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Cited by 148Artificial Intelligence in EducationTechnology Adoption and User BehaviourEducational Technology and Pedagogy

The DOI matches its Crossref record; 41 of the 42 references it cites were found in a registry or an index.

The factors influencing teacher education students’ willingness to adopt artificial intelligence technology for information-based teaching

Ma, Shuaiyao, Lei, Lei·2024·Asia Pacific Journal of Education
DOI 10.1080/02188791.2024.2305155
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Abstract

This study, rooted in the Technology Acceptance Model (TAM), investigates the multifaceted factors that influence teacher education students in Information-Based Teaching to embrace artificial intelligence technologies. To enrich the TAM framework, we have incorporated elements such as Artificial Intelligence Literacy (AIL), Subjective Norms (SN), and Output Quality (OQ), with the aim of examining their respective effects on the willingness of teacher education students to adopt AI technologies. To substantiate this theoretical framework, we conducted empirical research involving teacher education students from various Chinese universities. Our findings affirm the robustness of the TAM in explaining the inclination of teacher education students, engaged in the actual teaching process within a digitized educational environment, to adopt AI technologies. Through this model, our study underscores the pivotal role of Artificial Intelligence Literacy (AIL) in influencing educators’ acceptance of AI technologies, establishing a foundational cornerstone for subsequent explorations within the theoretical landscape of the TAM. In this study, we identify Perceived Usefulness (PU) and Artificial Intelligence Literacy (AIL) as the primary factors affecting Behavioral Intention (BI) to use AI technologies. Consequently, to foster broader adoption of AI technologies by educators, it is essential to emphasize their tangible benefits and superiority in teaching, with the goal of promoting the extended utilization of AI in digitalized instruction.

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Research works printed but not linked: 4

  1. Carmines, E. G., & McIver, J. P. (1983). An Introduction to the Analysis of Models with Unobserved Variables. Political Methodology, 9(1), 51–102. http://www.jstor.org/stable/25791175
  2. Hair, J.F., Anderson, R.E., Tatham, R.L., & Black, W.C. (1998). Multivariate data analysis. Englewood cliff. New Jersey, USA, 5(3), 207–2019.
  3. Nunnally, J., & Bernstein, I. (1994). Psychometric theory (3rd ed.). MacGraw-Hill.
  4. Xu, Y., Peng, X., Cao, Y., & Yang, X. (2020). The connotation, function and development of teachers’ data intelligence competence from the perspective of human-machine cooperation. Journal of Distance Education, 38(6), 13–21.

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Metadata

Authors
Ma, S., & Lei, L.
Affiliations
Chongqing Normal University (CN)
Corresponding author
Lei, Lei
ORCID
Ma, Shuaiyao
Venue
Asia Pacific Journal of Education
Publisher
Informa UK Limited
Published
2024-01-02
Volume
44
Issue
1
Pages
94-111
ISSN
  • 0129-4776 (ISSN-L)
  • 0218-8791
  • 1742-6855
DOI
10.1080/02188791.2024.2305155
OpenAlex
W4390952274
Cited by
148
References
42
Citations by year
2026: 67 · 2025: 63 · 2024: 18
FWCI
55.40
Type
journal article
Language
en
Open access
closed
Locations
Asia Pacific Journal of Education (publishedVersion)
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Social Sciences › Education › Educational Technology and Pedagogy
Keywords
artificial intelligence adoption, pre-service teachers, information-based teaching, technology acceptance model, AI literacy, perceived usefulness, behavioral intention, subjective norms, output quality, artificial intelligence in education

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