CoChat
Sign Up
Cited by 37Technology Adoption and User BehaviourArtificial Intelligence in EducationEducation and Technology Integration

A study of the factors influencing teachers' willingness to use generative artificial intelligence based on the UTAUT model

Open access
Zhang, Xinhua, Wareewanich, Thitinant·2024·International Journal of Interactive Mobile Technologies (iJIM)
DOI 10.3991/ijim.v18i06.47991
Open this paper in CoChat — free accountOpen PDF

Abstract

The advancement of wireless communication and mobile computing technologies has paved the way for the extensive application of Artificial Intelligence across diverse sectors, including electronics, automotive, medical, industrial, and educational fields. Employing the Unified Theory of Acceptance and Use of Technology, this study investigates the key determinants influencing preschool teachers’ willingness to utilize Generative Artificial Intelligence (GAI) in Jiangsu Province, China. The research, involving 154 participants, analyzes their inclination towards GAI adoption through four dimensions: performance expectancy, effort expectancy, social influence, and facilitating conditions. Findings reveal that performance expectancy, social influence, and facilitating conditions significantly enhance their willingness to adopt GAI. Additionally, teaching experience and IT proficiency were found to moderate the effects of certain variables on this willingness. This aligns with the broader goal of integrating digital technology into education, which is a vital element of the nexus between digital technology development and innovation.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3991/ijim.v18i06.47991

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Research works printed but not linked: 43

  1. C. Su, J. J. Huang, H. W. Zhu, Y. C. Jin, K. Y. Sun, G. L. Qi, and S. Q. Zhang, "Design of speech emotion recognition system based on mobile computing platform," Journal of Taizhou University, vol. 41, no. 3, pp. 11-15, 2019. https://doi.org/10.13853/j.cnki. issn.1672-3708.2019.03.003 · DOI
  2. P. S. Fang, "Research on semantic perception technology for trajectory data mining based on mobile computing platforms," Science and Technology Information, vol. 17, no. 24, pp. 23-24, 2019. https://doi.org/10.16661/j.cnki.1672-3791.2019.24.023 · DOI
  3. X. Zhang, X. S. Yang, and C. S. Xu, "ChatGPT and generative artificial intelligence: Current status and future development directions," Chinese Science Foundation, vol. 37, no. 5, pp. 743-750, 2023. https://doi.org/10.16262/j.cnki.1000-8217.20231026.002 · DOI
  4. Y. H. Wu and T. T. Li, "The development status, practice, reflection and prospect of robot education in the perspective of machine intelligence," Journal of Distance Education, vol. 36, no. 4, pp. 79-87, 2018. https://doi.org/10.15881/j.cnki.cn33-1304/g4.2018.04.010 · DOI
  5. X. R. Song, "Research on the value of robot education -based on the educational prac- tice of Lanzhou foreign language high school," Invention and Innovation (Vocational Education), no. 7, pp. 41-42, 2021.
  6. F. Alnajjar, C. Bartneck, P. Baxter, T. Belpaeme, and others, (2021). "Robots in education: An introduction to high-tech social agents, intelligent tutors, and curricular tools (1st ed.)," Routledge Press, 2021. https://doi.org/10.4324/9781003142706 · DOI
  7. X. J. Liu, "Overview of the current situation and development trend of domestic robot education research," Audio Visual Education in Primary and Secondary Schools, no. 9, pp. 8-10, 2021.
  8. Z. He, R. X. Zeng, W. Qin, L. Zheng, H. Zhang, X. Y. Zhang, and N. Zhang, "The social impact and governance of new generation artificial intelligence technologies such as ChatGPT," E-Government, no. 4, pp. 2-24, 2023. https://doi.org/10.16582/j.cnki.dzzw.2023.04.001 · DOI
  9. B. Q. Liu, X. L. Nie, S. J. Wang, T. T. Yuan, H. J. Zhu, Z. Q. Zhao, and G. M. Zhu, "Generative artificial intelligence and the reshaping of future educational forms: Technical frame- work, capability characteristics, and application trends," Research on Electronic Education, vol. 45, no. 1, pp. 13-20, 2024. https://doi.org/10.13811/j.cnki.eer.2024.01.002 · DOI
  10. G. L. Yang, "Overview of domestic robot education research," China Education Informatization, no. 7, pp. 36-41, 2019.
  11. J. A. Smith and S. A. Sivo, "Predicting continued use of online teacher professional development and the influence of social presence and sociability," British Journal of Educational Technology, vol. 43, no. 6, pp. 871-882, 2012. https://doi.org/10.1111/ j.1467-8535.2011.01223.x · DOI
  12. Y. B. Wang, K. Wan, and Y. Q. Ren, "Research on factors affecting preschool teachers' accep- tance of robot education," Research on Audio Visual Education, no. 6, pp. 105-111, 2019.
  13. X. T. Li, "Reflections on the application of Lego robots in children's education guide to knowledge," The Journal of Seeking Knowledge, no. 2, pp. 113, 2016.
  14. N. Zhang, M. M. Xu, and C. P. Lu, "Research on the application of educational robots in children's intelligence development," Silicon Valley, no. 2, pp. 99-100, 2012.
  15. W. Wei, "The implementation path and enlightenment of robot education in American primary and secondary schools," Educational Observation, no. 31, pp. 1-3 + 33, 2020.
  16. S. D. Peng, "On robot education," Research on Audio Visual Education, no. 6, pp. 3-7, 2002.
  17. J. P. Zhang and Y. Wang, "Robot education: Current situation, problems and promotion strategies," China Audio Visual Education, no. 12, pp. 65-68, 2006.
  18. Y. Wang and J. P. Zhang, "The characteristics of American robot education and its enlightenment," Modern Educational Technology, no. 11, pp. 108-112, 2007.
  19. B. C. Zhong and L. Zhang, "Investigation and analysis of the current situation of robot education in primary and secondary schools in China," China's Audio-Visual Education, no. 7, pp. 101-107, 2015.
  20. X. M. Zhao and H. C. Liu, "Research on course design of robot education in primary and secondary schools," China Education Technology Equipment, no. 3, pp. 86-87+92, 2019.
  21. L. B. Chen and Q. L. Lin, "Research on the curriculum system of teenager robot educa- tion," Wireless Internet Technology, no. 16, pp. 133-134, 2020.
  22. J. Q. Nie, Y. Ma, and W. X. Fan, "Research on robot education mode based on STEAM concept," China Education Informatization, no. 16, pp. 22-26, 2021.
  23. L. Zhang, L. Zhou, and L. L. Zhao, "Risks and avoidance of generative artificial intelli- gence education application: Based on the perspective of educational subjectivity," Open Education Research, no. 5, pp. 47-53, 2023.
  24. G. S. Lan, S. L. Du, F. Song, and others, "Generative artificial intelligence education: Key controversies, promoting methods, and future issues-key points and reflections on UNESCO's guidelines for generative artificial intelligence education and research appli- cations," Open Education Research, no. 6, pp. 15-26, 2023.
  25. J. H. Zeng, "Artificial intelligence and paradigm transformation of Chinese higher education-centered on ChatGPT," Journal of Yangzhou University of Education, no. 4, pp. 1-6, 2023. https://doi.org/10.15977/j.cnki.cn32-155/g4.2023.04.019 · DOI
  26. Y. B. Wang, K. Wan, and Y. Q. Ren, "Research on factors influencing the acceptance of robot education by primary and secondary school teachers," Research on Electronic Education, vol. 40, no. 6, pp. 105-111, 2019. https://doi.org/10.13811/j.cnki.eer.2019.06.014 · DOI
  27. V. Venkatesh and F. D. Davis, "Theoretical extension of technology acceptance model: Four longitudinal field studies," Management Science, vol. 46, no. 2, pp. 186-204, 2000. https://doi.org/10.1287/mnsc.46.2.186.11926 · DOI · Paper page
  28. 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. https://doi.org/10.2307/30036540 · DOI · Paper page
  29. V. Venkatesh and X. Zhang, "Unified theory of acceptance and use of technology: U.S. vs. China," Journal of Global Information Technology Management, no. 1, pp. 5-24, 2010. https://doi.org/10.1080/1097198X.2010.10856507 · DOI
  30. K. T. Wong, T. Teo, and S. Russo, "Interactive whiteboard acceptance: Applicability of the UTAUT model to student teachers," Asia-Pacific Educational Research, no. 22, pp. 1-10, 2013. https://doi.org/10.1007/s40299-012-0001-9 · DOI
  31. B. Pynoo, P. Devolder, J. Tondeur, and others, "Predicting secondary school teachers' acceptance and use of a digital learning environment: A cross-sectional study," Computers in Human Behavior, vol. 27, no. 1, pp. 568-575, 2011. https://doi.org/10.1016/ j.chb.2010.10.005 · DOI
  32. K. T. Wong, S. Russo, and J. Mcdowall, "Understanding early childhood student teach- ers' acceptance and use of interactive whiteboard," Campus-Wide Information Systems, vol. 30, no. 1, pp. 4-16, 2013. https://doi.org/10.1108/10650741311288788 · DOI
  33. H. Y. Zhang, D. M. Qian, and Y. Q. Ren, "Promoting the application of e-bag teaching: An empirical atudy of teacher acceptance," Research on Audio Visual Education, no. 10, pp. 92-97, 2014.
  34. J. H. Bu, "Research on the current situation and influencing factors of the application of interactive electronic whiteboard by primary and middle school teachers," Master dissertation, Tianjin Normal University, Tianjin, 2022. https://kns.cnki.net/KCMS/detail/ detail.aspx?dbname=CMFDTEMP&filename=1022554986.nh
  35. H. Y. Wang, H. E. Sun, W. N. Lei, and others, "Research on the acceptance behavior of online teaching by college teachers in the post epidemic era: A regulated medi- ation model," Journal of Teacher Education, vol. 8, no. 1, pp. 92-100, 2021. https://doi. org/10.13718/j.cnki.jsjy.2021-01.012 · DOI
  36. B. Kumar, S. S. Chand, S. M. Goundar, and A. Narayan, "Extended UTAUT model for mobile learning adoption studies," International Journal of Mobile and Blended Learning, vol. 14, no. 1, 2022. https://doi.org/10.4018/IJMBL.312570 · DOI
  37. S. Zhang, Q. T. Liu, J. X. Huang, and P. Wu, "A study on the factors affecting preschool Tteachers' use of e-learning space -a survey based on UTAUT model," China Audio Visual Education, no. 3, pp. 99-106, 2016.
  38. A. Birch and V. Irvine, "Preservice teachers' acceptance of ICT integration in the class- room: Applying the UTAUT model," Educational Media International, vol. 46, no. 4, pp. 295-315, 2009. https://doi.org/10.1080/09523980903387506 · DOI
  39. H. X. Li and S. Q. Li, "Empirical study on the intention of continuous use of mobile social networks," Journal of Xi'an Petroleum University, no. 5, pp. 22-30, 2017.
  40. H. X. Li, C. L. Zhao, Z. H. Jiang, and Y. Z. Liang, "Influencing factors of preschool teachers' acceptance of e-learning-empirical analysis based on UTAUT model," Research on Preschool Education, no. 4, pp. 14-25, 2017.
  41. P. Schaik, "Unified theory of acceptance and use for websites used-by students in higher education," Journal of Educational Computing Research, no. 2, pp. 229-257, 2009. https:// doi.org/10.2190/EC.40.2.e · DOI
  42. V. Gialamas and K. Nikolopoulou, "In-service and pre-service early childhood teachers' views and intentions about ICT use in early childhood settings: A comparative study," Computers and Education, vol. 55, no. 1, pp. 333-341, 2010. https://doi.org/10.1016/ j.compedu.2010.01.019 · DOI
  43. L. Bai, "Reflection and reconstruction: AI China plan in the process of Chinese path to modernization -summary of the special forum enabling and innovation: AI and Chinese path to modernization," Yuejiang Academic Journal, no. 6, pp. 78-86 + 170-171, 2023. https://doi.org/10.13878/j.cnki.yjxk.2023110.2003 · DOI

Cited by

Sorted by citations

Authors
Zhang, X., & Wareewanich, T.
Affiliations
Rajamangala University of Technology Tawan-ok (TH)
ORCID
Venue
International Journal of Interactive Mobile Technologies (iJIM)
Publisher
International Association of Online Engineering (IAOE)
Published
2024-03-19
Volume
18
Issue
06
Pages
126-142
ISSN
1865-7923 (ISSN-L)
DOI
10.3991/ijim.v18i06.47991
OpenAlex
W4392957432
Cited by
37
References
0
Citations by year
2026: 13 · 2025: 18 · 2024: 6
FWCI
13.81
Type
journal article
Language
en
Open access
Licence
Locations
International Journal of Interactive Mobile Technologies (iJIM) (publishedVersion, cc-by)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Education › Education and Technology Integration
Keywords
generative AI, teacher technology adoption, willingness to use, UTAUT, preschool teachers, performance expectancy, effort expectancy, social influence, facilitating conditions, teaching experience, moderating effect, digital technology in education

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review