Browsing by Person "Ruiner, Caroline"

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Labour market collectivism: New solidarities of highly skilled freelance workers in medicine, IT and the film industry
(2022) Apitzsch, Birgit; Wilkesmann, Maximiliane; Ruiner, Caroline; Bassyiouny, Mona; Ehlen, Ronny; Schulz, Lena
Highly skilled freelance workers are mainly depicted as a challenge to trade unionism because of their mobility, market power and specific interests in organisational support. The authors explore the manifestations of collectivism of highly skilled freelance workers on the basis of semi-structured interviews with 14 highly skilled freelancers and 35 representatives of intermediaries such as trade unions, professional associations, staffing agencies and cooperatives in medicine, IT and film in Germany. The results reveal new forms and dynamics of labour market collectivism arising from concurrent conflicts and negotiations of job access and working conditions.
Rahmenkonzept der Universitäten des Landes Baden-Württemberg für das High-Performance Computing (HPC) und Data-Intensive Computing (DIC) für den Zeitraum 2025 bis 2032
(2023) von Suchodoletz, Dirk; Heuveline, Vincent; Farrenkopf, Stefan; Neumair, Bernhard; Kohl-Frey, Oliver; Pfister, Alexander; Beutner, Jörg; Resch, Michael; Walter, Thomas; Nau, Thomas; Dorn, Raphael; Frank, Martin; Ruiner, Caroline; Schneider, Gerhard; Wesner, Stefan
Unlocking the power of generative AI models and systems such asGPT-4 and ChatGPT for higher education
(2023) Vandrik, Steffen; Urbach, Nils; Gimpel, Henner; Hall, Kristina; Decker, Stefan; Eymann, Torsten; Lämmermann, Luis; Mädche, Alexander; Röglinger, Maximilian; Ruiner, Caroline; Schoch, Manfred; Schoop, Mareike
Generative AI technologies, such as large language models, have the potential to revolutionize much of our higher education teaching and learning. ChatGPT is an impressive, easy-to-use, publicly accessible system demonstrating the power of large language models such as GPT-4. Other compa- rable generative models are available for text processing, images, audio, video, and other outputs – and we expect a massive further performance increase, integration in larger software systems, and diffusion in the coming years. This technological development triggers substantial uncertainty and change in university-level teaching and learning. Students ask questions like: How can ChatGPT or other artificial intelligence tools support me? Am I allowed to use ChatGPT for a seminar or final paper, or is that cheating? How exactly do I use ChatGPT best? Are there other ways to access models such as GPT-4? Given that such tools are here to stay, what skills should I acquire, and what is obsolete? Lecturers ask similar questions from a different perspective: What skills should I teach? How can I test students’ competencies rather than their ability to prompt generative AI models? How can I use ChatGPT and other systems based on generative AI to increase my efficiency or even improve my students’ learning experience and outcomes? Even if the current discussion revolves around ChatGPT and GPT-4, these are only the forerunners of what we can expect from future generative AI-based models and tools. So even if you think ChatGPT is not yet technically mature, it is worth looking into its impact on higher education. This is where this whitepaper comes in. It looks at ChatGPT as a contemporary example of a conversational user interface that leverages large language models. The whitepaper looks at ChatGPT from the perspective of students and lecturers. It focuses on everyday areas of higher education: teaching courses, learning for an exam, crafting seminar papers and theses, and assessing students’ learning outcomes and performance. For this purpose, we consider the chances and concrete application possibilities, the limits and risks of ChatGPT, and the underlying large language models. This serves two purposes: • First, we aim to provide concrete examples and guidance for individual students and lecturers to find their way of dealing with ChatGPT and similar tools. • Second, this whitepaper shall inform the more extensive organizational sensemaking processes on embracing and enclosing large language models or related tools in higher education. We wrote this whitepaper based on our experience in information systems, computer science, management, and sociology. We have hands-on experience in using generative AI tools. As professors, postdocs, doctoral candidates, and students, we constantly innovate our teaching and learning. Fully embracing the chances and challenges of generative AI requires adding further perspectives from scholars in various other disciplines (focusing on didactics of higher education and legal aspects), university administrations, and broader student groups. Overall, we have a positive picture of generative AI models and tools such as GPT-4 and ChatGPT. As always, there is light and dark, and change is difficult. However, if we issue clear guidelines on the part of the universities, faculties, and individual lecturers, and if lecturers and students use such systems efficiently and responsibly, our higher education system may improve. We see a greatchance for that if we embrace and manage the change appropriately.