  In this conceptual paper, we review existing literature on artificial
intelligence/machine learning (AI/ML) education to identify three approaches to
how learning and teaching ML could be conceptualized. One of them, a
data-driven approach, emphasizes providing young people with opportunities to
create data sets, train, and test models. A second approach, learning
algorithm-driven, prioritizes learning about how the learning algorithms or
engines behind how ML models work. In addition, we identify efforts within a
third approach that integrates the previous two. In our review, we focus on how
the approaches: (1) glassbox and blackbox different aspects of ML, (2) build on
learner interests and provide opportunities for designing applications, (3)
integrate ethics and justice. In the discussion, we address the challenges and
opportunities of current approaches and suggest future directions for the
design of learning activities.
