Abstract
Due to advances in Artificial Intelligence (AI), computer science education has rapidly started to include topics related to AI along K-12 education. Although this development is timely and important, it is also concerning because the elaboration of the AI field for K-12 is still ongoing. Current efforts may significantly underestimate the role of data, the fundamental component of an AI system. If the goal is to enable students to understand how AI systems work, knowledge of key concepts related to data processing is a prerequisite, as data collection, preparation, and engineering are closely linked to the functionality of AI systems. To advance the field, the following research provides a comprehensive collection of key data-related concepts relevant to K-12 computer science education. These concepts were identified through a theoretical review of the AI field, aligned through a review of AI curricula for school education, evaluated through interviews with domain experts and teachers, and structured hierarchically according to the data lifecycle. Computer science educators can use the elaborated structure as a conceptual guide for designing learning arrangements that aim to enable students to understand how AI systems are created and function.
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