Pre-service teachers’ acceptance of Artificial Intelligence - Zhang, Chengming; Schießl, Jessica; Hofmann, Florian; Plößl, Lea; Gläser-Zikuda, Michaela (Digital Transformation in Teaching and Teacher Education, EARLI SIG 11 conference 2022, 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. <br
Keywords: Artificial intelligence; Educational technology; Pre-service teacher education; Quantitative methods