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.

翻译：生成式人工智能应用向公众的引入，引发了关于其对K-12教育潜在影响与风险的热烈讨论。一个特别的挑战在于确定学生应学习哪些人工智能知识，以及这与计算思维的关系——计算思维长期以来作为在学校推广和引入计算教育的总体框架。本文旨在探讨我们应如何拓展计算思维，以纳入人工智能与机器学习技术。此外，我们讨论了这些努力如何能从过去十年在设计教学项目、将计算与其他学科融合、以及在学校计算教学中应对算法偏见与公平性问题的经验教训中获得启示。