一个用于教授K-12学生AI驱动的画像、聚类和基于参与度的推荐的XAI社交媒体平台
An XAI Social Media Platform for Teaching K-12 Students AI-Driven Profiling, Clustering, and Engagement-Based Recommending
AI总结：
本文介绍一个面向K-12学生的XAI教育工具，通过模拟社交媒体平台教授数据收集、用户画像、参与度和推荐算法，以增强数据代理和AI素养，并在209名儿童的测试中验证其效果。
摘要：
本文提交至K-12人工智能教学资源特别专题，介绍了一个面向K-12课堂（尤其针对4-9年级学生）设计的可解释人工智能（XAI）教育工具。该工具旨在对社交媒体平台背后的基本过程进行干预，聚焦于四个AI和数据驱动的核心概念：数据收集、用户画像、参与度指标和推荐算法。一个类似Instagram的界面和一个用于解释数据驱动过程的监控工具，使这些复杂概念对年轻学习者变得易于理解且引人入胜。该工具提供动手实验和实时可视化，展示用户行为如何影响他们个人在平台上的体验以及他人的体验。这种方法旨在增强学习者的数据代理能力、AI素养和对AI伦理的敏感性。论文包含来自12次两小时测试环节的案例，涉及209名儿童，利用学习分析展示他们如何浏览社交媒体信息流以及出现的浏览模式。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
