Acceptance of Artificial Intelligence among Pre-Service Teachers: A Multigroup Analysis
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| Title: | Acceptance of Artificial Intelligence among Pre-Service Teachers: A Multigroup Analysis |
|---|---|
| Language: | English |
| Authors: | Zhang, Chengming (ORCID |
| Source: | International Journal of Educational Technology in Higher Education. 2023 20. |
| Availability: | BioMed Central, Ltd. Available from: Springer Nature. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: customerservice@springernature.com; Web site: https://www.springer.com/gp/biomedical-sciences |
| Peer Reviewed: | Y |
| Page Count: | 22 |
| Publication Date: | 2023 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, Preservice Teachers, Technology Uses in Education, Student Attitudes, Educational Technology, Intention, Use Studies, Gender Differences, Foreign Countries, Stress Variables |
| Geographic Terms: | Germany |
| DOI: | 10.1186/s41239-023-00420-7 |
| ISSN: | 2365-9440 |
| 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. |
| Abstractor: | As Provided |
| Entry Date: | 2023 |
| Accession Number: | EJ1391151 |
| Database: | ERIC |
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| 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. |
|---|---|
| ISSN: | 2365-9440 |
| DOI: | 10.1186/s41239-023-00420-7 |
