Breakable Machine: A K-12 Classroom Game for Transformative AI Literacy Through Spoofing and eXplainable AI (XAI)
Olli Hilke, Nicolas Pope, Juho Kahila, Henriikka Vartiainen, Teemu Roos, Tuomo Parkki, Matti Tedre

TL;DR
This paper introduces 'Breakable Machine', an educational game for K-12 students that teaches AI literacy through adversarial play, visualization of model features, and critical interrogation of AI vulnerabilities.
Contribution
It presents a novel, hands-on classroom activity that emphasizes breaking AI models to understand their limitations, bias, and vulnerabilities, fostering critical AI literacy among young learners.
Findings
Students learn to identify AI model vulnerabilities.
The game promotes collaborative inquiry and critical thinking.
It enhances understanding of AI biases and ethical considerations.
Abstract
This paper, submitted to the special track on resources for teaching AI in K-12, presents an eXplainable AI (XAI)-based classroom game "Breakable Machine" for teaching critical, transformative AI literacy through adversarial play and interrogation of AI systems. Designed for learners aged 10-15, the game invites students to spoof an image classifier by manipulating 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, revealing how models attend to specific visual cues. A shared classroom leaderboard fosters collaborative inquiry and comparison of strategies, turning the classroom into a site for…
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Taxonomy
TopicsOnline Learning and Analytics · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI)
