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MASTER OF SCIENCE IN ARTIFICIAL INTELLIGENCE
Master of Science
MASTER OF SCIENCE IN ARTIFICIAL INTELLIGENCEPurdue's innovative Master of Science in Artificial Intelligence prepares professionals for success in today's tech-focused job market – and it gives them the skills they need to meet the demands of the future. Whether you're interested in the technical capabilities, implementation, and ethics of AI, or just someone new to the world of AI, this program provides foundational AI knowledge applicable to many different career paths – from engineering to business, communication, and more. This fully online program leverages the expertise and research excellence of Purdue University's West Lafayette faculty and academic community. Select from two different tracks tailored towards your specific interest. The AI and Machine Learning major focuses on programming, computer science, and mathematics. For those without a coding background, the AI Management and Policy major focuses on leadership, management, ethics, and policy.
This program does not require an application fee, so you can apply for free. Learn more about Purdue University's Master of Science in Artificial Intelligence by connecting with our Enrollment Counselors.
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60+ Industries in Demand $149k Average Salary 45,081 Unique Job Postings 60+ Industries in Demand $149k Average Salary 45,081 Unique Job Postings prev next Explore the Majors AI and Machine Learning The AI and Machine Learning major teaches technical skills in AI, with a focus on topics such as programming, machine learning, data mining, language processing, statistics and more. Learners in this major are expected to have a programming background and prior experience with calculus, linear algebra, and probability theory.
AI Management and PolicyFor those without a coding background, the AI Management and Policy major teaches the business and management aspects of AI. This major focuses less on technical skill and more on developing a foundational understanding of AI and its implications. Learners in this major will take classes on topics like risk management, communication, leadership, marketing, and data literacy. All learners in this major must have at least 24 months of relevant work experience.
Latest in AI research
Watch Video Improving life and wellness through innovation To face the world's most pressing challenges, Purdue University has committed its signature strengths in science and technology to creating a leading research and education program in physical AI — the application of artificial intelligence to our physical world.
Here's just a sample of the work coming out of the many hubs across campus.
Petros Drineas
Petros Drineas is a Professor and Associate Head in the Computer Science Department of Purdue University. He earned a PhD in Computer Science from Yale University in 2003 and a BS in Computer Engineering and Informatics from the University of Patras, Greece in 1997. Drineas' research interests lie in the design and analysis of randomized algorithms for linear algebraic problems, as well as their applications to the analysis of modern, massive datasets. He is the recipient of an NSF CAREER award and was a Visiting Professor at the Sandia National Laboratories during the fall of 2005, a visiting fellow at the Institute for Pure and Applied Mathematics at the University of California, Los Angeles in the fall of 2007, and has also worked for industrial labs (including Yahoo! Research and Microsoft Research).
Chris Clifton
Chris Clifton is an Associate Professor of Computer Science at Purdue University. He has a Ph.D. From Princeton University, and Bachelor's and Master's degrees from the Massachusetts Institute of Technology. His research interests include data mining, data security, database support for text, and heterogeneous databases. Dr. Clifton works on challenges posed by novel uses of data mining technology, including data mining of text, data mining techniques applied to interoperation of heterogeneous information sources, and security and privacy issues raised by data mining. Prior to joining Purdue, Dr. Clifton was a Principal Scientist in the Information Technology Division at the MITRE Corporation.
Alan J. Zillich
Dr. Zillich graduated with a BS and Doctor of Pharmacy (PharmD) from Purdue University. Then, he completed four years of post-graduate training. First, at the University of Kentucky, he completed a general practice (PGY1) residency and a specialty residency in ambulatory care (PGY2). Then, he completed a health outcomes research fellowship at the University of Iowa Colleges of Pharmacy and Medicine. He has served on NIH and VA grant review panels and provided leadership on the American College of Clinical Pharmacy Research Institute Board of Trustees. He has published more than 110 peer reviewed publications and been awarded more than 4 million dollars in research grants.
Ankita Raturi
Ankita received her PhD in Software Engineering from the Department of Informatics at the University of California, Irvine, where she took a human-centered approach to the design of a Modeling Sustainable Systems (MoSS) framework for representing complex, adaptive, agricultural systems. From 2018-2019, Ankita was with the Sustainable Agricultural Systems Lab at USDA ARS and the Center for Environmental Farming Systems at NC State. Ankita leads the Agricultural Informatics Lab, focused on human-centered design, information modeling, and software engineering, for increased resilience in food and agricultural systems
Cherie Maestas
Cherie D. Maestas, is Professor and Head of the Department of Political Science at Purdue University. Previously she served as the Marshall A. Rauch Professor of Political Science and Director of the interdisciplinary Public Policy Program in the College of Liberal Arts and Sciences received her PhD from the University of Colorado, Boulder in 2000. Dr. Maestas received her PhD from the University of Colorado, Boulder in 2000. Her current research examines how emotions and personal characteristics (e.G. Emotion regulation, personality traits, political predispositions) moderate the effects of media messages and policy framing.
Kyle Hultgren
Kyle Hultgren is the founding director of the Purdue University College of Pharmacy's Center for Medication Safety Advancement. Dr. Hultgren is a co-author of a process improvement program in partnership with Purdue University and the United States Veterans Health Administration that has been provided to thousands of professionals in health systems nationwide. His current research includes extensive work on dashboards and measurement systems for evaluating and improving medication use systems and integration of publicly available social media data as potential surveillance signals. As a result of this work, he led his Center through the creation of SafeRx, a large, curated, relational database containing millions of adverse drug events.
Stephen Leitch
Stephen Leitch, PhD is a Clinical Assistant Professor at Purdue University, teaching business classes in Consumer Science and Hospitality Management. His teaching and research interests cover revenue management, marketing, and big data. He has won several innovation in teaching awards and holds strong ties to industry and the business community. He is also the Assistant Director in the Centre for Hospitality & Retail Industries Business Analytics at Purdue, speaker at many events, reviewer for several top journals and consultant on various industry projects. In addition to academics, Stephen founded and sold several retail/ecommerce businesses and is Executive Director for Millie's Mission, a not-for-profit group of stores in Indiana.
Bryce Dietrich
Bryce Dietrich is an Associate Professor in the Department of Political Science and research scholar at the Center for C-SPAN Scholarship and Engagement (CCSE). His research uses novel quantitative, automated, and machine learning methods to analyze non-traditional data sources such as audio (or speech) data and video data. Professor Dietrich's work has appeared in the Nature, American Political Science Review, Journal of Politics, Political Analysis, and Political Psychology. This work has also received grant support from major organizations, including the National Institutes of Health and the National Science Foundation. He received his Ph.D. From the University of Illinois and was recently a postdoctoral research fellow at Harvard's Kennedy School and Northeastern University.
Dilip Chhajed
Dilip Chhajed serves as the Associate Dean for Online Programs and Strategic Innovations and as the Executive Director of the BS in Integrated Business and Engineering program. He spearheads the development and management of all online programs at the Daniels School of Business. Currently, the portfolio of online programs at DSB encompasses an MBA, MS in Business Analytics, MS in Global Supply Chain Management, MS in Human Resource Management, MS in Economics, and nine Graduate Certificates. Chhajed obtained his PhD from Purdue University, an MS from UT-Dallas, and a B.Tech from IIT Bombay. He has had the privilege of teaching at the University of Warsaw in Poland, Sogang University in South Korea, Purdue University, and GISMA in Hanover, Germany.
Daniel Schiff
Dr. Schiff is an Assistant Professor of Technology Policy at Purdue University's Department of Political Science and the Co-Director of GRAIL, the Governance and Responsible AI Lab. Dr. Schiff studied Philosophy at Princeton University, focusing on robotics and intelligent systems, before completing a Master's in Social Policy at the University of Pennsylvania and PhD in Public Policy from the Georgia Institute of Technology. As a policy scientist with a background in philosophy, he studies the formal and informal governance of AI through policy and industry, as well as AI's social and ethical implications in domains like education, manufacturing, finance, and criminal justice.
Kaylyn Schiff
Kaylyn Schiff is an Assistant Professor in the Department of Political Science at Purdue University and Co-Director of the Governance and Responsible AI Lab (GRAIL). Dr. Schiff studies American politics and policy, with a focus on quantitative and experimental methods. Her research addresses how citizens share information with government and examines the drivers of policymaker and bureaucrat responsiveness to citizen input. Kaylyn received her Ph.D. And M.A. In Political Science from Emory University and completed a B.A. In Public Policy from Princeton University and an M.Ed. From Fordham University. For the 2022-2023 academic year, she was a Postdoctoral Associate with the Institution for Social and Policy Studies at Yale University.
Corey Maley
Corey Maley is an associate professor of philosophy, coming to Purdue from the University of Kansas. He spent some years as an undergraduate at the University of Nebraska, quadruple-majoring in computer science, mathematics, philosophy, and psychology, and getting both a B.S. And a B.A. For all that effort. He then worked in a cognitive neuroscience lab at Washington University in St. Louis, where he learned to do real science, as well as appreciate that his talents primarily lie in being a philosophical consumer—rather than producer—of science. He then went to graduate school at Princeton University, getting his Ph.D. In the Logic and Philosophy of Science program in the philosophy department. Corey's primary focus is on foundational issues in the philosophy of computation and the sciences that use computation as a conceptual framework for understanding other phenomena.
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Healthcare Embraces GenAI For EMRs, But Legal And Ethical Questions Loom
A scoping review of generative artificial intelligence (GenAI) applications in electronic medical records (EMRs) has mapped the current landscape of innovation, highlighting promising use cases while raising urgent questions about safety, ethics, and integration. Conducted by researchers at McMaster University and published in the journal Information, the review "Applications of Generative Artificial Intelligence in Electronic Medical Records: A Scoping Review" explores how GenAI is being integrated into healthcare's digital backbone.
Analyzing 55 peer-reviewed studies, the review categorizes GenAI applications into five major themes: data manipulation, patient communication, clinical decision-making, clinical prediction, and summarization. Data manipulation was the most common use case, with 24 studies demonstrating GenAI's ability to extract and synthesize information from unstructured clinical notes, including tasks like cancer symptom identification and HIV patient detection. The review found that GenAI tools, particularly large language models (LLMs) such as ChatGPT, significantly outperformed manual methods in both speed and scalability.
Patient communication emerged as another key application, with GenAI-generated responses showing strengths in empathy and clarity across nine studies. Some research even found these AI-generated messages to be preferred over those written by physicians, though concerns around personalization and factual accuracy remain. Clinical decision-making and prediction featured prominently in eight studies each. While some models demonstrated capabilities on par with physicians in diagnosing and predicting hospital or ICU admission, others showed poor performance in emergency or medication-dosing scenarios, with instances of unsafe recommendations and hallucinated outputs.
Summarization was the least-studied but still promising category, appearing in four studies focused on making discharge summaries and radiology reports more accessible. In two additional studies classified as 'other,' researchers explored GenAI's utility in generating referral letters and assessing healthcare disparities, underscoring its breadth of potential use cases beyond traditional EMR functions.
The review found that ChatGPT was the most frequently evaluated model, appearing in over half the studies, followed by other commercial and proprietary models such as Claude, Microsoft Co-Pilot, Vicuna, and BERT-based variants. Many studies relied on publicly available datasets like MIMIC-III and MIMIC-IV, but others incorporated institution-specific records, highlighting the variability in data sources.
Performance evaluations varied widely. For example, in seizure prediction and rare disease phenotyping, GenAI models demonstrated superior accuracy compared to structured-data-only algorithms. However, in decision-support tasks like triage or renal dosing, GenAI systems struggled, underscoring the need for contextual understanding and robust safeguards.
The study argues that EMRs are a natural entry point for GenAI integration due to their text-heavy format. GenAI's ability to process large volumes of unstructured data, extract clinical insights, and communicate with patients offers new opportunities for workflow optimization and burden reduction. However, the authors caution that many applications remain in proof-of-concept stages. Trust deficits, safety concerns, legal ambiguities, and interpretability issues continue to limit deployment in real-world clinical settings.
Ethical considerations feature prominently in the study. Key risks include breaches of patient confidentiality, AI hallucinations, overreliance by clinicians, and embedded biases in training data. As EMR-integrated GenAI evolves, unresolved questions about accountability, regulatory oversight, and equitable access are increasingly pressing. The authors emphasize that while GenAI can support clinical workflows, it should not be treated as a substitute for medical judgment.
Limitations of the review include geographic skew. 63% of included studies originated in the United States, as well as methodological diversity and small sample sizes in many studies. Additionally, as GenAI models evolve rapidly, some findings may already be outdated. Most of the studies evaluated models that were commercially available, limiting insight into domain-specific, medically trained systems. The review did not include a formal critical appraisal due to the preliminary nature of the field.
Despite these limitations, the review points to a significant shift in how AI can enhance EMRs. From zero-shot data extraction to hybrid NLP-LLM frameworks for rare disease detection, GenAI offers scalable alternatives to traditional rule-based systems. Yet integration must proceed cautiously. Concerns around explainability, safety, and bias require rigorous validation, multidisciplinary collaboration, and continuous human oversight.
Project Governance For Defense Applications Of Artificial Intelligence: An Ethics-Based Approach
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