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.

翻译：尽管预防人工智能漏洞对于保护用户和企业的安全与隐私至关重要，但全球范围内针对鲁棒性人工智能的教育工具仍发展不足。我们介绍了Maestro的设计、实现与评估。Maestro是一个有效的开源游戏化平台，致力于推动鲁棒性人工智能教育的进步。该平台提供基于目标的学习场景，使大学生在竞争性编程环境中接触富有挑战性的生活化作业。我们评估了Maestro对学生参与度、学习动机以及在鲁棒性人工智能领域学习成效的影响。本研究还揭示了在线学习工具中促进鲁棒性人工智能领域主动学习机会的设计特征。我们分析了147名本科学生在两个季度的大学人工智能课程中使用Maestro后的反思反馈（采用李克特量表测量）。结果表明，自认为在鲁棒性人工智能方面获得新技能的学生往往高度评价Maestro，并在材料巩固、好奇心和鲁棒性人工智能掌握方面得分较高。此外，作为Maestro核心游戏化元素的排行榜有效促进了学生的参与度和学习。研究结果还表明，Maestro能够灵活适应不同课程时长与深度，同时保持其教育质量。