Keywords
artificial intelligence
AI
perception of AI
teachers
influencing factors
How to Cite
Abstract
Artificial intelligence (AI) is rapidly becoming a part of education, so it is essential for us to understand exactly how teachers relate to these technologies. The study investigates how the intensity and variety of practical use of digital tools, as well as the awareness of ethical and technical barriers, influence the formation of educators' attitudes toward artificial intelligence technologies in their professional activities. This work investigates the mechanisms behind the formation of teachers’ positive attitudes toward tools based on large language models (LLMs). The empirical basis of the research consists of the results of an online survey of 486 pedagogical workers. Mathematical processing and data coding were conducted in the RStudio statistical programming environment using the lavaan package. Structural Equation Modeling (SEM) was applied to test the hypotheses. Model analysis confirmed: the more often a teacher uses AI in practice, the better their attitude toward it. The study showed that teachers trust AI more if they actually use it daily, rather than just knowing about the existence of various services. It is proven that awareness of ethical risks and technical imperfections does not have a statistically significant negative impact on educators’ readiness to use the technology, provided they have successful experience in solving applied tasks with its help. This indicates the dominance of a pragmatic approach among teachers over theoretical fears. The practical significance of the work lies in shifting the focus of professional development programs from lecture-based risk overviews to the intensive mastery of prompt engineering techniques for daily routines. The feasibility of implementing a "depth over breadth" strategy is justified, which involves a transition to relational learning design to preserve the psychological continuity of the user experience amidst rapid technological transformations. The obtained empirical data can serve as a basis for updating the professional standards of the modern educator's digital competence.
References
[1] A. Al Darayseh, “Acceptance of artificial intelligence in teaching science: Science teachers' perspective”, Computers and Education: Artificial Intelligence, vol. 4, Art. no. 100132, 2023, doi: 10.1016/j.caeai.2023.100132.
[2] S. C. Kong, Y. Yang, and C. Hou, “Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model”, Computers and Education: Artificial Intelligence, vol. 7, Art. no. 100328, 2024, doi: 10.1016/j.caeai.2024.100328.
[3] C. Zhang, M. Hu, W. Wu, F. Kamran, and X. Wang, “Unpacking perceived risks and AI trust influences pre-service teachers’ AI acceptance: A structural equation modeling-based multi-group analysis”, Education and Information Technologies, vol. 30, pp. 2645–2672, 2025, doi: 10.1007/s10639-024-12905-7.
[4] X. An, C. S. Chai, Y. Li, Y. Zhou, X. Shen, C. Zheng, and M. Chen, “Modeling English teachers’ behavioral intention to use artificial intelligence in middle schools,” Education and Information Technologies, vol. 28, no. 5, pp. 5187–5208, 2023, doi: 10.1007/s10639-022-11286-z.
[5] A. Hazzan-Bishara, O. Kol, and S. Levy, “The factors affecting teachers’ adoption of AI technologies: A unified model of external and internal determinants”, Education and Information Technologies, vol. 30, pp. 15043–15069, 2025, doi: 10.1007/s10639-025-13393-z.
[6] X. Tan, G. Cheng, and M. H. Ling, “Artificial intelligence in teaching and teacher professional development: A systematic review”, Computers and Education: Artificial Intelligence, vol. 8, Art. no. 100355, 2025, doi: 10.1016/j.caeai.2024.100355.
[7] A. M. Al-Abdullatif, “Modeling teachers’ acceptance of generative artificial intelligence use in higher education: The role of AI literacy, intelligent TPACK, and perceived trust”, Education Sciences, vol. 14, no. 11, Art. no. 1209, 2024, doi: 10.3390/educsci14111209.
[8] S. Guo, L. Shi, and X. Zhai, “Developing and validating an instrument for teachers’ acceptance of artificial intelligence in education”, Education and Information Technologies, vol. 30, pp. 13439–13461, 2025, doi: 10.1007/s10639-025-13338-6.
[9] T. K. F. Chiu, Z. Ahmad, and M. Çoban, “Development and validation of teacher artificial intelligence (AI) competence self-efficacy (TAICS) scale”, Education and Information Technologies, vol. 30, pp. 6667–6685, 2025, doi: 10.1007/s10639-024-13094-z.
[10] Y. Y. Wang and Y. W. Chuang, “Artificial intelligence self-efficacy: Scale development and validation”, Education and Information Technologies, vol. 29, pp. 4785–4808, 2024, doi: 10.1007/s10639-023-12015-w.
[11] L. K. Dietrich and S. Grassini, “Assessing ChatGPT acceptance and use in education: A comparative study among german-speaking students and teachers”, Education and Information Technologies, vol. 30, pp. 22151–22176, 2025, doi: 10.1007/s10639-025-13658-7.
[12] S. Choi, Y. Jang, and H. Kim, “Influence of pedagogical beliefs and perceived trust on teachers’ acceptance of educational artificial intelligence tools”, International Journal of Human-Computer Interaction, vol. 39, no. 4, pp. 910–922, 2023, doi: 10.1080/10447318.2022.2049145.
[13] J. Cabero-Almenara, A. Palacios-Rodríguez, M. I. Loaiza-Aguirre, and M. d. R. d. Rivas-Manzano, “Acceptance of educational artificial intelligence by teachers and its relationship with some variables and pedagogical beliefs”, Education Sciences, vol. 14, no. 7, p. 740, 2024, doi: 10.3390/educsci14070740.
[14] C. K. Y. Chan and K. K. W. Lee, “The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers?”, Smart Learning Environments, vol. 10, p. 60, 2023, doi: 10.1186/s40561-023-00269-3.
[15] N. A. Dahri, N. Yahaya, M. S. Vighio, et al., “Exploring the impact of ChatGPT on teaching performance: Findings from SOR theory, SEM and IPMA analysis approach”, Education and Information Technologies, vol. 30, pp. 18241–18276, 2025, doi: 10.1007/s10639-025-13539-z.
[16] V. Donard and J. C. Ribeiro, “Technological mourning after AI updates: mental health and well-being in the GPT-4o/GPT-5 transition”, Academia Mental Health and Well-Being, vol. 3, no. 2, 2026, doi: 10.20935/MHealthWellB8256.
REFERENCES (TRANSLATED AND TRANSLITERATED)
[1] A. Al Darayseh, “Acceptance of artificial intelligence in teaching science: Science teachers' perspective”, Computers and Education: Artificial Intelligence, vol. 4, Art. no. 100132, 2023, doi: 10.1016/j.caeai.2023.100132. (in English).
[2] S. C. Kong, Y. Yang, and C. Hou, “Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model”, Computers and Education: Artificial Intelligence, vol. 7, Art. no. 100328, 2024, doi: 10.1016/j.caeai.2024.100328. (in English).
[3] C. Zhang, M. Hu, W. Wu, F. Kamran, and X. Wang, “Unpacking perceived risks and AI trust influences pre-service teachers’ AI acceptance: A structural equation modeling-based multi-group analysis”, Education and Information Technologies, vol. 30, pp. 2645–2672, 2025, doi: 10.1007/s10639-024-12905-7. (in English).
[4] X. An, C. S. Chai, Y. Li, Y. Zhou, X. Shen, C. Zheng, and M. Chen, “Modeling English teachers’ behavioral intention to use artificial intelligence in middle schools,” Education and Information Technologies, vol. 28, no. 5, pp. 5187–5208, 2023, doi: 10.1007/s10639-022-11286-z. (in English).
[5] A. Hazzan-Bishara, O. Kol, and S. Levy, “The factors affecting teachers’ adoption of AI technologies: A unified model of external and internal determinants”, Education and Information Technologies, vol. 30, pp. 15043–15069, 2025, doi: 10.1007/s10639-025-13393-z. (in English).
[6] X. Tan, G. Cheng, and M. H. Ling, “Artificial intelligence in teaching and teacher professional development: A systematic review”, Computers and Education: Artificial Intelligence, vol. 8, Art. no. 100355, 2025, doi: 10.1016/j.caeai.2024.100355. (in English).
[7] A. M. Al-Abdullatif, “Modeling teachers’ acceptance of generative artificial intelligence use in higher education: The role of AI literacy, intelligent TPACK, and perceived trust”, Education Sciences, vol. 14, no. 11, Art. no. 1209, 2024, doi: 10.3390/educsci14111209. (in English).
[8] S. Guo, L. Shi, and X. Zhai, “Developing and validating an instrument for teachers’ acceptance of artificial intelligence in education”, Education and Information Technologies, vol. 30, pp. 13439–13461, 2025, doi: 10.1007/s10639-025-13338-6. (in English).
[9] T. K. F. Chiu, Z. Ahmad, and M. Çoban, “Development and validation of teacher artificial intelligence (AI) competence self-efficacy (TAICS) scale”, Education and Information Technologies, vol. 30, pp. 6667–6685, 2025, doi: 10.1007/s10639-024-13094-z. (in English).
[10] Y. Y. Wang and Y. W. Chuang, “Artificial intelligence self-efficacy: Scale development and validation”, Education and Information Technologies, vol. 29, pp. 4785–4808, 2024, doi: 10.1007/s10639-023-12015-w. (in English).
[11] L. K. Dietrich and S. Grassini, “Assessing ChatGPT acceptance and use in education: A comparative study among german-speaking students and teachers”, Education and Information Technologies, vol. 30, pp. 22151–22176, 2025, doi: 10.1007/s10639-025-13658-7. (in English).
[12] S. Choi, Y. Jang, and H. Kim, “Influence of pedagogical beliefs and perceived trust on teachers’ acceptance of educational artificial intelligence tools”, International Journal of Human-Computer Interaction, vol. 39, no. 4, pp. 910–922, 2023, doi: 10.1080/10447318.2022.2049145. (in English).
[13] J. Cabero-Almenara, A. Palacios-Rodríguez, M. I. Loaiza-Aguirre, and M. d. R. d. Rivas-Manzano, “Acceptance of educational artificial intelligence by teachers and its relationship with some variables and pedagogical beliefs”, Education Sciences, vol. 14, no. 7, p. 740, 2024, doi: 10.3390/educsci14070740. (in English).
[14] C. K. Y. Chan and K. K. W. Lee, “The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers?”, Smart Learning Environments, vol. 10, p. 60, 2023, doi: 10.1186/s40561-023-00269-3. (in English).
[15] N. A. Dahri, N. Yahaya, M. S. Vighio, et al., “Exploring the impact of ChatGPT on teaching performance: Findings from SOR theory, SEM and IPMA analysis approach”, Education and Information Technologies, vol. 30, pp. 18241–18276, 2025, doi: 10.1007/s10639-025-13539-z. (in English).
[16] V. Donard and J. C. Ribeiro, “Technological mourning after AI updates: mental health and well-being in the GPT-4o/GPT-5 transition”, Academia Mental Health and Well-Being, vol. 3, no. 2, 2026, doi: 10.20935/MHealthWellB8256. (in English).
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Copyright (c) 2026 Valentyna Kovalenko, Maiia Marienko, Mariya Shyshkina