The track is dedicated to researching the topic of “Digital Education” and examines the influence of digitalization on both companies and educational institutions such as universities, schools and other educational institutions. In particular, it examines how digital technologies, such as artificial intelligence (AI), influence the design of teaching-learning processes – whereby embedding in the socio-technical system or the teaching-learning arrangement should always be considered, from blended learning solutions and flipped classroom concepts to fully digital teaching-learning concepts such as MOOCs. New technologies such as the metaverse, augmented reality and mobile learning solutions for work processes also offer additional potential for digital learning and expand the possibilities of digital education and its exploration.
Aspects that examine the influence of artificial intelligence on teaching and learning also play a role here, in particular the contribution of generative AI, which is fundamentally changing and transforming learning. In this context, questions also arise about the skills and abilities that learners need in the digital age in order to be successful on the labor market and how these can be systematically developed. The aim is to recognize the opportunities and potential of digitalization and artificial intelligence in teaching, to develop digital skills and to adapt business processes and models. New skills such as prompt engineering are emerging, which means that further research is needed to conceptualize these digital skills.
However, digitalization and the increasing integration and presence of artificial intelligence also give rise to new risks. For example, the unconsidered use of new technologies can lead to problems such as superficial learning through copy-pasting, a lack of in-depth learning processes and reduced learner autonomy. An important aspect of research is whether and how artificial intelligence can be used effectively in teaching and how both students and teachers can be motivated to use these technologies effectively.
In this context, in addition to universities and educational institutions, companies are also required to design digital work processes in such a way that employees can further develop their skills and the organization can build the necessary capabilities to use knowledge successfully. New AI tools also enable learners to familiarize themselves effectively with new subject areas, which is particularly important for work process-oriented learning. In this context, it is important to research which conditions and methods support the successful use of such technologies, such as the promotion of motivation, the design of practical learning environments and integration into existing work processes. In addition to design-oriented contributions, quantitative and qualitative approaches that include evaluations and testing of approaches to the design of digital teaching are also welcome.
Track Topics:
- Digital skills at individual level (e.g. prompt engineering) and at group work level
- Promotion of digital skills in educational institutions and organizations for the use of artificial intelligence in teaching and learning concepts
- Novel concepts of learning through and with artificial intelligence in organizations
- New approaches for the design of school lessons in the age of digitalization
- AI-based systems in (higher) education and for learning in the workplace
- Gamification and serious gaming in digital teaching and personalization of playful approaches using artificial intelligence
- Development of hybrid intelligence between humans and AI-based systems
- Nudging for learning in the (digital) workplace
- Generative AI in the workplace to establish teaching and learning concepts
- Use of voicebots and chatbots in digital teaching
- Challenges of using generative AI in teaching
- Digital approaches to support learning processes, such as scaffolding
- Potential of the metaverse and augmented reality for digital education
- Conditions and methods for the successful use of AI tools in practical learning environments
- Risks and challenges of the unconsidered use of artificial intelligence in learning
Track chairs
AEs
- Roman Rietsche, Berner Fachhochschule
- Dennis Benner, Universität Kassel
- Christian Koldewey, Universität Paderborn
- Michael Breitner, Leibniz Universität Hannover
- Matthias Söllner, Universität Kassel
- Stefan Thalmann, Universität Graz
- Sebastian Hobert, Technische Hochschule Lübeck
- Jeanine Kirchner-Krath, FAU Nürnberg
- Matthias Schumann, Universität Göttingen
- Philipp Ebel, Universität St. Gallen
- Bastian Kordyaka, Abo Akademi Universität
- Anuschka Schmitt, London School of Economics and Political Science (LSE)
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