Abstract:

This scoping review explores artificial intelligence (AI) teaching activity design for K-12 students. Its objective is to provide an overview of the landscape of AI education for K-12 students with a focus on AI teaching activity design to identify trends, challenges, and opportunities. Using the PCC (participants, concept, and context) framework, the study’s inclusion criteria encompass K-12 students and teachers (participants), emphasizing AI learning activity design (concept) in diverse formal school and informal after-school settings (context). A systematic search across Education Database, SCOPUS, IEEE, and Web of Science databases, facilitated by Covidence, was conducted for relevant English-language literature. The review’s findings show a diverse research landscape encompassing research scale, research methodology, teaching content, and pedagogical strategies embedded in AI teaching activity design. Our review also finds that while the majority of studies highlight positive outcomes such as heightened AI knowledge and student engagement, challenges related to the depth of AI conceptual understanding and resource limitations are acknowledged. Based on the findings, our review highlights the importance of comprehensive AI education, which covers both technical and social dimensions. It also advocates for interdisciplinary integration, resource development, and effective assessment approaches. Its significance lies in providing valuable insights for educational researchers in understanding the landscape of AI education for K-12, aiding instructional designers to create innovative teaching approaches, assisting teachers in integrating AI activities into their teaching practices, and guiding policymakers in fostering a robust AI learning ecosystem tailored for K-12 students.

Keywords:

Artificial Intelligence (AI) education, K-12 students, teaching activity design.