Abstract
This article provides an in-depth look at how K-12 students should be introduced to Machine Learning and the knowledge and skills they will develop as a result. We begin with an overview of the AI4K12 Initiative, which is developing national guidelines for teaching AI in K-12, and briefly discuss each of the “Five Big Ideas in AI” that serve as the organizing framework for the guidelines. We then discuss the general format and structure of the guidelines and grade band progression charts and provide a theoretical framework that highlights the developmental appropriateness of the knowledge and skills we want to impart to students and the learning experiences we expect them to engage in. Development of the guidelines is informed by best practices from Learning Sciences and CS Education research, and by the need for alignment with CSTA’s K-12 Computer Science Standards, Common Core standards, and Next Generation Science Standards (NGSS). The remainder of the article provides an in-depth exploration of the AI4K12 Big Idea 3 (Learning) grade band progression chart to unpack the concepts we expect students to master at each grade band. We present examples to illustrate the progressions from two perspectives: horizontal (across grade bands) and vertical (across concepts for a given grade band). Finally, we discuss how these guidelines can be used to create learning experiences that make connections across the Five Big Ideas, and free online tools that facilitate these experiences.
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Acknowledgements
We are grateful to our colleague Fred Martin, now an emeritus member of the AI4K12 Steering Committee, for his important contributions to launching the AI4K12 Initiative and formulating the Five Big Ideas. We also thank the members of the AI4K12 Working Group and Advisory Board for their diligent efforts in developing the guidelines.
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This work was supported by the National Science Foundation under Grant No. DRL-1846073.
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Touretzky, D., Gardner-McCune, C. & Seehorn, D. Machine Learning and the Five Big Ideas in AI. Int J Artif Intell Educ 33, 233–266 (2023). https://doi.org/10.1007/s40593-022-00314-1
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DOI: https://doi.org/10.1007/s40593-022-00314-1