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Cited by 295Artificial Intelligence in EducationTechnology Adoption and User BehaviourEducation and Technology Integration

The DOI matches its Crossref record; 63 of the 64 references it cites were found in a registry or an index.

Influence of Pedagogical Beliefs and Perceived Trust on Teachers’ Acceptance of Educational Artificial Intelligence Tools

Choi, Seongyune, Jang, Yeonju, Kim, Hyeoncheol·2023·International Journal of Human–Computer Interaction
DOI 10.1080/10447318.2022.2049145
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Abstract

Advancements in artificial intelligence (AI) have stimulated the development of educational AI tools (EAIT). EAITs intelligently assist teachers in formulating better pedagogical decisions or actions for their students. However, teachers are hardly integrating EAITs, and little is known about their perceptions of EAITs. This study seeks to identify human factors that encourage or restrict teachers’ acceptance of EAITs. We propose a revised technology acceptance model incorporating teachers’ pedagogical beliefs and perceived trust in EAITs. Survey data were collected from 215 teachers in South Korea and analyzed using structural equation modeling. The results indicate that teachers with constructivist beliefs are more likely to integrate EAITs than teachers with transmissive orientations. Furthermore, perceived usefulness, perceived ease of use, and perceived trust in EAITs are determinants to be considered when explaining teachers’ acceptance of EAITs. Among them, the most influential determinant of predicting their acceptance was found to be how easily the EAIT is constructed. Significant implications for researchers and stakeholders regarding the development and integration of EAITs are discussed.

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Research works printed but not linked: 8

  1. 10.14507/epaa.v8n51.2000 · DOI
  2. 10.1080/10447318.2021.1970430 · DOI
  3. 10.1007/s10857-021-09504-5 · DOI
  4. Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate data analysis: A global perspective (Vol. 7). Pearson.
  5. Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L., & Sohl-Dickstein, J. (2015). Deep knowledge tracing. Advances in Neural Information Processing Systems, 2015-Janua, 505–513.
  6. 10.1145/3362789.3362918 · DOI
  7. 10.1016/j.compedu.2018.09.009 · DOI · Paper page
  8. 10.1145/3313831.3376416 · DOI

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Metadata

Authors
Choi, S., Jang, Y., & Kim, H.
Affiliations
Korea University (KR)
Corresponding author
Kim, Hyeoncheol
ORCID
Venue
International Journal of Human–Computer Interaction
Publisher
Informa UK Limited
Published
2022-04-19
Volume
39
Issue
4
Pages
910-922
ISSN
  • 1044-7318 (ISSN-L)
  • 1532-7590
DOI
10.1080/10447318.2022.2049145
OpenAlex
W4224229986
Cited by
294
References
64
Citations by year
2026: 75 · 2025: 104 · 2024: 75 · 2023: 30 · 2022: 10
FWCI
54.19
Type
journal article
Language
en
Open access
closed
Locations
International Journal of Human-Computer Interaction (publishedVersion)
Funders
National Research Foundation
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Social Sciences › Education › Education and Technology Integration
Keywords
teacher acceptance, pedagogical beliefs, perceived trust, constructivist beliefs, technology acceptance model, perceived usefulness, perceived ease of use, teacher perceptions, AI integration in education

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