BiasFairnessEthicscs.CY
Expanding the Scope of Computational Thinking in Artificial Intelligence for K-12 Education
Yasmin Kafai, Shuchi Grover
February 18, 2026
arXiv.org
Key Takeaways
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Computational thinking needs to evolve beyond traditional programming to include AI and machine learning concepts, as generative AI's public emergence has created urgent questions about what K-12 students should understand about these technologies.
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Teaching AI literacy should address algorithmic bias and justice from the start, drawing on lessons learned over the past decade about how to responsibly integrate computing education in schools and help students recognize potential harms.
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AI education works best when integrated across subjects rather than taught in isolation, building on successful models from the last decade of interdisciplinary computing instruction to make AI concepts relevant to students' broader learning.
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There is significant debate about AI's impacts on K-12 education, with both potential benefits and risks from generative AI applications, making it essential that educators have clear guidance on what students need to learn to engage responsibly with these tools.
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.