Despite recent calls for including artificial intelligence (AI) literacy in K-12 education, not enough attention has been paid to studying youths' everyday knowledge about machine learning (ML). Most research has examined how youths attribute intelligence to AI/ML systems. Other studies have centered on youths' theories and hypotheses about ML highlighting their misconceptions and how these may hinder learning. However, research on conceptual change shows that youths may not have coherent theories about scientific phenomena and instead have knowledge pieces that can be productive for formal learning. We investigate teens' everyday understanding of ML through a knowledge-in-pieces perspective. Our analyses reveal that youths showed some understanding that ML applications learn from training data and that applications recognize patterns in input data and depending on these provide different outputs. We discuss how these findings expand our knowledge base and implications for the design of tools and activities to introduce youths to ML.

翻译：尽管近期有呼声要求在K-12教育中纳入人工智能素养，但关于青少年对机器学习日常知识的研究仍未得到足够重视。大多数研究聚焦于青少年如何将智能归因于人工智能/机器学习系统，另有部分研究关注青少年对机器学习的朴素理论和假设，突出其误解及这些误解可能如何阻碍学习。然而，概念转变研究表明，青少年对科学现象可能不具备连贯的理论体系，而是拥有对正式学习具有建设性的知识碎片。本研究采用知识碎片理论视角探究青少年对机器学习的日常理解。分析显示，青少年在一定程度上理解机器学习应用能从训练数据中学习，并能识别输入数据中的模式，根据这些模式生成不同输出。我们讨论了这些发现如何拓展现有知识基础，以及对设计引导青少年了解机器学习的工具与活动的启示。