Game-Based Learning for AI Education: Systematic Review

Abstract

The rapid development of artificial intelligence (AI) globally has pushed educational systems to focus on AI literacy in K-12 curricula. However, teaching AI at this level presents challenges due to the abstract and complex nature of AI concepts, which can be difficult for younger students to understand. Game-based learning (GBL) offers an effective solution, providing interactive and immersive experiences that can boost student engagement and help them better understand these complex concepts. This paper explores how to integrate GBL into K-12 AI education, focusing on key design elements, game mechanics that improve engagement and learning outcomes, and the challenges of implementation. Based on a review of relevant literature from 2019 to 2024, the study proposes several design principles and practical recommendations. The findings highlight the importance of selecting suitable game types for different grade levels and learning goals, using game mechanics that encourage both competition and cooperation, and structuring learning in phases to improve engagement and learning results. At the same time, the shortage of resources and the integration of games and curriculum objectives are still the main obstacles to the implementation of GBL.

Keywords: Game-Based Learning, K-12 Education, AI Education

DOI: 10.54941/ahfe1006660