  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.
