While there exists a miscellany of learning platforms for cybersecurity education, training tools specifically targeting robust AI are still at an early stage of development and implementation. We present the design factors of Maestro, an effective open-source game-based platform for robust AI education and oriented to college students. This work provides insights into a set of effective design features for educational games for robust AI, which are long overdue to educate the future AI workforce in countermeasures and the prevention of AI vulnerabilities. In addition, the assessment of the current implementation sets future directions for emerging online learning tools to promote active learning opportunities in the robust AI domain. Maestro provides goal-based scenarios (GBSs) where students are exposed to challenging life-inspired assignments in a competitive programming environment. Our goal was to assess Maestro’s influence on students’ engagement, motivation and learning success in robust AI. We analyzed the reflection responses (measured with Likert scales) of 147 undergraduate students using Maestro in two quarterly courses in AI in a large US public university. According to our survey results, the design features of Maestro, including its leaderboard, our key gamification element in the platform, has been crucial for effective learning and skill-development. Students who felt the acquisition of new skills in robust AI tended to appreciate highly Maestro and scored highly on material consolidation, curiosity and maestry in robust AI.