While AI technology is becoming increasingly prevalent in our daily lives, the comprehension of machine learning (ML) among non-experts remains limited. Interactive machine learning (IML) has the potential to serve as a tool for end users, but many existing IML systems are designed for users with a certain level of expertise. Consequently, it remains unclear whether IML experiences can enhance the comprehension of ordinary users. In this study, we conducted a public event using an IML system to assess whether participants could gain technical comprehension through hands-on IML experiences. We implemented an interactive sound classification system featuring visualization of internal feature representation and invited visitors at a science museum to freely interact with it. By analyzing user behavior and questionnaire responses, we discuss the potential and limitations of IML systems as a tool for promoting technical comprehension among non-experts.

翻译：尽管人工智能技术正日益渗透日常生活，但非专业人士对机器学习（ML）的理解仍然有限。交互式机器学习（IML）有望成为终端用户的实用工具，但现有诸多IML系统主要面向具备一定专业知识的用户设计。因此，IML体验能否提升普通用户的技术理解尚不明确。本研究通过举办使用IML系统的公众活动，评估参与者能否通过IML实践操作获得技术理解。我们实现了一个交互式声音分类系统，该系统具备内部特征表征可视化功能，并邀请科学博物馆参观者自由互动。通过分析用户行为与问卷调查结果，我们探讨了IML系统作为促进非专业人士技术理解工具的潜力与局限性。