Curricula around the world have started to include content related to artificial intelligence (AI) in their agendas. Although this process is timely and important, it is also challenging because the elaboration of the AI field for K–12 remains ongoing. Current efforts often underappreciate the critical role of data literacy for AI education. If the goal is to enable students to understand how AI systems work and what their implications are, they must understand what data underpins these systems and how that data is collected and processed. To advance knowledge about data literacy in AI education, we conducted a comprehensive theoretical analysis of the data science and AI fields, which led to creating a model of key data-related practices and a collection of key data-related concepts. Using a design-based research process, we also developed a pedagogy for educating K-12 students about data: the data case study. The collection and the model equip teachers with a map and a shared vocabulary. The resulting pedagogy provides them with practical ways to help students develop a conceptual understanding and agency.
Olari et al. (Thu,) studied this question.