Expanding the Scope of Computational Thinking in Artificial Intelligence for K-12 Education

TL;DR

This paper explores how to broaden computational thinking in K-12 education to better include artificial intelligence and machine learning, emphasizing curriculum design, interdisciplinary integration, and ethical considerations.

Contribution

It proposes expanding computational thinking frameworks to incorporate AI and ML, informed by lessons from past educational program designs and ethical issues.

Findings

Guidelines for integrating AI into computational thinking education

Insights from a decade of computing education research

Strategies for addressing algorithmic bias and justice in schools

Abstract

The introduction of generative artificial intelligence applications to the public has led to heated discussions about its potential impacts and risks for K-12 education. One particular challenge has been to decide what students should learn about AI, and how this relates to computational thinking, which has served as an umbrella for promoting and introducing computing education in schools. In this paper, we situate in which ways we should expand computational thinking to include artificial intelligence and machine learning technologies. Furthermore, we discuss how these efforts can be informed by lessons learned from the last decade in designing instructional programs, integrating computing with other subjects, and addressing issues of algorithmic bias and justice in teaching computing in schools.