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Dazhen Tong; Yang Tao; Kangkang Zhang; Xinxin Dong; Yangyang Hu; Sudong Pan; Qiaoyi Liu – Asia Pacific Education Review, 2024
Artificial intelligence (AI) technologies have been consistently influencing the progress of education for an extended period, with its impact becoming more significant especially after the launch of ChatGPT-3.5 at the end of November 2022. In the field of physics education, recent research regarding the performance of ChatGPT-3.5 in solving…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Performance
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Rebeckah K. Fussell; Emily M. Stump; N. G. Holmes – Physical Review Physics Education Research, 2024
Physics education researchers are interested in using the tools of machine learning and natural language processing to make quantitative claims from natural language and text data, such as open-ended responses to survey questions. The aspiration is that this form of machine coding may be more efficient and consistent than human coding, allowing…
Descriptors: Physics, Educational Researchers, Artificial Intelligence, Natural Language Processing
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Jennifer Campbell; Katie Ansell; Tim Stelzer – Physical Review Physics Education Research, 2024
Recent advances in publicly available natural language processors (NLP) may enhance the efficiency of analyzing student short-answer responses in physics education research (PER). We train a state-of-the-art NLP, IBM's Watson, and test its agreement with human coders using two different studies that gathered text responses in which students…
Descriptors: Artificial Intelligence, Physics, Natural Language Processing, Computer Uses in Education
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Julia Lademann; Jannik Henze; Sebastian Becker-Genschow – Physical Review Physics Education Research, 2025
This work explores the integration of artificial intelligence (AI) custom chatbots in educational settings, with a particular focus on their applicability in the context of mathematics and physics. In view of the increasing deployment of AI tools such as ChatGPT in educational contexts, the present study explores their potential in generating…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Technology Uses in Education
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Alexis Buzzell; Timothy J. Atherton; Ramón Barthelemy – Physical Review Physics Education Research, 2025
[This paper is part of the Focused Collection in Investigating and Improving Quantum Education through Research.] The modern physics course is a crucial gateway for physics majors as it provides an introduction to concepts beyond the scope of the K-12 education. This study collected 167 modern physics syllabi from 127 U.S. research-intensive…
Descriptors: Physics, Course Content, Science Instruction, Required Courses
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Colin Green; Eric Brewe; Jillian Mellen; Adrienne Traxler; Sarah Scanlin – Physical Review Physics Education Research, 2024
This project aims to understand physics faculty responses to transitioning to online teaching during the COVID-19 pandemic. We surveyed 662 physics faculty from the United States following the Spring 2020 term; of these, 258 completed a follow-up survey after the Fall 2020 term. We used natural language processing to measure the sentiment scores…
Descriptors: Teacher Attitudes, Online Courses, Physics, Science Instruction
Xiaoming Zhai, Editor; Joseph Krajcik, Editor – Oxford University Press, 2025
In the age of rapid technological advancements, the integration of Artificial Intelligence (AI), machine learning (ML), and large language models (LLMs) in Science, Technology, Engineering, and Mathematics (STEM) education has emerged as a transformative force, reshaping pedagogical approaches and assessment methodologies. "Uses of AI in STEM…
Descriptors: Artificial Intelligence, STEM Education, Technology Uses in Education, Educational Technology