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 engaging for young learners. The tool provides hands-on experiments and real-time visualizations, illustrating how user actions influence their personal experience on the platform as well as 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.

This paper introduces an educational tool for classroom use, based on explainable AI (XAI), designed to demystify key social media mechanisms—tracking, profiling, and content recommendation—for novice learners. The tool provides a familiar, interactive interface that resonates with learners’ experiences with popular social media platforms, while also offering the means to “peek under the hood” and exposing basic mechanisms of datafication. Learners gain first-hand experience of how even the slightest actions, such as pausing to view content, are captured and recorded in their digital footprint, and further distilled into a personal profile. The tool uses real-time visualizations and verbal explanations to create a sense of immediacy: each time the user acts, the resulting changes in their engagement history and their profile are displayed in a visually engaging and understandable manner. This paper discusses the potential of XAI and educational technology in transforming data and digital literacy education and in fostering the growth of children’s privacy and security mindsets.

This paper introduces an AI education tool designed for novice learners to create machine learning (classifier) based applications. Advancing from Google’s Teachable Machine 2 and developed using the design science research methodology, the tool is piloted in 36 K-12 classroom sessions with 213 children and allows learners to easily navigate the complete ML workflow—from data collection to app deployment—without any programming skills. To evaluate how well the tool met children’s expectations children were asked, as part of the design process, to articulate their goals and intentions for their apps; then, after using the tool, to describe how well they perceived their final app realized their intention. The tool’s main novelty is its ability to create a standalone app by defining one or more actions to be triggered by each classifier result, and deploy that app to other devices. A no-code approach and fully integrated development environment reduces the need for technical skills, making AI learning more inclusive. The tool represents a significant step in making AI education accessible for early learners, with future enhancements aimed at expanding its capabilities.

In this poster, we present the results from the co-design school projects on machine learning. We address social and educational challenges in artificial intelligence including security, privacy and education. We employ the participatory co-design approach, which facilitates children's right to be heard, and positions them as active partners, advisers, and designers in research and development work on technology and socio-technological practices.