K-12 Computing Education for the AI Era: From Data Literacy to Data Agency

Abstract

The question of how to teach classical, rule-based programming has been driving much of the computing education research since the 1950s. In the K--12 (school) context, a consensus has emerged over time on the paradigmatic elements of computing education, which implicitly assumes a von Neumann computer executing instruction sequences guided by imperative programs. Within this framework, many researchers have focused on how to facilitate learners to develop an accurate mental model of what the computer does when it executes a piece of code. However, the traditional programming approach in computing education is inadequate for understanding and developing machine learning (ML) driven technology. ML has already facilitated significant advancements in automation, ranging from speech and image recognition, autonomous cars, and deepfake videos to super-human performance in board and computer games, and more. Many data-driven approaches that power today's cutting edge services and apps significantly diverge from the central paradigmatic assumptions of traditional programming. Consequently, traditional views on computing education are increasingly being challenged to account for the changes that AI/ML brings. This keynote talk presents early results from a study on how to teach fundamental AI insights and techniques to 200 4--9 graders in 14 primary schools in Eastern Finland. It describes the learning environments, tools, and pedagogical approaches involved, and explores the paradigmatic and conceptual changes required in transitioning from teaching classical programming to teaching ML in K--12 computing education. It outlines the mindset shifts required for this transition and discusses the challenges posed to the development of curricula, educational technology, and learning environments. It further provides examples of how AI ethics concepts, such as algorithmic bias, privacy, misinformation, diversity, and accountability, can be integrated into ML education. The talk discusses the relationship between different literacies in computing and presents an active concept, data agency, that refers to people's volition and capacity for informed actions that make a difference in their digital world. It emphasizes not only the understanding of data (i.e., data literacy) but also the active control and manipulation of information flows and the ethical and wise use of them.