Briteller: Shining a Light on AI Recommendations for Children

要旨

Understanding how AI recommendations work can help the younger generation become more informed and critical consumers of the vast amount of information they encounter daily. However, young learners with limited math and computing knowledge often find AI concepts too abstract. To address this, we developed Briteller, a light-based recommendation system that makes learning tangible. By exploring and manipulating light beams, Briteller enables children to understand an AI recommender system's core algorithmic building block, the dot product, through hands-on interactions. Initial evaluations with ten middle school students demonstrated the effectiveness of this approach, using embodied metaphors, such as "merging light" to represent addition. To overcome the limitations of the physical optical setup, we further explored how AR could embody multiplication, expand data vectors with more attributes, and enhance contextual understanding. Our findings provide valuable insights for designing embodied and tangible learning experiences that make AI concepts more accessible to young learners.

著者
Xiaofei Zhou
University of Rochester, Rochester, New York, United States
Yi Zhang
University of California, Irvine, Irvine, California, United States
Yufei Jiang
University of Rochester, Rochester, New York, United States
Yunfan Gong
University of Rochester, Rochester, New York, United States
Chi Zhang
University of Rochester, Rochester, New York, United States
Alissa N.. Antle
Simon Fraser University, Vancouver, British Columbia, Canada
Zhen Bai
University of Rochester, Rochester, New York, United States
DOI

10.1145/3706598.3714106

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714106

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Recommendation and Personalization

G401
7 件の発表
2025-04-29 20:10:00
2025-04-29 21:40:00
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