ASSESSING THE READINESS OF PRE-SERVICE MATHEMATICS TEACHERS FOR GENERATIVE AI INTEGRATION: A SCALE DEVELOPMENT STUDY

Authors

  • Tang Minh Dung Department of Mathematics and Informatics, Ho Chi Minh City University of Education, Ho Chi Minh city, Vietnam
  • Nguyen Thi Nga Department of Mathematics and Informatics, Ho Chi Minh City University of Education, Ho Chi Minh city, Vietnam
  • Le Thai Bao Thien Trung Department of Mathematics and Informatics, Ho Chi Minh City University of Education, Ho Chi Minh city, Vietnam
  • Bui Hoang Dieu Ban Department of Mathematics and Informatics, Ho Chi Minh City University of Education, Ho Chi Minh city, Vietnam
  • Phu Luong Chi Quoc Department of Mathematics and Informatics, Ho Chi Minh City University of Education, Ho Chi Minh city, Vietnam
  • Ta Thanh Trung Department of Physics, Ho Chi Minh City University of Education, Ho Chi Minh city, Vietnam

DOI:

https://doi.org/10.18173/2354-1075.2025-0118

Keywords:

thang đo, hành vi, sinh viên sư phạm, sự sẵn sàng, AI tạo sinh

Abstract

This study develops and validates a measurement scale assessing factors influencing the readiness of pre-service mathematics teachers to adopt generative AI in education. The proposed scale comprises five independent factors (Relevance of AI, Confidence in AI, Policy, Classmates' Support, and Ethics), one mediating factor (Subjective Norms), and two dependent factors (Readiness for Teaching and Learning). We used confirmatory factor analysis (CFA) to analyse survey data from 337 pre-service teachers to ensure reliability and construct validity. The findings indicate that this scale is a reliable tool for assessing readiness for generative AI adoption. The study provides valuable insights for policymakers and teacher training institutions as they design training programs and policies to facilitate AI integration in teaching and learning.

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References

[1] UNESCO, (2022). K-12 AI curricula: A mapping of government-endorsed AI curricula. Paris, France. Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000380602.

[2] Wang H, Dang A, Wu Z & Mac S, (2024). Generative AI in higher education: Seeing ChatGPT through universities' policies, resources, and guidelines. Computers and Education: Artificial Intelligence, 7, Article 100326. doi:10.1016/j.caeai.2024.100326.

[3] Ma X & Huo Y, (2023). Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework. Technology in Society, 75, Article 102362. doi:10.1016/j.techsoc.2023.102362.

[4] Duong CD, Vu TN & Ngo TV, (2023). Applying a modified technology acceptance model to explain higher education students’ usage of ChatGPT: A serial multiple mediation model with knowledge sharing as a moderator. The International Journal of Management Education, 21(3), Article 100883. doi:10.1016/j.ijme.2023.100883.

[5] Lai CY, Cheung KY & Chan CS, (2023). Exploring the role of intrinsic motivation in ChatGPT adoption to support active learning: An extension of the technology acceptance model. Computers and Education: Artificial Intelligence, 5, Article 100178. doi:10.1016/j.caeai.2023.100178.

[6] Rahim NIM, Iahad NA, Yusof AF & Al-Sharafi MA, (2022). AI-based chatbots adoption model for higher-education institutions: A hybrid PLS-SEM-neural network modelling approach. Sustainability, 14(19), Article 12726. doi:10.3390/su141912726.

[7] Lozano A & Fontao CB, (2023). Is the education system prepared for the irruption of artificial intelligence? A study on the perceptions of students of primary education degree from a dual perspective: Current pupils and future teachers. Education Sciences, 13(7), Article 733. doi:10.3390/educsci13070733.

[8] Singh RG & Ngai CSB, (2024). Top-ranked US and UK’s universities’ first responses to GenAI: Key themes, emotions, and pedagogical implications for teaching and learning. Discover Education, 3(1), Article 115. doi:10.1007/s44217-024-00211-w.

[9] Ayanwale MA, Sanusi IT, Adelana OP, Aruleba KD & Oyelere SS, (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3, Article 100099. doi:10.1016/j.caeai.2022.100099.

[10] Jöhnk J, Weißert M & Wyrtki K, (2021). Ready or not, AI comes - An interview study of organizational AI readiness factors. Business & Information Systems Engineering, 63(1), 5-20. doi:10.1007/s12599-020-00676-7.

[11] Wang X, (2023). Preparing for AI-enhanced education: Conceptualizing and empirically examining teachers’ AI readiness. Computers in Human Behavior, 146, Article 107798. doi:10.1016/j.chb.2023.107798.

[12] Cuhadar C, (2018). Investigation of pre-service teachers' levels of readiness to technology integration in education. Contemporary Educational Technology, 9(1), 61-75. doi:10.30935/cedtech/6211.

[13] Tiba C & Condy JL, (2021). Identifying factors influencing pre-service teacher readiness to use technology during professional practice. International Journal of Information and Communication Technology Education, 17(2), 149-161. doi:10.4018/IJICTE.20210401.oa3.

[14] Jatileni CN, Sanusi IT, Olaleye SA, Ayanwale MA, Agbo FJ & Oyelere PB, (2024). Artificial intelligence in compulsory level of education: Perspectives from Namibian in-service teachers. Education and Information Technologies, 29(10), 12569-12596. doi:10.1007/s10639-023-12341-z.

[15] Fundi M, Sanusi IT, Oyelere SS & Ayere M, (2024). Advancing AI education: Assessing Kenyan in-service teachers' preparedness for integrating artificial intelligence in competence-based curriculum. Computers in Human Behavior Reports, 14, Article 100412. doi:10.1016/j.chbr.2024.100412.

[16] Nguyen HD, Nguyen HN & Ta TT, (2024). Factors affecting the implementation of STEAM education among primary school teachers in various countries and Vietnamese educators: Comparative analysis. Education 3-13, 1-15. doi:10.1080/03004279.2024.2318239.

[17] Taddeo M & Floridi L, (2018). How AI can be a force for good. Science, 361(6404), 751-752. doi:10.1126/science.aat5991.

[18] Nguyen HN & Ta TT, (2024). Developing scale to assess teachers' behaviour for implementing the 2018 general education program: A study based on the theory of planned behavior. HNUE Journal of Science, 69(2), 3-14. doi:10.18173/2354-1075.2024-0018.

[19] Miller G, (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63, 81-97.

[20] Montano DE & Kasprzyk D, (2008). Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. In: Health behavior: Theory, research and practice. CA, Jossey-Bass, pp. 67-92.

[21] Ta TT & Nguyen TN, (2022). A comparison of using CB-SEM and PLS-SEM to assess training effectiveness evaluation model for teacher’s online continuing professional development. Ho Chi Minh City University of Education Journal of Science, 19(2), 213-228. doi:10.54607/hcmue.js.19.2.3306(2022).

[22] Nunnally JC & Bernstein IH, (1994). The assessment of reliability. In: Psychometric theory (Vol. 3). New York, NY, US, McGraw-Hill, pp. 248-292.

[23] Fornell C & Larcker DF, (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. doi:10.1177/002224378101800104.

[24] Henseler J, Hubona G & Ray PA, (2016). Using PLS path modelling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2-20. doi:10.1108/IMDS-09-2015-0382.

[25] Hair JF, Black WC, Babin BJ & Anderson RE, (2019). Multivariate data analysis (8th ed.). Gautam Buddha, Uttar Pradesh, India: CENGAGE Learning Publisher.

[26] Fornell C & Larcker DF, (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18, 382–388. doi:10.2307/3150980(1981).

[27] Hooper D, Coughlan J & Mullen MR, (2008). Structural equation modeling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53-60.

[28] Hu L & Bentler PM, (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424-453. doi:10.1037/1082-989X.3.4.424.

Published

2025-11-28

Issue

Section

Educational Sciences: Natural Science

How to Cite

Minh Dung, T. (2025) “ASSESSING THE READINESS OF PRE-SERVICE MATHEMATICS TEACHERS FOR GENERATIVE AI INTEGRATION: A SCALE DEVELOPMENT STUDY”, Journal of Science Educational Science, 70(6), pp. 152–163. doi:10.18173/2354-1075.2025-0118.

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