  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 quarterly college courses in
AI. According to the results, students who felt the acquisition of new skills
in robust AI tended to appreciate highly Maestro and scored highly on material
consolidation, curiosity, and mastery in robust AI. Moreover, the leaderboard,
our key gamification element in Maestro, has effectively contributed to
students' engagement and learning. Results also indicate that Maestro can be
effectively adapted to any course length and depth without losing its
educational quality.

Human-Computer Interaction;Artificial Intelligence