A systematic review of learning task design for K-12 AI education: Trends, challenges, and opportunities
Li Li, Fengchao Yu, Enting Zhang
Abstract
This systematic review investigates learning task design for K-12 AI education, aiming to provide an overview of the status of AI education and identify trends, challenges, and opportunities. Through an analysis of 47 empirical studies, the review presents, synthesizes, and evaluates the educational theories underpinning the learning task design, the content, the pedagogies used for teaching, as well as the measurement and outcomes of the existing literature on AI education programs in K-12 settings. The principal findings reveal a diverse landscape of learning task design for teaching AI to K-12 students. Positive outcomes underscore the effectiveness of well-crafted hands-on tasks in fostering deep understanding and engagement. Challenges include addressing initial teacher and student apprehension, enhancing deep conceptual explanations of AI concepts, and hardware-related obstacles. The review encourages deep conceptual knowledge, holistic AI education, collaborative knowledge-sharing across nations, and co-design of learning tasks and resources across sectors.