Our paper "Machine Learning and the Five Big Ideas in AI" has been
published in the International Journal of Artificial Intelligence in
Education.  It is available here:
https://github.com/touretzkyds/ai4k12/raw/master/documents/Touretzky_et_al_IJAIED_2022.pdf
The official archival version is here: https://doi.org/10.1007/s40593-022-00314-1
Abd a free read-only archival version is here: https://rdcu.be/cYqXm

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.

-- Dave Touretzky, Christina Gardner-McCune, and Deborah Seehorn