As Artificial Intelligence (AI) becomes ubiquitous in children’s lives, it is crucial to introduce newand effective ways to teach the younger generation about AI. This study describes the experience of training undergraduate students as middle school AI summer camp facilitators. These undergraduates participated in a professional development session prior to camp for 9 days (54 hours), and then facilitated AI learning and camp activities within the two-week camp. Our findings indicate that the undergraduate facilitators benefited greatly from this experience, in turn providing benefits to the middle school learners. The undergraduates developed both professional and conceptual skills. By interacting with the facilitators as “near-peers”, the students learned about AI in a fun, engaging, and
supportive way. These findings contribute to our understanding of how to effectively teach young learners about complex AI concepts.
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This content will become publicly available on June 10, 2025
Approaching “Filter Bubble” in Recommendation Systems: A Transformative AI Literacy Learning Experience
Young learners today are constantly influenced by AI recommendations, from media choices to social connections. The resulting "filter bubble" can limit their exposure to diverse perspectives, which is especially problematic when they are not aware this manipulation is happening or why. To address the need to support youth AI literacy, we developed "BeeTrap", a mobile Augmented Reality (AR) learning game designed to enlighten young learners about the mechanisms and the ethical issue of recommendation systems. Transformative Experience model was integrated into learning activities design, focusing on making AI concepts relevant to students’ daily experiences, facilitating a new understanding of their digital world, and modeling real-life applications. Our pilot study with middle schoolers in a community-based program primarily investigated how transformative structured AI learning activities affected students’ understanding of recommendation systems and their overall conceptual, emotional, and behavioral changes toward AI.
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- Award ID(s):
- 2238675
- NSF-PAR ID:
- 10519176
- Publisher / Repository:
- International Society of the Learning Sciences
- Date Published:
- Page Range / eLocation ID:
- 490 to 497
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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