Breakable Machine: A K–12 Classroom Game for Transformative AI Literacy Through Spoofing and eXplainable AI (XAI)

This paper presents an eXplainable AI (XAI)-based class- room game “Breakable Machine” for teaching critical, trans- formative AI literacy through adversarial play and interroga- tion of AI systems. Designed for learners aged 10–15, the game invites students to spoof an image classifier by manip- ulating their appearance or environment in order to trigger high-confidence misclassifications. Rather than focusing on building AI models, this activity centers on breaking them— exposing their brittleness, bias, and vulnerability through hands-on, embodied experimentation. The game includes an XAI view to help students visualize feature saliency, reveal- ing how models attend to specific visual cues. A shared class- room leaderboard fosters collaborative inquiry and compar- ison of strategies, turning the classroom into a site for col- lective sensemaking. This approach repositions AI education by treating model failure and misclassification not as prob- lems to be debugged, but as pedagogically rich opportuni- ties to interrogate AI as a sociotechnical system. In doing so, the game supports students in developing data agency, ethical awareness, and a critical stance toward AI systems increas- ingly embedded in everyday life. The game and its source code are freely available.