focusing on using data of human-human interaction to build and evaluate computational models that support social interactions between people and machines. Martin Dobricki is a senior scientist in the learning technologies research group at the Swiss Federal Institute for Vocational Education and Training (SFIVET) focusing on vocational learning and teaching through immersive virtual reality technology. Jennifer K. Olsen is a postdoctoral researcher at EPFL in the CHILI lab researching educational technologies and learning analytics. Alessia E. Coppi is a Ph.D. student at SFIVET interested in vocational education, new technologies and games, usability and cognitive enhancement. Alberto Cattaneo is a professor at SFIVET focusing on learning technologies, instructional design and teacher education. PierreDillenbourg is a professor in the School of Computer and Communication Sciences at EPFL and the head of the CHILI lab where he researches learning technologies and educational robotics.

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In this paper, we present a response to the Interest-Driven Creator (IDC) theory from a European perspective. Specifically, we raise six questions intended to start a dialog with respect to IDC theory's placement in existing learning theories, its adoption in educational systems, and how it can be influenced by emerging learning technologies and digitalization, which is currently a driving force in the Alps region. By referring to our own work in vocational education and classroom orchestration, we demonstrate how IDC can begin to play a part in guiding innovations and its potential impact on education both in and outside of Asia. With respect to digitalization, rather than allowing technological innovations to fully guide educational decisions, we call for IDC theory to be part of the conversation to help guide future educational designs.

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This paper investigates the potential impact of deep generative models on the work of creative professionals, specifically focusing on fashion design. We argue that current generative modeling tools lack critical features that would make them useful creativity support tools, and introduce our own tool, generative.fashion 1 , which was designed with theoretical principles of design space exploration in mind. Through qualitative studies with fashion design apprentices, we demonstrate how generative.fashion supported both divergent Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).