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Verena Ruf; Yavuz Dinc; Stefan Küchemann; Markus Berndt; Steffen Steinert; Daniela Kugelmann; Jonathan Bortfeldt; Jörg Schreiber; Martin R. Fischer; Jochen Kuhn – Physical Review Physics Education Research, 2024
Graphical representations of data are common in many disciplines. Previous research has found that physics students appear to have better graph comprehension skills than students from social science disciplines, regardless of the task context. However, the graph comprehension skills of physics students have not yet been compared with (veterinary)…
Descriptors: Artificial Intelligence, Graphs, Comprehension, College Freshmen
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Fabian Kieser; Peter Wulff; Jochen Kuhn; Stefan Küchemann – Physical Review Physics Education Research, 2023
Generative AI technologies such as large language models show novel potential to enhance educational research. For example, generative large language models were shown to be capable of solving quantitative reasoning tasks in physics and concept tests such as the Force Concept Inventory (FCI). Given the importance of such concept inventories for…
Descriptors: Physics, Science Instruction, Artificial Intelligence, Computer Software
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Stefan Küchemann; Steffen Steinert; Natalia Revenga; Matthias Schweinberger; Yavuz Dinc; Karina E. Avila; Jochen Kuhn – Physical Review Physics Education Research, 2023
The recent advancement of large language models presents numerous opportunities for teaching and learning. Despite widespread public debate regarding the use of large language models, empirical research on their opportunities and risks in education remains limited. In this work, we demonstrate the qualities and shortcomings of using ChatGPT 3.5…
Descriptors: Artificial Intelligence, Natural Language Processing, Man Machine Systems, Physics