AI’s longstanding promise to improve education is now expanded and challenged by communicative AI and mobile AI use, highlighting both the opportunities and risks of automated feedback systems and privacy-preserving mobile-tracking methods that reveal how learners actually use AI tools in real learning contexts. These developments suggest that AI is becoming deeply embedded in both the organizational structures of educational institutions and the everyday practices of learners. They also underscore the need for responsible, privacy-aware approaches to studying AI-supported learning in real-world mobile environments.
Prof. Dr Andreas Breiter, University of Bremen
Starting with an overview of the historical paths of AI in education, I would like to highlight that the hope of using AI (or other educational technologies) to solve fundamental challenges for teaching and learning processes (such as learning effectiveness, individualization, equality, etc.) has been part of the narrative from the beginning. The new wave of AI-based tools, particularly communicative AI, raises the question of what happens when AI-based systems become an integral part of automated communication in teaching and learning, fostering deep learning. Based on our prototypical implementations I will focus on opportunities and risks of automated feedback systems for learners and how they can be embedded in the organisational processes of education institutions.
Prof. Dr Philipp Krieter, University of Applied Sciences Düsseldorf,
Smartphones have changed the way we learn, gather information, communicate, socialize, work, participate in social life, and many other aspects of our lives. But how do learners actually use AI on their mobile devices in the learning process? How often, for what purposes, and in what mobile usage contexts? To answer such questions, we need highly detailed data on mobile interactions without violating users’ privacy. However, logging the use of mobile apps is limited to very general system events, unless you have access to the source code of the operating system or the applications. Long-term screen recordings, on the other hand, shed light on precisely this “black box” of in-app behavior, regardless of the app. The automated analysis of these screen recordings using artificial intelligence, computer vision, and machine learning enables the capture of specific in-app activities (such as the use of an AI tool in the learning process). A strict local-first approach allows data to be collected in a privacy-friendly manner. The presentation will introduce the general idea of this mobile tracking approach and current research perspectives.
Moderation: Prof. Dr. Hendrik Heuer (CAIS)