An XAI Social Media Platform for Teaching K-12 Students AI-Driven Profiling, Clustering, and Engagement-Based Recommending
Nicolas Pope, Juho Kahila, Henriikka Vartiainen, Mohammed Saqr,, Sonsoles Lopez-Pernas, Teemu Roos, Jari Laru, Matti Tedre

TL;DR
This paper introduces an explainable AI social media platform designed for K-12 students, providing interactive tools and visualizations to teach core AI concepts like profiling and recommendations, aiming to improve AI literacy and ethics awareness.
Contribution
It presents a novel educational platform with interactive experiments and visualizations tailored for young learners to understand social media AI processes.
Findings
Students engaged with the platform and demonstrated understanding of AI concepts.
The tool increased awareness of data-driven processes and ethical considerations.
Browsing patterns revealed insights into student interactions with AI systems.
Abstract
This paper, submitted to the special track on resources for teaching AI in K-12, presents an explainable AI (XAI) education tool designed for K-12 classrooms, particularly for students in grades 4-9. 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 engaging for young learners. The tool provides hands-on experiments and real-time visualizations, illustrating how user actions influence both their personal experience on the platform and the experience of others. This approach seeks to enhance learners' data agency, AI literacy, and sensitivity to AI ethics. The paper includes a…
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Taxonomy
TopicsOnline Learning and Analytics
