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Over the past decades, numerous practical applications of machine learningtechniques have shown the potential of data-driven approaches in a large numberof computing fields. Machine learning is increasingly included in computingcurricula in higher education, and a quickly growing number of initiatives areexpanding it in K-12 computing education, too. As machine learning enters K-12computing education, understanding how intuition and agency in the context ofsuch systems is developed becomes a key research area. But as schools andteachers are already struggling with integrating traditional computationalthinking and traditional artificial intelligence into school curricula,understanding the challenges behind teaching machine learning in K-12 is aneven more daunting challenge for computing education research. Despite thecentral position of machine learning in the field of modern computing, thecomputing education research body of literature contains remarkably few studiesof how people learn to train, test, improve, and deploy machine learningsystems. This is especially true of the K-12 curriculum space. This articlecharts the emerging trajectories in educational practice, theory, andtechnology related to teaching machine learning in K-12 education. The articlesituates the existing work in the context of computing education in general,and describes some differences that K-12 computing educators should take intoaccount when facing this challenge. The article focuses on key aspects of theparadigm shift that will be required in order to successfully integrate machinelearning into the broader K-12 computing curricula. A crucial step isabandoning the belief that rule-based "traditional" programming is a centralaspect and building block in developing next generation computational thinking.

Teaching Machine Learning in K-12 Computing Education: Potential and Pitfalls