Unlocking the Power of Generative AI Models and Systems such as GPT-4 and ChatGPT for Higher EducationA Guide for Students and Lecturers
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Zusammenfassungen
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 comparable 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 Given that such tools are here to stay, what skills should I acquire, and what is obsolete?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:
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 great chance for that if we embrace and manage the change appropriately.
Von Henner Gimpel, Kristina Hall, Stefan Decker, Torsten Eymann, Luis Lämmermann, Alexander Mädche, Maximilian Röglinger, Caroline Ruiner, Manfred Schoch, Mareike Schoop, Nils Urbach, Steffen Vandirk in der Broschüre Unlocking the Power of Generative AI Models and Systems such as GPT-4 and ChatGPT for Higher Education (2023) 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.
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 great chance for that if we embrace and manage the change appropriately.
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Personen KB IB clear | Sandhini Agarwal , Dario Amodei , Amanda Askell , Christopher Berner , Tom B. Brown , Mark Chen , Benjamin Chess , Rewon Child , Jack Clark , Kewal Dhariwal , Prafulla Dhariwal , Aidan N. Gomez , Scott Gray , Tom Henighan , Ariel Herbert-Voss , Christopher Hesse , Sebastian Hobert , Llion Jones , Lukasz Kaiser , Jared Kaplan , Gretchen Krueger , Mateusz Litwin , Benjamin Mann , Sam McCandlish , Arvind Neelakantan , Niki Parmar , Illia Polosukhin , Alec Radford , Aditya Ramesh , Nick Ryder , Girish Sastry , Noam Shazeer , Pranav Shyam , Eric Sigler , Christian Spannagel , Melanie Subbiah , Ilya Sutskever , Jakob Uszkoreit , Ashish Vaswani , Clemens Winter , Jeffrey Wu , Daniel M. Ziegler | ||||||||||||||||||||||||||||||||||||
Begriffe KB IB clear | Algorithmusalgorithm , Bildungeducation (Bildung) , Chat-GPT , Computercomputer , Generative Machine-Learning-Systeme (GMLS)computer-generated text , Generative Pretrained Transformer 3 (GPT-3) , Generative Pretrained Transformer 4 (GPT-4) , Google , GPT-2 , Informatikcomputer science , Künstliche Intelligenz (KI / AI)artificial intelligence , Lernenlearning , machine learning , Managementmanagement , Microsoft , Softwaresoftware , Sprachelanguage , Statistikstatistics , User Interface (Benutzerschnittstelle)User Interface | ||||||||||||||||||||||||||||||||||||
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Nicht erwähnte Begriffe | Apple, Digitalisierung, GMLS & Bildung, GMLS & Schule, Informatik-Didaktik, Informatik-Unterricht (Fachinformatik), Intelligenz, Kinder, LehrerIn, Schule, Unterricht |
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5 Erwähnungen
- ChatGPT und andere Computermodelle zur Sprachverarbeitung - Grundlagen, Anwendungspotenziale und mögliche Auswirkungen (Steffen Albrecht) (2023)
- Generative künstliche Intelligenz in der Hochschullehre - Positionspapier der HSLU (Stefan Jörissen, David Loher) (2023)
- Zehn Thesen zur Zukunft des Schreibens in der Wissenschaft (Anika Limburg, Ulrike Bohle-Jurok, Isabella Buck, Ella Grieshammer, Johanna Gröpler, Dagmar Knorr, Margret Mundorf, Kirsten Schindler, Nicolaus Wilder) (2023)
- Künstliche Intelligenz, Large Language Models, ChatGPT und die Arbeitswelt der Zukunft (Michael Seemann) (2023)
- Generative KI und betriebliche Bildung/Personalentwicklung - Orientierung – Befähigung – Weiterentwicklung (Christoph Meier) (2024)
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