As faculty, researchers, and students turn to machine learning and AI across disciplines, best practices have emerged

5 developments around machine learning and AI in higher-ed research


As faculty, researchers, and students turn to machine learning and AI across disciplines, best practices have emerged

Using machine learning and AI in research is not limited to computer science and statistics–researchers in higher education are using it across a variety of disciplines, including life sciences and the humanities, according to new research.

The higher-ed community has been more proactive in exploring how machine learning and artificial intelligence (AI) can be used by researchers across higher education, a new EDUCAUSE report notes.

A partnership between EDUCAUSE and HP explores the types of machine learning and AI researchers use as they design and conduct their research. The partnership also looks into the methods and practices IT managers and departments use to determine the processes and infrastructure that best supports researchers at their institutions.

“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,” writes author Sean Burns, a corporate researcher at EDUCAUSE. “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.”

Laura Ascione