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

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 case example from 12 two-hour test sessions involving 209 children, using learning analytics to demonstrate how they navigated their social media feeds and the browsing patterns that emerged.