Zhang C, Schießl J, Hofmann F, Plößl L, Gläser-Zikuda M (2022)
Publication Language: English
Publication Type: Conference contribution, Abstract of lecture
Publication year: 2022
Pages Range: 42 - 43
Conference Proceedings Title: Digital Transformation in Teaching and Teacher Education, EARLI SIG 11 conference 2022
Artificial intelligence (AI) applications are increasingly appearing in higher education, such as learning
management systems, grading/assessment, and student information system (EDUCAUSE, 2021).
Acceptance of AI has been investigated in marketing, but although AI is applied in education, there is
research needed (Chikobava & Romeike, 2021) how it is accepted and which individual factors
determine the use of AI. Therefore, our study (funded by the BMBF – Federal Ministry of Education and
Research) aimed at the analysis of pre-service teachers’ acceptance of AI in testing their behavioral
intentions regarding prospective AI technology use. Furthermore, gender effects, and differences
between primary and secondary pre-service teachers’ acceptance of AI were tested.
The study based on the Technology Acceptance Model (Venkatesh & Bala, 2008). An adapted German
version of the instrument (Stephan, Markus & Gläser-Zikuda, 2019) was applied and consists of eight
subscales with 3-4 items each and good reliabilities (α = .69 to .88): Perceived Usefulness; Perceived
Ease of Use; AI Self-Efficacy; AI Anxiety; Perceived Enjoyment; Subjective Norm; Job Relevance,
Behavioral Intention.
Over 600 pre-service teachers participated voluntary in an online-survey administered with Unipark
during a lecture in December 2021 at one German university. Analyzes included a total of 405 (294
females, 108 males, 3 third gender; mean age 21.14 years; SD = 3.82) valid responses. 75.06% of
respondents were enrolled in the first semester (M = 1.76, SD = 1.48) of teacher training programs (n =
239 primary school; n = 166 secondary school). Structural equation modeling (SEM) was performed
using R software.
The results show that the participants report moderate levels in all subscales of AI acceptance, except
for Behavioral Intention (M = 2.81, SD = .88). The proposed model achieved a good model fit
(X2/df=2.28, CFI=.932, TLI=.920, RMSEA=.056[.051, .056], SRMR=.071). In the overall sample model,
only AI Anxiety does not influence pre-service teachers’ intentions to use AI indirectly; the remaining
study variables showed all differential effects on the intention to use AI. In the calculated female model,
Behavioral Intention is significantly determined by Subjective Norm (.297***); in the male model is no
significant effect. Secondary school pre-service teachers Subjective Norm has an influence on Perceived
Usefulness (.320***); but this is not the case for primary school pre-service teachers.
Main results of the study are presented and discussed in terms of acceptance research on AI and with
respect to their relevance for teacher education and school education.
APA:
Zhang, C., Schießl, J., Hofmann, F., Plößl, L., & Gläser-Zikuda, M. (2022). Pre-service teachers’ acceptance of Artificial Intelligence. Paper presentation at EARLI SIG 11 Conference 2022, Oldenburg, DE.
MLA:
Zhang, Chengming, et al. "Pre-service teachers’ acceptance of Artificial Intelligence." Presented at EARLI SIG 11 Conference 2022, Oldenburg 2022.
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