This paper presents an eXplainable AI (XAI)-based classroom game “Breakable Machine” for teaching critical, transformative AI literacy through adversarial play and interrogation of AI systems. Designed for learners aged 10–15, the game invites students to spoof an image classifier by manipulating their appearance or environment in order to trigger high-confidence misclassifications. Rather than focusing on building AI models, this activity centers on breaking them—exposing their brittleness, bias, and vulnerability through hands-on, embodied experimentation. The game includes an XAI view to help students visualize feature saliency, revealing how models attend to specific visual cues. A shared classroom leaderboard fosters collaborative inquiry and comparison of strategies, turning the classroom into a site for collective sensemaking. This approach repositions AI education by treating model failure and misclassification not as problems to be debugged, but as pedagogically rich opportunities to interrogate AI as a sociotechnical system. In doing so, the game supports students in developing data agency, ethical awareness, and a critical stance toward AI systems increasingly embedded in everyday life.

In the fast-growing field of K–12 AI education, there is an urgent need for accessible, hands-on tools that introduce AI concepts and workflows to novice learners. In recent years, a variety of AI education tools have been introduced, ranging from coding environments to physical kits and robots. To provide an alternative to existing AI education tools, this paper presents a low-cost robotics kit (

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.

Existing communication technologies have displayed a lack of affordances in supporting social-emotional connections, which is of particular interest in educational settings. We are therefore developing a live sensory immersive 3D video technology, built on a prior developed platform. Pilot trials in a Finnish school have yielded promising findings. We continue to advance the state-of-the-art platform in parallel with regards to 3D capture quality and data compression algorithms. Current developments entail joint investigations and evaluations of affordances to support emotional, social, motivational, and achievement impacts with learners and teachers from a Namibian and Finnish school. Participants can experience ”remote presence” wearing the hololens 2 while others are live streamed from another country captured by two cameras.

Work in Progress: In this paper we outline our initial findings on the potential of state-of-the-art immersive telepresence to support the practical and collaborative group work element in our remotely taught international degree programmes, specifically a Software Engineering Masters degree. Whilst we adopt widely used distance learning approaches generally, the challenge remains in supporting live synchronous practical sessions that depend heavily, on one hand, on the expertise of the educator and their presence in the shared learning environment, and on the other hand, the students' active engagement within teams partially distributed physically. Any technical progress in improving the "sense of presence" can radically renew our conceptions of (distance) teaching and learning, maybe also in terms of the 21st century skills or even those that we are not yet aware of. The question to address is that if sufficiently high-fidelity, and unintrusive, immersive capture technology is used then how could this be game changing in this area? Previous work using similar immersive technology has failed to achieve the level of quality or scope necessary, however, the past 4 years have seen significant progress in the hardware and algorithms required. To support our degree programmes we have designed and developed a custom live 3D capture system for a higher fidelity immersive experience targeting small groups of 2-6 people collaborating both locally and remotely. Here we will present the system and scope out some of the initial plausible affordances for education through the Conceive, Design, Implement, Operate (CDIO)-model. We focus on the affordances offered by the technology to support the learning of the much needed competences of communication, collaboration, critical thinking and creativity which are very challenging to enhance by conventional, content delivery oriented distance learning approaches.