Machine teaching uses sophisticated mathematics to allow researchers to model actual human students and devise the best possible lessons for teaching them. Rather than dealing with pools of data and not knowing at the outset what patterns might be revealed through analysis, the researcher in a machine teaching arrangement already knows what knowledge he or she wants to impress upon the learner. Machine teaching turns this concept on its ear. Practical tasks like speech recognition are aided by machine learning. The goal of machine learning is to develop models that will prove useful in the future when dealing with large, often unwieldly data sets. The machine learner (the computer) is like a student. Machine learning is a well-established subfield of computer science in which experts develop mathematical tools to help computers learn from data and detect patterns. “It will give us optimal, personalized lessons for real, human students.” It’s quite different from how people usually think about education,” says Zhu. “My hope is that machine teaching has an impact on the educational world. Their collaborative research aims to break new ground in what computer scientist Jerry Zhu calls “machine teaching” - a twist on the more familiar concept of machine learning. That long-range goal is moving toward reality thanks to an effort led by professors in the University of Wisconsin–Madison departments of computer sciences, psychology and educational psychology.

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