English

Briteller: Shining a Light on AI Recommendations for Children

Human-Computer Interaction 2025-03-31 v1

Abstract

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.

Keywords

Cite

@article{arxiv.2503.22113,
  title  = {Briteller: Shining a Light on AI Recommendations for Children},
  author = {Xiaofei Zhou and Yi Zhang and Yufei Jiang and Yunfan Gong and Chi Zhang and Alissa N. Antle and Zhen Bai},
  journal= {arXiv preprint arXiv:2503.22113},
  year   = {2025}
}

Comments

2025 ACM CHI conference on Human Factors in Computing Systems

R2 v1 2026-06-28T22:37:35.607Z