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As AI tools become common across jobs and industries, it is critical to broaden education about AI beyond teaching computer scientists how to build AI systems. To expand AI education, we are researching AI for AI learning: a personalized and adaptive learning system that integrates dialog-based tutoring and gamified programming activities. To study this problem, we adapted and expanded an existing smartphone adaptive coach to develop the Game-if-AI system. Using a design-based research approach, Game-if-AI was iteratively tested and improved across four semesters of optional use in a course designed for technician-level understanding of AI: mastering programming skills to apply AI libraries and established models. In this study, we measured the interests and needs of these technical learners, based on both survey data and on how they engaged with topics in the system. Based on this data, new topics were added and the system was refined. In this paper, we report students' usability ratings for system components and student preferences based on completion rates of AI topics available each semester. Students rated the adaptive system positively overall (93% rated as a good idea), but more complex learning activities (tutoring dialogs, programming) were rated lower than traditional ones (e.g., multiple choice, reading). Students were most likely to master topics highly aligned to the course materials, as well as self-directed learning toward easier high-interest topics (e.g., LLM Prompting).
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PrimaryAI: Co-Designing Immersive Problem-Based Learning for Upper Elementary Student Learning of AI Concepts and Practices
There is growing awareness of the central role that artificial intelligence (AI) plays now and in children's futures. This has led to increasing interest in engaging K-12 students in AI education to promote their understanding of AI concepts and practices. Leveraging principles from problem-based pedagogies and game-based learning, our approach integrates AI education into a set of unplugged activities and a game-based learning environment. In this work, we describe outcomes from our efforts to co design problem-based AI curriculum with elementary school teachers.
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- PAR ID:
- 10352386
- Date Published:
- Journal Name:
- 27th ACM Conference on Innovation and Technology in Computer Science Education
- Volume:
- 2
- Page Range / eLocation ID:
- 628 to 628
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1145/3502717.3532142
