The emergence of reinforcement-based AI for text generation (Chat-GPT) and image creation (Dall-E) has opened a wide range of possibilities for changing the game design and development process. While game development researchers have mostly focused on integrating these technologies to improve production workflow and demonstrate their use in the creation of content for entertainment purposes (intelligent NPCS), there is very little knowledge on how to integrate this technology into the design of educational games. In this paper, we present the results of integrating reinforcement AI (text and image generation) into educational gaming experiences by graduate students enrolled in a game-based learning course. The students were given a core set of requirements that enable the integration into their project with some flexibility on the desired educational outcome. The produced experiences were then evaluated by a small sample of experts (gaming and learning sciences) and their observations were compiled. Specifically, we describe the wide range of experiences developed by the students and the results of a qualitative study with a small group of experts that evaluated these experiences. Our results indicate that reinforcement AI-based integrations into educational game design and development helps enrich the user experience and has the potential to improve learning outcomes.
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.
more » « less- Award ID(s):
- 2039634
- NSF-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
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1609/aaai.v37i13.26878
