depth with Interdisciplinary Reach
Drexel鈥檚 AI/ML undergraduate program is designed around three structural priorities. First, technical depth in the core AI and machine learning curriculum: students complete a sequence covering machine learning fundamentals, intelligent algorithms, systems design, and the mathematical and statistical methods that underlie modern AI. Second, ethics and responsibility woven throughout the curriculum rather than treated as a single course: data ethics, the social aspects of AI, transparency, explainability, and the responsibility framework for AI-driven systems are core elements of the program design. Third, flexibility for students to apply AI tools in their second area of interest, whether that is natural sciences, media, design, business, engineering, or another field where AI is reshaping practice.
AI is not a self-contained discipline operating in isolation; it is a set of tools applied to problems in other fields. A graduate who can apply machine learning to medical imaging, build NLP systems for legal document analysis, develop AI-driven design tools, or deploy predictive models for supply chain operations is positioned differently from a graduate trained narrowly on AI theory without exposure to applied contexts. Drexel鈥檚 program intentionally builds in this applied flexibility through electives and crossover course pathways.
Curriculum elements students can expect
Based on the program announcement and on Drexel鈥檚 existing AI/ML research and graduate programs, students enrolling in the new BS can expect coursework across foundational mathematics for AI (linear algebra, probability, statistics, optimization), programming and software engineering for AI systems, machine learning algorithms (supervised, unsupervised, reinforcement learning), deep learning and neural network architectures, natural language processing, computer vision, intelligent agents and systems design, data ethics and responsible AI, and applied AI in one or more crossover domains chosen by the student through electives.
Crossover areas the program anticipates include human-AI interaction (designing systems that work alongside human operators), AI hardware systems (the physical computing infrastructure that makes large-scale AI possible), and applied AI in specialized fields such as biomedical engineering, media, finance, or natural sciences. Students will shape their elective sequence around their secondary interest while maintaining the core AI/ML technical foundation.
Leadership Perspective: An Interdisciplinary Vision
The new degree is the product of close collaboration between two of Drexel鈥檚 largest academic units, CCI and the College of Engineering. The interdisciplinary structure also previews a larger institutional restructuring: in May 2025, Drexel鈥檚 Faculty Senate approved a proposal to combine CCI, CoE, and the School of Biomedical Engineering, Science and Health Systems into a single academic unit. The new AI/ML degree is described by university leadership as a model for the kind of cross-departmental program that the integrated unit will be designed to support and expand.
鈥淭he new AI and Machine Learning degree exemplifies the kind of forward-looking, interdisciplinary collaboration that will define the future of higher education. By integrating foundational engineering principles with advances in computing and insights from across the University, we are equipping students to lead in a world where AI touches nearly every field, from health care and infrastructure to advanced manufacturing and the arts. This program reflects our shared commitment to preparing graduates who are not only technically proficient but also ethically grounded and impact driven.鈥� (Kapil R. Dandekar, Interim Dean of the College of Engineering)
鈥淎I and ML are now part of the everyday toolkit in many professions. These aren鈥檛 just technologies for computer scientists but doctors, lawyers, artists 鈥� they are all using machine learning to analyze data, make predictions, and support decisions. That鈥檚 why we built this program to connect with other disciplines. We want to be one of the first universities to pioneer this discipline.鈥� (Ali Shokoufandeh, Interim Dean of the College of Computing & Informatics)
The Drexel Advantage: Existing AI Research Strength and the Co-op Program
Drexel did not arrive at AI undergraduate education from a standing start. The university has been conducting AI research and education for many years, with a well-established research portfolio in artificial intelligence, machine learning, and robotics housed in the College of Computing & Informatics. The existing research program covers algorithms and applications including computer vision and pattern recognition, data science, knowledge representation, cognitive modeling and neural networks, image and signal processing, predictive modeling and statistical learning, and applied work in explainable AI, fairness in AI, and natural language processing. The full research portfolio is documented on .
The depth of existing AI research at Drexel means undergraduate students entering the new program will have access to faculty actively conducting funded research in the field, not just teaching from textbooks. In 2024, Drexel was selected by the U.S. National Science Foundation as one of the first cohort of recipients under the National AI Research Resource Pilot program. The project, “Neuro-inspired Oversight for Safe and Trustworthy Large Language Models,” is led by Edward Kim, an associate professor in CCI, and Matthew Stamm, an associate professor in the College of Engineering. The research applies brain-inspired machine learning algorithms to improve transparency and oversight of large language models like ChatGPT, with the goal of ensuring AI outputs are accurate, unbiased, and internally moderated by the model鈥檚 own behavioral control mechanisms. This is precisely the kind of research that informs the curriculum students will encounter in the new undergraduate program.
Drexel鈥檚 co-op program
Beyond the research strength, Drexel offers something few peer institutions can match at scale: one of the largest and oldest cooperative education (co-op) programs in the United States. Drexel鈥檚 co-op model integrates full-time professional work experience into the undergraduate degree, with students alternating periods of academic coursework with six-month co-op placements at participating employer partners. For an undergraduate AI/ML major, the co-op program means students can spend a substantial portion of their undergraduate years working as applied AI practitioners in industry, government, or nonprofit settings before they graduate.
Modern AI/ML practice depends heavily on hands-on experience with real datasets, production-scale infrastructure, and the messy applied problems that classroom projects cannot fully replicate. Co-op placements let undergraduate AI students build genuine professional portfolios alongside their academic credentials, which strengthens both graduate school applications and direct-to-industry employment prospects. Drexel鈥檚 co-op partners include technology companies, pharmaceutical and biomedical research organizations, financial services firms, healthcare systems, government agencies, and nonprofit research institutions across the Philadelphia region and beyond.
Philadelphia location and the regional tech ecosystem
Drexel鈥檚 University City campus places students in the heart of Philadelphia鈥檚 university research corridor, immediately adjacent to the University of Pennsylvania and within a regional ecosystem that includes substantial pharmaceutical and biotechnology research (Merck, GSK, and other major employers within commuting distance), healthcare informatics (the Children鈥檚 Hospital of Philadelphia and Penn Medicine鈥檚 extensive health data infrastructure), financial services (Vanguard鈥檚 headquarters and a substantial regional financial industry), and a growing AI startup community in the Greater Philadelphia metro. For undergraduate AI students, the location means co-op placements and post-graduation employment options exist at scale within a short commute, alongside access to the broader Northeast Corridor tech and research economy.
Career Prospects: AI Workforce Demand Across Industries
The labor market context for graduates of dedicated AI undergraduate programs is structurally favorable. AI and machine learning roles have expanded across nearly every sector of the economy over the past five years, with hiring demand growing fastest in healthcare and biomedical applications, financial services, technology platforms, pharmaceutical research, manufacturing and supply chain optimization, and public sector applications.
Graduates of a focused AI/ML undergraduate program can enter several career trajectories: direct-to-industry roles as AI/ML engineers, data scientists, applied machine learning engineers, or AI research engineers at technology companies, financial services firms, healthcare organizations, and consulting practices; applied AI roles where AI tools serve specific domain problems (biomedical informatics specialist, AI-driven product manager, computational biologist, AI-augmented designer); or graduate school pathways in AI, machine learning, computational biology, or related research fields.
Compensation in AI-aligned roles has tracked the broader tech sector, with entry-level AI/ML engineer salaries in major metropolitan markets typically running well above general-track computer science roles. Remote work has become standard for many AI/ML roles in technology and financial services, expanding the addressable employment market beyond a graduate鈥檚 local region. For students whose interests fall in healthcare, biomedical, or pharmaceutical applications, the Philadelphia regional ecosystem provides especially strong post-graduation employment density.
Who Should Consider Drexel鈥檚 New AI/ML Degree
Drexel鈥檚 new BS in Artificial Intelligence and Machine Learning is designed for several distinct prospective student profiles.
Students focused specifically on AI
Prospective undergraduates who already know they want to specialize in AI and machine learning, rather than enter a broader computer science program and add AI through electives, are the primary audience. The program provides earlier, deeper AI-specific coursework than a general CS major and supports graduate school applications and specialized career trajectories more directly. Strong high school preparation in mathematics (calculus, statistics) and computer science (programming experience) provides a useful foundation.
Students drawn to applied AI in another field
Students who want to apply AI to a specific domain (medicine, biology, design, business, the arts, engineering, or natural sciences) will find the program鈥檚 flexibility well-suited to that goal. The elective structure permits significant exploration of a secondary discipline alongside the core AI/ML curriculum, which prepares graduates for the applied-AI roles where the strongest demand growth is occurring.
Students drawn to co-op and ethics-integrated AI education
Drexel鈥檚 co-op program is one of the strongest reasons students choose Drexel over peer institutions, providing substantial applied work experience before graduation. The program鈥檚 integration of data ethics, AI fairness, transparency, and responsible AI throughout the curriculum, rather than as a single afterthought course, reflects the reality that AI ethics decisions happen at every layer of system design. Students who want hands-on industry experience combined with a grounded understanding of responsibility frameworks will find Drexel鈥檚 program design well-aligned with that goal.
Applying to Drexel鈥檚 New AI/ML Program
The new BS in Artificial Intelligence and Machine Learning is scheduled to begin accepting students for fall 2026. Prospective applicants should monitor Drexel University鈥檚 undergraduate admissions website for application timelines, requirements, and cohort enrollment details as the inaugural class application cycle approaches. The official program announcement is documented on , and broader information about the College of Computing & Informatics is available on .
Preparing a strong application
As the inaugural cohort of a new program, the fall 2026 admissions cycle is expected to draw attention from prospective AI students nationally. Strong applications typically demonstrate mathematics preparation (calculus and ideally beyond), programming experience (Python familiarity, project work, or contributions to open-source projects where applicable), and a clear articulation of why the applicant wants to study AI specifically rather than computer science more broadly. Project work demonstrating curiosity about AI applications (Kaggle competitions, personal machine learning projects, contributions to community AI initiatives, or relevant high school coursework) can strengthen applications meaningfully.
Financial aid and scholarships
Drexel offers institutional merit scholarships and need-based financial aid to undergraduate applicants, with specific aid packages determined through the standard FAFSA and CSS Profile process. For prospective AI/ML students, the combination of federal aid, institutional aid, and Drexel鈥檚 co-op program (which provides paid employment during co-op terms, offsetting living costs and reducing borrowing requirements) produces a financial profile that students should evaluate alongside the program鈥檚 academic offerings. Prospective students should engage Drexel鈥檚 admissions and financial aid offices early in the application process to understand the full picture of aid options.
A Program Built for the AI Era
Drexel鈥檚 new Bachelor of Science in Artificial Intelligence and Machine Learning launches at a moment when undergraduate AI education has finally caught up with the field鈥檚 actual centrality in modern work. For prospective students who want to build their undergraduate years around AI as a discipline rather than as an elective topic, who want technical depth combined with applied flexibility across multiple domains, and who want the structural advantage of Drexel鈥檚 established co-op program and existing AI research infrastructure, the program offers a strong fit. The fall 2026 inaugural cohort will be the first to experience Drexel鈥檚 integrated approach to AI undergraduate education.
鈥淭his will be a catalyst to attract top talent and establish Drexel as a hub for responsible, innovative AI education.鈥� (Ali Shokoufandeh, Interim Dean of the College of Computing & Informatics)
Prospective students, families, and counselors can learn more about the new BS in Artificial Intelligence and Machine Learning through and the broader resources available through .
