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Machine Learning (ML) opens exciting scientific opportunities in K-12 STEM classrooms. However, students struggle with interpreting ML patterns due to limited data literacy. Face glyphs offer unique benefit by leveraging our brain’s facial feature processing. Yet, they have limitations like lacking contextual information and data biases. To address this, we created three enhanced face glyph visualizations: feature-independent and feature-aligned range views, and the sequential feature inspector. In a study with 25 high school students, feature-aligned range visualization helped contextual analysis, and the sequential feature inspector reduced missing data risks. Face glyphs also benefit the global interpretation of data. 

Understanding the inner workings of Artificial Intelligence (AI) recommendation systems may benefit children in becoming more sensible consumers of the ever-growing information in their daily lives. It may further enable deeper reflections on related ethical issues such as the filter bubble. With limited prior knowledge in math and computing, children often find AI concepts overly abstract. Inspired by optical computation, we propose a novel tangible interface, OptiDot. Through exploratory manipulation with light beams, OptiDot supports children in learning the dot product—a building block for numerous AI algorithms—and AI recommendations through embodied learning experiences. Findings of a preliminary user study with ten middle school students indicate the effectiveness of the key embodied metaphors. We also discuss the design implications and challenges of developing optical-inspired learning tools for children. 

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.