In recent years, higher education institutions have been exploring uses of machine learning and artificial intelligence (AI) for student success analytics, and with improvements in technology and algorithms, chatbots continue to gain popularity after Georgia State University famously used one to help reduce summer melt. But now chatbots are being used to help students with questions about areas such as financial aid, IT services, and library services and are continuing to expand to new areas of support. Machine learning and AI are also being tested as digital tutors such as OpenStax Tutor. But these are not the only areas that are seeing the expanded use of machine learning and AI in higher education. Faculty, researchers, and students are incorporating machine learning and AI in many disciplines at higher education institutions.

In the summer and fall of 2020, EDUCAUSE and HP partnered on a research project to explore how machine learning and AI are being employed by researchers across higher education. In particular, this research project investigates the types of machine learning and AI technologies—both hardware and software—that researchers across different disciplines are employing as they design and conduct their research. Additionally, this research explores the methods and practices that IT managers and departments are utilizing to develop processes and infrastructure to support the researchers at their institutions, especially those who are just beginning to explore how machine learning can improve their research.

The classic machine learning domains of computer science and statistics are continuing to push the boundaries of current knowledge, use, and application of machine learning and AI. But exciting new work is incorporating machine learning and AI into fields such as protein engineering, digital art, computational biology, civil engineering, and many more. Institutions are also reporting more interest in and need for additional courses in machine learning for undergraduate students, while faculty are reporting higher application rates for master's and PhD programs that involve machine learning.

Because machine learning technology and methods are being used by so many different groups, IT departments need to ensure they have methods and plans to coordinate with their users across the institution and properly prepare them for entry into use of machine learning. IT departments are discovering some new challenges and are developing practices to ensure they are prepared to support researchers as they work to understand the needs of the growing applications of machine learning.

• The use of machine learning is not limited to computer science and statistics. Researchers are beginning to explore how machine learning can improve research across many different disciplines in engineering, life sciences, and the humanities as they gain access to new and larger datasets.
• Not all machine learning users are created equal—they have different technical ability levels. As the number of disciplines and faculty engaging in machine learning expands, many more researchers can be found who are just starting to learn how to use machine learning in their field of study.
• Building communication lines between IT and researchers is key for effective machine learning support. Both IT and researchers yield better outcomes with fewer resources when they communicate early and often about needs and goals.
• Machine learning is costly and requires substantial support. Institutions often lack the internal resources for supporting machine learning. As a result, funding through grants and other external sources is often necessary for both researchers and IT to obtain modern machine learning hardware.
• Institutions are working to lower the barrier of entry to machine learning. As more and more researchers from backgrounds other than computer science are exploring machine learning, institutions are developing training and new resources to help make machine learning more available for the uninitiated.