Antecedents of Norwegian High School Teachers’ AI Resistance

Eyvind Elstad; Harald Eriksen

Antecedents of Norwegian High School Teachers’ AI Resistance

Abstract

This study aims to explore the factors influencing Norwegian high school teachers’ instructional resistance to artificial intelligence (AI) in terms of age, instructional AI efficacy, and collective AI beliefs among school staff. Grounded in a robust theoretical framework that integrates technology-use models, social cognitive theory, and organizational culture theory, the article presents the multifaceted challenges and opportunities associated with AI integration in educational settings. The study involved examining the perceptions of 223 teachers, using structural equation modelling to investigate the antecedents of instructional AI resistance. The analysis reveals that the most significant relationship exists between instructional AI efficacy and AI resistance, highlighting the crucial role of teacher competence and confidence in technology adoption. By contrast, the link between age and AI resistance is weaker, reflecting age-related nuances in acceptance patterns. Additionally, the pathway between collective AI beliefs and AI resistance shows a low negative correlation, underscoring the importance of fostering positive communal attitudes for reducing resistance. As anticipated, there is a moderate positive correlation between instructional AI efficacy and teachers’ shared AI beliefs, suggesting a synergistic association that enhances community-wide opinion of AI’s possibilities. These findings are discussed, and insights and recommendations for practice and directions for future research are offered.

Keywords

Ethical Statement

This study was conducted in accordance with the highest ethical standards to ensure the protection and dignity of all participants involved. Informed consent was obtained from all participants, who were thoroughly briefed on the purpose and procedures of the study. Participants were assured of their right to withdraw from the study at any point without repercussions and were informed that their identities would remain confidential. Anonymity was preserved by detaching any identifying information from the data collected and securely storing responses to prevent unauthorized access.

References

Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), Article ep429. https://doi.org/10.30935/cedtech/13152
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall.
Beck, C., Kosnik, C., & Rosales, E. (2017). Longitudinal study of teachers. In K. Hytten (Ed.), Oxford research encyclopedia of education. https://doi.org/10.1093/acrefore/9780190264093.013.117
Bjork, R. A., & Bjork, E. L. (2020). Desirable difficulties in theory and practice. Journal of Applied Research in Memory and Cognition, 9(4), 475–479.
Brandhofer, G., & Tengler, K. (2024). Acceptance of intelligence in education: Opportunities, concerns, and need for action. Advances in Mobile Learning Educational Research, 4(2), 1105–1113. https://doi.org/10.25082/AMLER.2024.02.005
Brevik, S. M., & Dahl, I. D. (2024, February 8). ChatGPT i skolen: – Et jukseverktøy. NRK. https://www.nrk.no/rogaland/chatgpt-i-skolen_-_-et-jukseverktoy-1.16711327
Bryk, A., & Schneider, B. (2002). Trust in schools: A core resource for improvement. Russell Sage Foundation.
Cabero-Almenara, J., Palacios-Rodríguez, A., Loaiza-Aguirre, M. I., & Andrade-Abarca, P. S. (2024). The impact of pedagogical beliefs on the adoption of generative AI in higher education: Predictive model from UTAUT2. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1497705

Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138. https://doi.org/10.1016/j.chb.2022.107468
Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66(4), 616–630. https://doi.org/10.1007/s11528-022-00715-y
Chiu, T. K., Falloon, G., Song, Y., Wong, V. W., Zhao, L., & Ismailov, M. (2024a). A self-determination theory approach to teacher digital competence development. Computers & Education, 214, Article 105017. https://doi.org/10.1016/j.compedu.2024.105017
Chiu, T. K., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2024b). Teacher support and student motivation to learn with artificial intelligence (AI) based chatbot. Interactive Learning Environments, 32(7), 3240–3256. https://doi.org/10.1080/10494820.2023.2172044
Chocarro, R., Cortiñas, M., & Marcos-Matás, G. (2023). Teachers’ attitudes towards chatbots in education: A technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics. Educational Studies, 49(2), 295–313. https://doi.org/10.1080/03055698.2020.1850426
Choi, S., Jang, Y., & Kim, H. (2023). Influence of pedagogical beliefs and perceived trust on teachers’ acceptance of educational artificial intelligence tools. International Journal of Human–Computer Interaction, 39(4), 910–922. https://doi.org/10.1080/10447318.2022.2049145
Chou, C. M., Shen, T. C., Shen, T. C., & Shen, C. H. (2024). Teachers’ adoption of AI-supported teaching behavior and its influencing factors: Using structural equation modeling. Journal of Computers in Education. https://doi.org/10.1007/s40692-024-00332-z
Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education. Routledge.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.
Deng, R., Jiang, M., Yu, X., Lu, Y. & Liu, S. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, Article 105224. https://doi.org/10.1016/j.compedu.2024.105224
Diehl, D. K. (2020). It’s good until it’s not: The curvilinear relationship between affective school engagement and career and technical education participation. Career and Technical Education Research, 45(3), 45–62. https://doi.org/10.5328/cter45.3.45
Doyle, W. (1983). Academic work. Review of Educational Research, 53(2), 159–199. https://doi.org/10.3102/00346543053002159
Drossel, K., & Eickelmann, B. (2017). Teachers’ participation in professional development concerning the implementation of new technologies in class: A latent class analysis. Large-Scale Assessment in Education, 5, Article 19. https://doi.org/10.1186/s40536-017-0053-7
Du, Y., & Sun, Y. (2024). Exploring the effects of AI literacy in teacher learning: An empirical study. Humanities and Social Sciences Communications, 11, Article 559. https://doi.org/10.1057/s41599-024-03101-6
Elstad, E. (2024). AI in education: Rationale, principles, and instructional implications. arXiv. https://doi.org/10.48550/arXiv.2412.12116
Elstad, E. & Eriksen, H. (2024). High school teachers’ Adoption of generative AI: Antecedents of instructional AI utility in the early stages of school-specific chatbot implementation. Nordic Journal of Comparative and International Education, 8(1). https://doi.org/10.7577/njcie.5736
Ertmer, P. A., Paul, A., Molly, L., Eva, R., & Denise, W. (1999). Examining teachers’ beliefs about the role of technology in the elementary classroom. Journal of Research on Computing in Education, 32(1), 54–72. https://doi.org/10.1080/08886504.1999.10782269
Fan, S., Yu, Z., Zheng, X., & Gao, C. (2023). Relationship between psychological adaptability and work engagement of college teachers within smart teaching environments: The mediating role of digital information literacy self-efficacy. Frontiers in Psychology, 14, Article 1057158. https://doi.org/10.3389/fpsyg.2023.1057158
Fullan, M. (2007). Leading in a culture of change. John Wiley & Sons.
Fullan, M., Azorín, C., Harris, A., & Jones, M. (2024). Artificial intelligence and school leadership: challenges, opportunities and implications. School Leadership & Management, 44(4), 339–346. https://doi.org/10.1080/13632434.2023.2246856
Fusarelli, B. C., & Fusarelli, L. D. (2024). Leadership for the future: Enhancing principal preparation through standards and innovation. Education Sciences, 14(12), Article 1403. https://doi.org/10.3390/educsci14121403
Gârdan, I. P., Manu, M. B., Gârdan, D. A., Negoiță, L. D. L., Paștiu, C. A., Ghiță, E., & Zaharia, A. (2025). Adopting AI in education: Optimizing human resource management considering teacher perceptions. Frontiers in Education, 10, Article 1488147. https://doi.org/10.3389/feduc.2025.1488147
Haladyna, T. M., & Rodriguez, M. C. (2013). Developing and validating test items. Routledge.
Henderson, J., & Corry, M. (2021). Teacher anxiety and technology change: A review of the literature. Technology, Pedagogy and Education, 30(4), 573–587. https://doi.org/10.1080/1475939X.2021.1931426
Howard, S. K. (2013). Risk-aversion: Understanding teachers’ resistance to technology integration. Technology, Pedagogy and Education, 22(3), 357–372. https://doi.org/10.1080/1475939X.2013.802995
Huang, F., Wang, Y., & Zhang, H. (2024). Modelling generative AI acceptance, perceived teachers’ enthusiasm, and self-efficacy concerning English as a foreign language learners’ well-being in the digital era. European Journal of Education, 59(4), Article e12770. https://doi.org/10.1111/ejed.12770
Huberman, M. (1993). The lives of teachers. Teachers College Press.
Johnson, R. B., & Christensen, L. B. (2024). Educational research: Quantitative, qualitative, and mixed approaches. Sage.
Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford Publications.
Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X. H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv. https://doi.org/10.48550/arXiv.2506.08872
Municipality of Oslo. (2024, February 14). Facebook 14 February 2024 tag: Osloskolen. https://www.facebook.com/Oslo
Municipality of Oslo. (2025). Poengtabeller for videregående skoler i Oslo. https://www.oslo.kommune.no/skole-og-utdanning/videregaende-skole/soke-videregaende-skole/poengtabeller-for-videregaende-skoler-i-oslo/
Musanti, S. I., & Pence, L. (2010). Collaboration and teacher development: Unpacking resistance, constructing knowledge, and navigating identities. Teacher Education Quarterly, 37(1), 73–89.
Nagelhout, R. (2023). Academic resilience in a world of artificial intelligence: Guidance for educators as generative AI changes the classroom and the world at large. Harvard Graduate School of Education. https://www.gse.harvard.edu/ideas/usable-knowledge/23/08/academic-resilience-world-artificial-intelligence
Nickelsen, T. (2025, August 18). Forskere: – Ikke alle elever lærer noe av å bruke KI. Apollon. https://www.apollon.uio.no/artikler/2025/3_ki_skolen.html
Norwegian Directorate for Education and Training (2023). The Norwegian Education Mirror 2022. https://www.udir.no/in-english/the-education-mirror-2022/
O’Donnell, A. M., & Hmelo-Silver, C. E. (2013). Introduction: What is collaborative learning? An overview. In C. Hmelo-Silver, C. Chinn, C. Chan, & A. O’Donnell (Eds.), The international handbook of collaborative learning (pp. 1–15). Routledge
Oran, B. B. (2023). Correlation between artificial intelligence in education and teacher self-efficacy beliefs: A review. RumeliDE Dil ve Edebiyat Araştırmaları Dergisi, 34, 1354–1365.
Pagano, A., Mørch, A., Santa Barletta, V., & Andersen, R. (2023). AI for humans and humans for AI: Towards cultures of participation in the digital age. IxD&A – Interaction Design & Architecture(s), 59(1), 5–16.
Schein, E. H. (1992). Organizational culture and leadership. Jossey-Bass.
Seashore Louis, K., & Lee, M. (2016). Teachers’ capacity for organizational learning: The effects of school culture and context. School Effectiveness and School Improvement, 27(4), 534–556. https://doi.org/10.1080/09243453.2016.1189437
Skaalvik, E. M., & Skaalvik, S. (2007). Dimensions of teacher self-efficacy and relations with strain factors, perceived collective teacher efficacy, and teacher burnout. Journal of Educational Psychology, 99(3), 611–625. https://doi.org/10.1037/0022-0663.99.3.611
Statistics Norway. (2024). Ansatte i barnehage og skole. https://www.ssb.no/utdanning/barnehager/statistikk/ansatte-i-barnehage-og-skole
Sullivan, M., Kelly, A., & McLaughlan, (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning and Teaching, 6(1), Article 1. https://doi.org/10.37074/jalt.2023.6.1.17
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
Tammets, K., & Ley, T. (2023). Integrating AI tools in teacher professional learning: a conceptual model and illustrative case. Frontiers in Artificial Intelligence, 6, Article 1255089. https://doi.org/10.3389/frai.2023.1255089
The Norwegian Media Authority. (2024). Kritisk medieforståelse i den norske befolkningen. Medietilsynet. https://www.medietilsynet.no/fakta/rapporter/kritisk-medieforstaelse/rapporter-2024/kmf2024/
Tjønn, H., Ludvigsen, S.R. & Mørch, A. (2024, June 13). Bruk av KI i grunnskolen − hvordan utforme undervisningsopplegg. Bedre Skole. https://www.utdanningsnytt.no/bedre-skole-fagartikkel-kunstig-intelligens/bruk-av-ki-i-grunnskolen-hvordan-utforme-undervisningsopplegg/403025
Tjønn, H., Ludvigsen, S., & Mørch, A. I. (2025). Generative AI in Norwegian classrooms: Differences in teaching about, with and through GAI. In J. Oshima, B. Chen, F. Vogel, & Järvelä, J. (Eds.), Proceedings of the 18th International Conference on Computer-Supported Collaborative Learning – CSCL 2025 (pp. 605-607). International Society of the Learning Sciences.
Tondeur, J., Van Braak, J., Ertmer, P. A., & Ottenbreit-Leftwich, A. (2017). Understanding the relationship between teachers’ pedagogical beliefs and technology use in education: A systematic review of qualitative evidence. Educational Technology Research and Development, 65, 555–575. https://doi.org/10.1007/s11423-016-9481-2
Tschannen-Moran, M., Hoy, A. W., & Hoy, W. K. (1998). Teacher efficacy: Its meaning and measure. Review of Educational Research, 68(2), 202–248. https://doi.org/10.3102/00346543068002202
Tweed, S. (2013). Technology implementation: Teacher age, experience, self-efficacy, and professional development as related to classroom technology integration [Doctoral dissertation, East Tennessee State University]. https://dc.etsu.edu/etd/1109
Velander, J., Taiye, M. A., Otero, N., & Milrad, M. (2024). Artificial intelligence in K-12 education: Eliciting and reflecting on Swedish teachers’ understanding of AI and its implications for teaching & learning. Education and Information Technologies, 29(4), 4085–4105. https://doi.org/10.1007/s10639-023-11990-4
Viberg, O., Cukurova, M., Feldman-Maggor, Y., Alexandron, G., Shirai, S., Kanemune, S., Wasson, B., Tømte, C., Spikol, D., Milrad, M., Coelho, R., & Kizilcec, R. F. (2024). What explains teachers’ trust in AI in education across six countries? International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-024-00433-x
Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21(1), 2–29. https://doi.org/10.1186/s41239-024-00448-3
Weidlich, J., Gasevic, D., Drachsler, H., & Kirschner, P. (2025). ChatGPT in education: An effect in search of a cause. Journal of Computer Assisted Learning, 41(5), Article e70105. https://doi.org/10.1111/jcal.70105
Yang, Y. F., Tseng, C. C., & Lai, S. C. (2024). Enhancing teachers’ self-efficacy beliefs in AI-based technology integration into English speaking teaching through a professional development program. Teaching and Teacher Education, 144, Article 104582. https://doi.org/10.1016/j.tate.2024.104582
Yang, Y., Xia, Q., Liu, C., & Chiu, T. K. (2025). The impact of TPACK on teachers’ willingness to integrate generative artificial intelligence (GenAI): The moderating role of negative emotions and the buffering effects of need satisfaction. Teaching and Teacher Education, 154, Article 104877. https://doi.org/10.1016/j.tate.2024.104877
Yin, X., Ye, J., Yu, S., Li, H., Liu, Q., & Zhao, G. (2025). The association between groups’ interactions with the Visual-GenAI learning analytics feedback and student engagement in CSCL. Computers & Education, Article 105434. https://doi.org/10.1016/j.compedu.2025.105434

Details

Primary Language

English

Subjects

Educational Technology and Computing

Journal Section

Research Article

Authors

Eyvind Elstad ^*
0000-0003-4369-0040
Norway

Harald Eriksen
0000-0002-8897-1674
Norway

Publication Date

August 31, 2025

Submission Date

March 27, 2025

Acceptance Date

August 28, 2025

Published in Issue

Year 1970 Volume: 14 Number: 2

IZ

https://izlik.org/JA68KK84HR

Cite

RIS / Bibtex

APA

Elstad, E., & Eriksen, H. (2025). Antecedents of Norwegian High School Teachers’ AI Resistance. Journal of Teacher Education and Educators, 14(2), 181-197. https://izlik.org/JA68KK84HR