Acceptance of Artificial Intelligence among Pre-Service Teachers: A Multigroup Analysis

Autor/inn/en	Zhang, Chengming; Schießl, Jessica; Plößl, Lea; Hofmann, Florian; Gläser-Zikuda, Michaela
Titel	Acceptance of Artificial Intelligence among Pre-Service Teachers: A Multigroup Analysis
Quelle	In: International Journal of Educational Technology in Higher Education, 20 (2023), Artikel 49 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Zhang, Chengming)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1186/s41239-023-00420-7
Schlagwörter	Artificial Intelligence; Preservice Teachers; Technology Uses in Education; Student Attitudes; Educational Technology; Intention; Use Studies; Gender Differences; Foreign Countries; Stress Variables; Germany + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Schülerverhalten; Unterrichtsmedien; Benutzerschulung; Geschlechterkonflikt; Ausland; Deutschland
Abstract	Over the past few years, there has been a significant increase in the utilization of artificial intelligence (AI)-based educational applications in education. As pre-service teachers' attitudes towards educational technology that utilizes AI have a potential impact on the learning outcomes of their future students, it is essential to know more about pre-service teachers' acceptance of AI. The aims of this study are (1) to discover what factors determine pre-service teachers' intentions to utilize AI-based educational applications and (2) to determine whether gender differences exist within determinants that affect those behavioral intentions. A sample of 452 pre-service teachers (325 female) participated in a survey at one German university. Based on a prominent technology acceptance model, structural equation modeling, measurement invariance, and multigroup analysis were carried out. The results demonstrated that eight out of nine hypotheses were supported; perceived ease of use ([beta] = 0.297*) and perceived usefulness ([beta] = 0.501) were identified as primary factors predicting pre-service teachers' intention to use AI. Furthermore, the latent mean differences results indicated that two constructs, AI anxiety (z = -3.217) and perceived enjoyment (z = 2.556), were significantly different by gender. In addition, it is noteworthy that the paths from AI anxiety to perceived ease of use (p = 0.018) and from perceived ease of use to perceived usefulness (p = 0.002*) are moderated by gender. This study confirms the determinants influencing the behavioral intention based on the Technology Acceptance Model 3 of German pre-service teachers to use AI-based applications in education. Furthermore, the results demonstrate how essential it is to address gender-specific aspects in teacher education because there is a high percentage of female pre-service teachers, in general. This study contributes to state of the art in AI-powered education and teacher education. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01