FACTORS INFLUENCING THE ACCEPTANCE AND USE OF ARTIFICIAL INTELLIGENCE IN TEACHING BY VOCATIONAL EDUCATION TEACHERS | Vinh | TNU Journal of Science and Technology

FACTORS INFLUENCING THE ACCEPTANCE AND USE OF ARTIFICIAL INTELLIGENCE IN TEACHING BY VOCATIONAL EDUCATION TEACHERS

About this article

Received: 21/10/24                Revised: 17/12/24                Published: 17/12/24

Authors

Nguyen Xuan Vinh Email to author, Danang Architecture University

Abstract


The study aims to explore the factors influencing the acceptance and use of artificial intelligence in teaching by vocational teachers through the Technology Acceptance Model. The research employs Structural Equation Modeling SEM to examine the relationships between factors such as self-efficacy, organizational support, teaching performance expectancy, and social influence on artificial intelligence acceptance behavior. Data collected from 365 teachers were analyzed using SPSS and AMOS software. Exploratory Factor Analysis and Confirmatory Factor Analysis were conducted to assess the reliability and validity of the measurement scales. The results indicate that teaching performance expectancy and social influence have a positive and statistically significant impact on teachers' use of artificial intelligence. In contrast, self-efficacy and organizational support do not have a significant impact. The study suggests that building trust in the effectiveness of artificial intelligence and fostering positive social influence are key factors in encouraging teachers to use artificial intelligence. Additionally, a clear legal framework and active organizational support are needed to facilitate the implementation of artificial intelligence in vocational training.

Keywords


Artificial Intelligence (AI); Teacher; Vocational education; Teaching; Behavior

References


[1] M. I. Rosyadi, I. Kustiawan, E. O. Tetehfio, and Q. Joshua, "The Role of AI In Vocational Education: A Systematic Literature Review," Journal of Vocational Education Studies, vol. 6, no. 2, pp. 244-263, 2023, doi: 10.12928/joves.v6i2.9032.

[2] M. Becker, G. Spöttl, and L. Windelband, "The role of artificial intelligence in skilled work and consequences for vocational training," TVET@ Asia, no. 19, p. 1, 2022, doi: 10.5445/IR/1000149185.

[3] R. Ejjami, "AI's Impact on Vocational Training and Employability: Innovation, Challenges, and Perspectives," International Journal for Multidisciplinary Research (IJFMR), vol. 6, no. 4, pp. 1-16, 2024, doi: 10.36948/ijfmr.2024.v06i04.24967.

[4] K. J. Rott, L. Lao, E. Petridou, and B. Schmidt-Hertha, "Needs and requirements for an additional AI qualification during dual vocational training: Results from studies of apprentices and teachers," Computers and Education: Artificial Intelligence, vol. 3, 2022, Art. no. 100102, doi: 10.1016/j.caeai.2022.100102.

[5] L. Windelband, "Artificial intelligence and assistance systems for technical vocational education and training–Opportunities and risks," in New Digital Work: Digital Sovereignty at the Workplace: Springer, 2023, pp. 195-213, doi: 10.1007/978-3-031-26490-0.

[6] J. Ma, "The challenge and development of vocational education under the background of artificial intelligence," in 2019 5th International Conference on Humanities and Social Science Research (ICHSSR 2019), Atlantis Press, 2019, pp. 522-525, doi: 10.2991/ichssr-19.2019.102.

[7] F. Hui, "The impact of artificial intelligence on vocational education and countermeasures," in Journal of Physics: Conference Series, vol. 1693, no. 1, 2020, Art. no. 012124, doi: 10.1088/1742- 6596/1693/1/012124.

[8] W. Zeng, S. Kang, and B. Li, "Application of internet+ big data and artificial intelligence in vocational education," in 2019 4th international conference on information systems engineering (ICISE), IEEE, 2019, pp. 21-25, doi: 10.1109/ICISE.2019.00012.

[9] A. Suparyati, I. Widiastuti, I. N. Saputro, and N. A. Pambudi, "The Role of Artificial Intelligence (AI) in Vocational Education," JIPTEK: The Journal for Technology and Vocational Education, vol. 17, no. 1, 2023, doi: 10.20961/jiptek.v17i1.75995.

[10] K. Shiohira, Understanding the Impact of Artificial Intelligence on Skills Development. Education 2030, UNESCO, 2021.

[11] Y. Liu, "Examining the Impact of Assistive Technology on the Talent Development Path in AI-Driven Vocational Education," Journal of Autism and Developmental Disorders, vol. 54, no. 4, pp. 1621-1621, 2024, doi: 10.1007/s10803-023-06072-w.

[12] F. D. Davis, "Perceived usefulness, perceived ease of use, and user acceptance of information technology," MIS Quarterly, vol.13, pp. 319-340, 1989, doi: 10.2307/249008.

[13] V. Venkatesh and F. D. Davis, "A theoretical extension of the technology acceptance model: Four longitudinal field studies," Management Science, vol. 46, no. 2, pp. 186-204, 2000, doi: 10.1287/mnsc.46.2.186.11926

[14] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, "User acceptance of information technology: Toward a unified view," MIS Quarterly, vol. 27, no. 3, pp. 425-478, 2003, doi: 10.2307/30036540

[15] D. Ifenthaler and V. Schweinbenz, "The acceptance of Tablet-PCs in classroom instruction: The teachers’ perspectives," Computers in human behavior, vol. 29, no. 3, pp. 525-534, 2013, doi: 10.1016/j.chb.2012.11.004.

[16] K. M. Alraimi, H. Zo, and A. P. Ciganek, "Understanding the MOOCs continuance: The role of openness and reputation," Computers & Education, vol. 80, pp. 28-38, 2015, doi: 10.1016/j.compedu.2014.08.006.

[17] R. Scherer, F. Siddiq, and J. Tondeur, "All the same or different? Revisiting measures of teachers' technology acceptance," Computers & Education, vol. 143, 2020, Art. no. 103656, doi: 10.1016/j.compedu.2019.103656.

[18] M. Fishbein and I. Ajzen, Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley, 1975.

[19] Y. K. Dwivedi, N. P. Rana, A. Jeyaraj, M. Clement, and M. D. Williams, "Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model," Information Systems Frontiers, vol. 21, pp. 719-734, 2019, doi: 10.1007/s10796-017-9774-y.

[20] J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis. 7th Edition, Pearson, New York, 2010.

[21] J. C. Nunnally, "An overview of psychological measurement," in Clinical Diagnosis of Mental Disorders: A Handbook, B. B. Wolman, Ed., New York, NY: Springer, 1978, pp. 97-146. https://doi.org/10.1007/978-1-4684-2490-4_4

[22] C. Fornell and D. F. Larcker, "Evaluating structural equation models with unobservable variables and measurement error," Journal of Marketing Research, vol. 18, no. 1, pp. 39-50, 1981, doi: 10.1177/002224378101800104.

[23] J. F. Hair, G. T. M. Hult, C. M. Ringle, et al., "Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods," J. of the Acad. Mark. Sci., vol. 45, pp. 616-632, 2017, doi: 10.1007/s11747-017-0517-x.

[24] L. T. Hu and P. M. Bentler, "Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives," Structural Equation Modeling: A Multidisciplinary Journal, vol. 6, no. 1, pp. 1-55, 1999, doi: 10.1080/10705519909540118.




DOI: https://doi.org/10.34238/tnu-jst.11370

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