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Giulia Polverini; Bor Gregorcic – Physical Review Physics Education Research, 2024
The well-known artificial intelligence-based chatbot ChatGPT-4 has become able to process image data as input in October 2023. We investigated its performance on the test of understanding graphs in kinematics to inform the physics education community of the current potential of using ChatGPT in the education process, particularly on tasks that…
Descriptors: Computer Software, Artificial Intelligence, Visual Impairments, Graphs
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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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Dahlkemper, Merten Nikolay; Lahme, Simon Zacharias; Klein, Pascal – Physical Review Physics Education Research, 2023
This study aimed at evaluating how students perceive the linguistic quality and scientific accuracy of ChatGPT responses to physics comprehension questions. A total of 102 first- and second-year physics students were confronted with three questions of progressing difficulty from introductory mechanics (rolling motion, waves, and fluid dynamics).…
Descriptors: Physics, Science Instruction, Artificial Intelligence, Computer Software
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Jannis Weber; Thomas Wilhelm – Physical Review Physics Education Research, 2024
Students experience many difficulties learning the fundamental relationships in Newtonian mechanics, partly due to preexisting mental models that originate from their everyday lives. These preconceptions often persist even after instruction in mechanics and lead to a supposed incompatibility between physics lessons in school and personal…
Descriptors: Physics, Science Instruction, Scientific Concepts, Mechanics (Physics)
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Rebeckah K. Fussell; Megan Flynn; Anil Damle; Michael F. J. Fox; N. G. Holmes – Physical Review Physics Education Research, 2025
Recent advancements in large language models (LLMs) hold significant promise for improving physics education research that uses machine learning. In this study, we compare the application of various models for conducting a large-scale analysis of written text grounded in a physics education research classification problem: identifying skills in…
Descriptors: Physics, Computational Linguistics, Classification, Laboratory Experiments
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Tong Wan; Zhongzhou Chen – Physical Review Physics Education Research, 2024
Instructor's feedback plays a critical role in students' development of conceptual understanding and reasoning skills. However, grading student written responses and providing personalized feedback can take a substantial amount of time, especially in large enrollment courses. In this study, we explore using GPT-3.5 to write feedback on students'…
Descriptors: Physics, Science Instruction, Artificial Intelligence, Computer Software
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Gerd Kortemeyer; Julian Nöhl; Daria Onishchuk – Physical Review Physics Education Research, 2024
[This paper is part of the Focused Collection in Artificial Intelligence Tools in Physics Teaching and Physics Education Research.] Using a high-stakes thermodynamics exam as the sample (252 students, four multipart problems), we investigate the viability of four workflows for AI-assisted grading of handwritten student solutions. We find that the…
Descriptors: Grading, Physics, Science Instruction, Artificial Intelligence
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Justin Gambrell; Eric Brewe – Physical Review Physics Education Research, 2024
Computational thinking in physics has many different forms, definitions, and implementations depending on the level of physics or the institution it is presented in. To better integrate computational thinking in introductory physics, we need to understand what physicists find important about computational thinking in introductory physics. We…
Descriptors: Physics, Introductory Courses, Science Instruction, Thinking Skills
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Phillips, A. M.; Gouvea, E. J.; Gravel, B. E.; Beachemin, P. -H.; Atherton, T. J. – Physical Review Physics Education Research, 2023
Computation is intertwined with essentially all aspects of physics research and is invaluable for physicists' careers. Despite its disciplinary importance, integration of computation into physics education remains a challenge and, moreover, has tended to be constructed narrowly as a route to solving physics problems. Here, we broaden Physics…
Descriptors: Physics, Science Instruction, Teaching Methods, Models
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Victoria Borish; H. J. Lewandowski – Physical Review Physics Education Research, 2025
As quantum technologies transition from the research laboratory into commercial development, the opportunities for students to begin their careers in this new quantum industry are increasing. With these new career pathways, more and more people are considering the best ways to educate students about quantum concepts and relevant skills. In…
Descriptors: Physics, Science Instruction, Quantum Mechanics, Computer Software
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Kortemeyer, Gerd – Physical Review Physics Education Research, 2023
Massive pretrained language models have garnered attention and controversy due to their ability to generate humanlike responses: Attention due to their frequent indistinguishability from human-generated phraseology and narratives and controversy due to the fact that their convincingly presented arguments and facts are frequently simply false. Just…
Descriptors: Artificial Intelligence, Physics, Science Instruction, Introductory Courses
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Mashood, K. K.; Khosla, Kamakshi; Prasad, Arjun; V., Sasidevan; Ashefas CH, Muhammed; Jose, Charles; Chandrasekharan, Sanjay – Physical Review Physics Education Research, 2022
Recent educational policies advocate a radical revision of science curricula and pedagogy, to support interdisciplinary practices, a distinguishing feature of contemporary science. Computational modeling (CM) is a core methodology of interdisciplinary science, as such models allow intertwining of data and theoretical perspectives from multiple…
Descriptors: Teaching Methods, Undergraduate Students, Science Instruction, Science Curriculum
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Porter, C. D.; Smith, J. R. H.; Stagar, E. M.; Simmons, A.; Nieberding, M.; Orban, C. M.; Brown, J.; Ayers, A. – Physical Review Physics Education Research, 2020
Recent years have seen a resurgence of interest in using virtual reality (VR) technology to benefit instruction, especially in physics and related subjects. As VR devices improve and become more widely available, there remains a number of unanswered questions regarding the impact of VR on student learning and how best to use this technology in the…
Descriptors: Physics, Science Instruction, Video Games, Computer Simulation
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Langbeheim, Elon; Abrashkin, Ariel; Steiner, Ariel; Edri, Haim; Safran, Samuel; Yerushalmi, Edit – Physical Review Physics Education Research, 2020
This article describes the redesign of a project-based course on soft and biological materials to include computational modeling. Including the construction of computational models in the course is described as a shift from constructivism--a theory that characterizes the development of formal reasoning, to constructionism--a theory that focuses on…
Descriptors: Physics, Science Instruction, Educational Change, Curriculum Design