Students as AI literate designers: a pedagogical framework for learning and teaching AI literacy in elementary education
Miao Yue, Morris Siu–Yung Jong, Yun Dai, Wilfred W. F. Lau
Abstract
Although introducing AI education into K-12 schools has attracted significant interest from educators and researchers, research into effective strategies for enhancing elementary students’ AI literacy remains in its infancy. Grounded in design-based learning, we propose a pedagogical framework, “Students as AI literate designers” (SAILD), to facilitate young learners’ acquisition of AI literacy by designing solutions for real-world problem-solving. This paper reports on our mixed-methods study evaluating the effectiveness of SAILD in Grade-5 students’ AI literacy learning and teaching in Hong Kong, regarding AI knowledge, skills, ethics, and attitudes. The quantitative findings show significant differences in students’ AI knowledge, ethics, and attitudes between pre- and post-test scores; the qualitative findings reveal students’ problem-solving processes using AI skills. While the present work provides empirical evidence supporting the adoption of SAILD in elementary AI education, it underlines the challenges of SAILD and suggests areas for further research.