Briteller: Shining a Light on AI Recommendations for Children
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912298803658752 |
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| author | Zhou, Xiaofei Zhang, Yi Jiang, Yufei Gong, Yunfan Zhang, Chi Antle, Alissa N. Bai, Zhen |
| author_facet | Zhou, Xiaofei Zhang, Yi Jiang, Yufei Gong, Yunfan Zhang, Chi Antle, Alissa N. Bai, Zhen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Briteller: Shining a Light on AI Recommendations for Children Zhou, Xiaofei Zhang, Yi Jiang, Yufei Gong, Yunfan Zhang, Chi Antle, Alissa N. Bai, Zhen Human-Computer Interaction 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. |
| title | Briteller: Shining a Light on AI Recommendations for Children |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.22113 |