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There are many initiatives that teach Artificial Intelligence (AI) literacy to K-12 students. Most downsize college-level instructional materials to grade-level appropriate formats, overlooking students' unique perspectives in the design of curricula. To investigate the use of educational games as a vehicle for uncovering youth's understanding of AI instruction, we co-designed games with 39 Black, Hispanic, and Asian high school girls and non-binary youth to create engaging learning materials for their peers. We conducted qualitative analyses on the designed game artifacts, student discourse, and their feedback on the efficacy of learning activities. This study highlights the benefits of co-design and learning games to uncover students' understanding and ability to apply AI concepts in game-based learning, their emergent perspectives of AI, and the prior knowledge that informs their game design choices. Our research uncovers students' AI misconceptions and informs the design of educational games and grade-level appropriate AI instruction.
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Maestro: A Gamified Platform for Teaching AI Robustness
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 maestry 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.
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- Award ID(s):
- 2039634
- PAR ID:
- 10477443
- Publisher / Repository:
- AAAI
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume:
- 37
- Issue:
- 13
- ISSN:
- 2159-5399
- Page Range / eLocation ID:
- 15816 to 15824
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1609/aaai.v37i13.26878
