An XAI Social Media Platform for Teaching K-12 Students AI-Driven Profiling, Clustering, and Engagement-Based Recommending

This paper presents an explainable AI (XAI) educational tool designed for K-12 students (grades 4-9) to teach them about data-driven mechanisms in social media platforms, including data collection, user profiling, engagement metrics, and recommendation algorithms. The tool uses an Instagram-like interface with real-time visualizations and was tested with 209 children in 12 two-hour sessions, using learning analytics to track how students navigated and understood these AI-driven processes.

This paper presents an explainable AI (XAI) education tool designed for K-12 classrooms, particularly for students aged 11-16. The tool was designed for interventions on the fundamental processes behind social media platforms, focusing on four AI- and data-driven core concepts: data collection, user profiling, engagement metrics, and recommendation algorithms. An Instagram-like interface and a monitoring tool for explaining the data-driven processes make these complex ideas accessible and engagi

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