Physics instructors’ acceptance and implementation of generative AI
[This paper is part of the Focused Collection in Artificial Intelligence Tools in Physics Teaching and Physics Education Research.] This study investigates physics instructors’ acceptance and implementation of generative AI (GenAI) in physics education, guided by Rogers’ diffusion of innovation (DOI...
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| Format: | Article |
| Language: | English |
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American Physical Society
2025-06-01
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| Series: | Physical Review Physics Education Research |
| Online Access: | http://doi.org/10.1103/r2fn-kdy4 |
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| _version_ | 1850244462007025664 |
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| author | Pornrat Wattanakasiwich Kreetha Kaewkhong Duanghatai Katwibun |
| author_facet | Pornrat Wattanakasiwich Kreetha Kaewkhong Duanghatai Katwibun |
| author_sort | Pornrat Wattanakasiwich |
| collection | DOAJ |
| description | [This paper is part of the Focused Collection in Artificial Intelligence Tools in Physics Teaching and Physics Education Research.] This study investigates physics instructors’ acceptance and implementation of generative AI (GenAI) in physics education, guided by Rogers’ diffusion of innovation (DOI) theory, focusing on its five-stage innovation-decision process: knowledge, persuasion, decision, implementation, and confirmation. Two survey versions were developed—one for GenAI users and another for nonusers. Data were collected through an online survey featuring five-point Likert scale items and open-ended questions, yielding 320 responses from high school and university physics instructors. The persuasion stage was explored in depth using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). A mixed-method approach combining quantitative structural equation modeling (SEM) and qualitative content analysis was employed. We found instructors distributed across different adoption stages, with key barriers including insufficient technical knowledge (51.75% of nonusers) and language processing limitations (50.5% of users). Hedonic motivation (β=0.498) was found to have a substantially stronger influence on adoption than performance expectancy (β=0.121), with an effect size approximately 4 times greater. We identified significant differences between paid and free GenAI users, with paid users more likely to request prompt-writing guidance (30.56% vs 6.47%) despite using more advanced models. Support needs also varied substantially, as half of paid users requested budget support for GenAI services compared to only 17.65% of free users. Most physics instructors used GenAI primarily for assessment tasks, particularly for generatingphysics problems with solutions. Their primary concerns included incorrect physics information in GenAI responses, potential negative impacts on students’ analytical thinking, language barriers, and challenges with prompt writing. These findings suggest that successful GenAI integration in physics education requires physics-specific training and differentiated support based on whether instructors use free or paid versions of GenAI tools. |
| format | Article |
| id | doaj-art-487e53b010814a7d9f195146cef6fe92 |
| institution | OA Journals |
| issn | 2469-9896 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Physics Education Research |
| spelling | doaj-art-487e53b010814a7d9f195146cef6fe922025-08-20T01:59:43ZengAmerican Physical SocietyPhysical Review Physics Education Research2469-98962025-06-0121101015510.1103/r2fn-kdy4Physics instructors’ acceptance and implementation of generative AIPornrat WattanakasiwichKreetha KaewkhongDuanghatai Katwibun[This paper is part of the Focused Collection in Artificial Intelligence Tools in Physics Teaching and Physics Education Research.] This study investigates physics instructors’ acceptance and implementation of generative AI (GenAI) in physics education, guided by Rogers’ diffusion of innovation (DOI) theory, focusing on its five-stage innovation-decision process: knowledge, persuasion, decision, implementation, and confirmation. Two survey versions were developed—one for GenAI users and another for nonusers. Data were collected through an online survey featuring five-point Likert scale items and open-ended questions, yielding 320 responses from high school and university physics instructors. The persuasion stage was explored in depth using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). A mixed-method approach combining quantitative structural equation modeling (SEM) and qualitative content analysis was employed. We found instructors distributed across different adoption stages, with key barriers including insufficient technical knowledge (51.75% of nonusers) and language processing limitations (50.5% of users). Hedonic motivation (β=0.498) was found to have a substantially stronger influence on adoption than performance expectancy (β=0.121), with an effect size approximately 4 times greater. We identified significant differences between paid and free GenAI users, with paid users more likely to request prompt-writing guidance (30.56% vs 6.47%) despite using more advanced models. Support needs also varied substantially, as half of paid users requested budget support for GenAI services compared to only 17.65% of free users. Most physics instructors used GenAI primarily for assessment tasks, particularly for generatingphysics problems with solutions. Their primary concerns included incorrect physics information in GenAI responses, potential negative impacts on students’ analytical thinking, language barriers, and challenges with prompt writing. These findings suggest that successful GenAI integration in physics education requires physics-specific training and differentiated support based on whether instructors use free or paid versions of GenAI tools.http://doi.org/10.1103/r2fn-kdy4 |
| spellingShingle | Pornrat Wattanakasiwich Kreetha Kaewkhong Duanghatai Katwibun Physics instructors’ acceptance and implementation of generative AI Physical Review Physics Education Research |
| title | Physics instructors’ acceptance and implementation of generative AI |
| title_full | Physics instructors’ acceptance and implementation of generative AI |
| title_fullStr | Physics instructors’ acceptance and implementation of generative AI |
| title_full_unstemmed | Physics instructors’ acceptance and implementation of generative AI |
| title_short | Physics instructors’ acceptance and implementation of generative AI |
| title_sort | physics instructors acceptance and implementation of generative ai |
| url | http://doi.org/10.1103/r2fn-kdy4 |
| work_keys_str_mv | AT pornratwattanakasiwich physicsinstructorsacceptanceandimplementationofgenerativeai AT kreethakaewkhong physicsinstructorsacceptanceandimplementationofgenerativeai AT duanghataikatwibun physicsinstructorsacceptanceandimplementationofgenerativeai |