Artificial Intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review

Abstract

There is an emerging interest in Artificial Intelligence (AI) teaching and learning in the K-12 setting. While some work has explored the educational content and resources used for this purpose, there is limited empirical evidence on the effectiveness of such AI education interventions. The primary objective of the systematic literature review presented in this paper was to examine research with empirical evidence reporting learning outcomes for teaching and learning AI in K-12 between 2019 and 2022. Through a rigorous selection process, a total of 28 studies were included in the final analysis out of 8,175 papers identified from five research databases using specific search terms. A content analysis method was used to synthesise the data. This paper outlines the focus on learners' context, the extent of empirical support for the pedagogical approaches, and the theoretical coverage of AI topics included in the studies. The majority of studies reported an improvement in both cognitive and affective learning outcomes. The paper concludes by highlighting key areas where additional research is needed in the future as well as the challenges associated with them. Although the findings are based on limited empirical studies, they suggest that a more learner-centred approach, context-aware pedagogical practices, and consistent constructs to measure AI learning outcomes could benefit teaching and learning AI in K-12 schools. Further research is needed to build on these insights.

Keywords

• Artificial intelligence
• Machine learning
• K-12 education
• Systematic literature review