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.

翻译：在这篇概念性论文中，我们回顾了现有的人工智能/机器学习（AI/ML）教育文献，以识别三种对机器学习教与学进行概念化的路径。其中，数据驱动路径强调为青少年提供创建数据集、训练和测试模型的机会。第二种算法驱动路径则优先关注学习机器学习模型背后的学习算法或引擎的工作原理。此外，我们还发现了整合前两种路径的第三种路径的努力。在综述中，我们重点关注这些路径如何：（1）对机器学习的各个方面进行"玻璃盒"与"黑盒"化处理，（2）基于学习者兴趣并提供设计应用程序的机会，（3）融入伦理与公平性考量。在讨论部分，我们探讨了现有路径面临的挑战与机遇，并为学习活动的设计提出了未来发展方向。