Maestro: A Gamified Platform for Teaching AI Robustness
Margarita Geleta, Jiacen Xu, Manikanta Loya, Junlin Wang and, Sameer Singh, Zhou Li, Sergio Gago-Masague

TL;DR
Maestro is an open-source, gamified platform designed to enhance education in robust AI by engaging college students through goal-based scenarios and competitive programming, improving motivation and learning outcomes.
Contribution
This paper introduces Maestro, a novel gamified platform for teaching robust AI, with demonstrated effectiveness in increasing student engagement and learning success.
Findings
Students appreciated Maestro and scored highly on learning metrics.
Leaderboard gamification increased engagement and motivation.
Maestro is adaptable to various course lengths and depths.
Abstract
Although the prevention of AI vulnerabilities is critical to preserve the safety and privacy of users and businesses, educational tools for robust AI are still underdeveloped worldwide. We present the design, implementation, and assessment of Maestro. Maestro is an effective open-source game-based platform that contributes to the advancement of robust AI education. Maestro provides goal-based scenarios where college students are exposed to challenging life-inspired assignments in a competitive programming environment. We assessed Maestro's influence on students' engagement, motivation, and learning success in robust AI. This work also provides insights into the design features of online learning tools that promote active learning opportunities in the robust AI domain. We analyzed the reflection responses (measured with Likert scales) of 147 undergraduate students using Maestro in two…
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Taxonomy
TopicsEducational Games and Gamification · Online Learning and Analytics · Teaching and Learning Programming
