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Steffen Steinert; Karina E. Avila; Stefan Ruzika; Jochen Kuhn; Stefan Küchemann – Smart Learning Environments, 2024
Effectively supporting students in mastering all facets of self-regulated learning is a central aim of teachers and educational researchers. Prior research could demonstrate that formative feedback is an effective way to support students during self-regulated learning. In this light, we propose the application of Large Language Models (LLMs) to…
Descriptors: Formative Evaluation, Feedback (Response), Natural Language Processing, Artificial Intelligence
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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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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