Visual tools for teaching machine learning in K-12: A ten-year systematic mapping

Teaching Machine Learning in school helps students to be better prepared for a society rapidly changing due to the impact of Artificial Intelligence. This requires age-appropriate tools that allow students to develop a comprehensive understanding of Machine Learning in order to become creators of smart solutions. Following the trend of visual languages for introducing algorithms and programming in K-12, we present a ten-year systematic mapping of emerging visual tools that support the teaching of Machine Learning at this educational stage and analyze the tools concerning their educational characteristics, support for the development of ML models as well as their deployment and how the tools have been developed and evaluated. As a result, we encountered 16 tools targeting students mostly as part of short duration extracurricular activities. Tools mainly support the interactive development of ML models for image recognition tasks using supervised learning covering basic steps of the ML process. Being integrated into popular block-based programming languages (primarily Scratch and App Inventor), they also support the deployment of the created ML models as part of games or mobile applications. Findings indicate that the tools can effectively leverage students' understanding of Machine Learning, however, further studies regarding the design of the tools concerning educational aspects are required to better guide their effective adoption in schools and their enhancement to support the learning process more comprehensively.

Automated summary

Teaching Machine Learning in school is essential for preparing students for a society influenced by Artificial Intelligence. The study identified visual tools that support teaching Machine Learning in K-12 education, with findings suggesting that these tools can enhance students' understanding of ML but require further research on their educational design to optimize their effectiveness in schools.