The topics of Artificial intelligence (AI) and especially Machine Learning (ML) are increasingly making their way into educational curricula. To facilitate the access for students, a variety of platforms, visual tools, and digital games are already being used to introduce ML concepts and strengthen the understanding of how AI works. We take a look at didactic principles that are employed for teaching computer science, define criteria, and, based on those, evaluate a selection of prominent existing platforms, tools, and games. Additionally, we criticize the approach of portraying ML mostly as a black-box and the resulting missing focus on creating an understanding of data, algorithms, and models that come with it. To tackle this issue, we present a concept that covers intermodal transfer, computational and explanatory thinking, ICE-T, as an extension of known didactic principles. With our multi-faceted concept, we believe that planners of learning units, creators of learning platforms and educators can improve on teaching ML.

翻译：人工智能（AI），尤其是机器学习（ML）主题正日益融入教育课程。为了便于学生接触，目前已有多种平台、可视化工具和数字游戏被用于介绍ML概念并加深对AI工作原理的理解。我们审视了计算机科学教学中采用的教学原则，定义了一系列标准，并基于这些标准评估了一批现有的代表性平台、工具和游戏。此外，我们批评了当前主要将ML描绘为"黑箱"的做法，以及由此导致的、对与之相关的数据、算法和模型理解培养的缺失。为解决这一问题，我们提出了一个涵盖跨模态迁移、计算思维与解释性思维的多维度概念——ICE-T，作为对已知教学原则的扩展。我们相信，借助这一多维度概念，学习单元的设计者、学习平台的创建者以及教育工作者能够改进ML教学。