"Bee and I need diversity!" Break Filter Bubbles in Recommendation Systems through Embodied AI Learning
Authors: 
Xiaofei Zhou, Yushan Zhou, Yunfan Gong, Zhenyao Cai, Annie Qiu, Qinqin Xiao, 
Alissa N. Antle, Zhen BaiAuthors Info & Claims
Xiaofei Zhou, Yushan Zhou, Yunfan Gong, Zhenyao Cai, Annie Qiu, Qinqin Xiao, Alissa N. Antle, Zhen BaiAuthors Info & ClaimsPages 44 - 61
Abstract
AI recommendations influence our daily decisions. The convenience of navigating personalized content goes hand-in-hand with the notorious filter bubble effect, which may decrease people’s exposure to diverse options and opinions. Children are especially vulnerable to this due to their limited AI literacy and critical thinking skills. In this study, we propose a novel Augmented Reality (AR) application BeeTrap. It aims to not only raise children’s awareness of filter bubbles but also empower them to mitigate this ethical issue through sense-making of AI recommendation systems’ inner workings. By having children experience and break filter bubbles in a flower recommendation system, BeeTrap utilizes embodied metaphors (e.g., NEAR-FAR, ITERATION) and analogies (bee pollination) to bridge abstract AI concepts with sensory-motor experiences in familiar STEM contexts. To evaluate our design’s effectiveness and accessibility for a broad range of children, we introduced BeeTrap in a four-day summer camp for middle-school students from underrepresented backgrounds in STEM. Results from pre- and post-tests and interviews show that BeeTrap developed students’ technical understanding of AI recommendations, empowered them to break filter bubbles, and helped them foster new personal and societal perspectives around AI technologies.
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Index Terms
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June 2024
1049 pages
ISBN:9798400704420
DOI:10.1145/3628516
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