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

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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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The theory of planned behavior
Ajzen, I. (1991) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(91)90020-t · Cited by 90,587
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
Structural equation modeling in practice: A review and recommended two-step approach
Anderson, J. C., & Gerbing, D. W. (1988) · Psychological Bulletin · doi:10.1037/0033-2909.103.3.411 · Cited by 41,941
Structural equation models with unobservable variables and measurement error: Algebra and statistics
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800313 · Cited by 13,287
Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021) · Classroom Companion: Business · doi:10.1007/978-3-030-80519-7 · Cited by 10,343
Teachers' beliefs and educational research: cleaning up a messy construct
Pajares, M. F. (1992) · Review of Educational Research · doi:10.3102/00346543062003307 · Cited by 9,007
Trust and TAM in Online Shopping: An Integrated Model1
Gefen, D., Karahanna, E., & Straub, D. W. (2003) · MIS Quarterly · doi:10.2307/30036519 · Cited by 8,635
Technology Acceptance Model 3 and a Research Agenda on Interventions
Venkatesh, V., & Bala, H. (2008) · Decision Sciences · doi:10.1111/j.1540-5915.2008.00192.x · Cited by 8,159 · cited 1×
“These TAM-measuring items were adopted from Venkatesh and Bala (2008), which were based on Davis (1989).”
Method
Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review
Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006) · The Journal of Educational Research · doi:10.3200/joer.99.6.323-338 · Cited by 6,937 · cited 1×
“Confirmatory factor analysis was used to evaluate the measurement model and to assess convergent validity, reliability, and discriminant validity (Hair et al., 2010; Schreiber et al., 2006).”
Method
Teacher beliefs and technology integration practices: A critical relationship
Ertmer, P. A., Ottenbreit-Leftwich, A. T., Sadik, O., Sendurur, E., & Sendurur, P. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.02.001 · Cited by 2,107

Show all 64 references

Research works printed but not linked: 8

10.14507/epaa.v8n51.2000 · DOI
10.1080/10447318.2021.1970430 · DOI
10.1007/s10857-021-09504-5 · DOI
Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate data analysis: A global perspective (Vol. 7). Pearson.
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.
10.1145/3362789.3362918 · DOI
10.1016/j.compedu.2018.09.009 · DOI · Paper page
10.1145/3313831.3376416 · DOI

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Authors: Choi, S., Jang, Y., & Kim, H.
Affiliations: Korea University (KR)
Corresponding author: Kim, Hyeoncheol
ORCID: Choi, Seongyune
Jang, Yeonju
Kim, Hyeoncheol
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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Influence of Pedagogical Beliefs and Perceived Trust on Teachers’ Acceptance of Educational Artificial Intelligence Tools

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