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

2026-02-18

AI Summary

This study addresses the central questions of “what to teach” and “how to teach” in K–12 education in the age of artificial intelligence by proposing an expanded computational thinking framework that systematically integrates artificial intelligence and machine learning content, with an emphasis on algorithmic fairness and interdisciplinary connections. Drawing on design-based research, curriculum integration approaches, and AI ethics pedagogy, the project redefines computational thinking to align with the affordances and challenges of generative AI. The resulting instructional framework offers both theoretical innovation and practical feasibility for K–12 AI education, thereby advancing the evolution and implementation of computational thinking in the era of intelligent technologies.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Algorithmic accountability and transparency on the web

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.

Problem

• computational thinking
• artificial intelligence
• K-12 education
• machine learning
• AI curriculum

Innovation

• computational thinking
• artificial intelligence
• K-12 education
• machine learning
• algorithmic bias