The increasing attention to Machine Learning (ML) in K-12 levels and studies exploring a different aspect of research on K-12 ML has necessitated the need to synthesize this existing research. This study systematically reviewed how research on ML teaching and learning in K-12 has fared, including the current area of focus, and the gaps that need to be addressed in the literature in future studies. We reviewed 43 conference and journal articles to analyze specific focus areas of ML learning and teaching in K-12 from four perspectives as derived from the data: curriculum development, technology development, pedagogical development, and teacher training/professional development. The findings of our study reveal that (a) additional ML resources are needed for kindergarten to middle school and informal settings, (b) further studies need to be conducted on how ML can be integrated into subject domains other than computing, (c) most of the studies focus on pedagogical development with a dearth of teacher professional development programs, and (d) more evidence of societal and ethical implications of ML should be considered in future research. While this study recognizes the present gaps and direction for future research, these findings provide insight for educators, practitioners, instructional designers, and researchers into K-12 ML research trends to advance the quality of the emerging field.

Artificial Intelligence (AI) has triggered profound reforms across industries, including education. These developments necessitate the inclusion of AI as a subject in K-12 classrooms. However, the need for students to learn AI demands that educators pay increasing attention, believe in its relevance and intend to promote it among their students and colleagues. This paper aimed to explore teachers' perceptions of and behavioral intention to teach AI. We specifically considered the association of AI anxiety, perceived usefulness, attitude towards AI, AI relevance, AI readiness, and behavioral intention factors. This research further aims to examine the moderator effect of AI for social good and confidence on the relationship in our hypothesized research model. To address this purpose, a quantitative methodology with the use of structural equation modeling was utilized. Data were retrieved through an online questionnaire from 320 lower and upper secondary school in-service teachers, mostly in STEM-related fields. Our findings reveal that teacher perceptions of AI for social good and confidence will affect most relationships in the model. Teacher professional programs should include the benefits and risks of AI and good practice sharing.