Objective This study investigates what kind of conceptions primary school students have about ML if they are not conceptually "primed" with the idea that in ML, humans teach computers. Method Qualitative survey responses from 197 Finnish primary schoolers were analyzed via an abductive method. Findings We identified three partly overlapping ML conception categories, starting from the most accurate one: ML is about teaching machines (34%), ML is about coding (7.6%), and ML is about learning via or about machines (37.1%). Implications The findings suggest that without conceptual clues, children's conceptions of ML are varied and may include misconceptions such as ML is about learning via or about machines. The findings underline the importance of clear and systematic use of key concepts in computer science education. Besides researchers, this study offers insights for teachers, teacher educators, curriculum developers, and policymakers. Method Qualitative survey responses from 197 Finnish primary schoolers were analyzed via an abductive method. Findings We identified three partly overlapping ML conception categories, starting from the most accurate one: ML is about teaching machines (34%), ML is about coding (7.6%), and ML is about learning via or about machines (37.1%). Implications The findings suggest that without conceptual clues, children's conceptions of ML are varied and may include misconceptions such as ML is about learning via or about machines. The findings underline the importance of clear and systematic use of key concepts in computer science education. Besides researchers, this study offers insights for teachers, teacher educators, curriculum developers, and policymakers.

翻译：目的 本研究旨在探究未经概念性“引导”（即未事先告知机器学习中人类教授计算机这一理念）的情况下，小学生对机器学习持有何种概念。方法 采用溯因分析法对197名芬兰小学生的质性调查问卷数据进行处理。结果 我们识别出三个部分重叠的机器学习概念类别，按准确度由高到低依次为：机器学习是关于教机器（34%）、机器学习是关于编程（7.6%）、机器学习是通过机器或关于机器进行学习（37.1%）。启示 研究结果表明，缺乏概念线索时儿童对机器学习的概念认知具有多样性，可能包含诸如“机器学习是通过机器或关于机器进行学习”等错误概念。这一发现凸显了在计算机科学教育中清晰、系统使用关键概念的重要性。除研究人员外，本研究亦为教师、师范教育者、课程开发者及政策制定者提供参考。