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  • Open Access

Physics instructors’ acceptance and implementation of generative AI

Pornrat Wattanakasiwich1,*, Kreetha Kaewkhong2, and Duanghathai Katwibun2

  • 1Department of Physics and Material Sciences, Faculty of Science, Chiang Mai University, Chiang Mai, 50200 Thailand
  • 2Department of Curriculum, Teaching & Learning, Faculty of Education, Chiang Mai University, Chiang Mai, 50200 Thailand

  • *Contact author: pornrat.w@cmu.ac.th
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Phys. Rev. Phys. Educ. Res. 21, 010155 – Published 2 June, 2025

DOI: https://doi.org/10.1103/r2fn-kdy4

Abstract

[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.

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Physics Subject Headings (PhySH)

Corrections

12 December, 2025

Correction: The third author’s name has been changed from Duanghatai Katwibun to Duanghathai Katwibun.

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This article appears in the following collection:

Focused Collection in Artificial Intelligence Tools in Physics Teaching and Physics Education Research

Focused Collection in Artificial Intelligence Tools in Physics Teaching and Physics Education Research

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Outline

Information

Phys. Rev. Phys. Educ. Res. 21, 010155– Published 2 June, 2025
  • Received 18 December 2024
  • Accepted 30 April 2025
  • Corrected 12 December 2025
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