Factors Affecting the Adoption of AI-Based Applications in Higher Education: An Analysis of Teachers' Perspectives Using Structural Equation Modeling

• Published in: Educational Technology ＆ Society Vol. 24; no. 3; pp. 116 - 129
• Main Authors: Wang, Youmei, Liu, Chenchen, Tu, Yun-Fang
• Format: Journal Article
• Language: English
• Published: Palmerston North International Forum of Educational Technology ＆ Society 01.07.2021; International Forum of Educational Technology & Society
• Subjects: Acceptance testing; Adoption (Ideas); Analysis; Anxiety; Artificial Intelligence; Beliefs, opinions and attitudes; College Faculty; College teachers; Computer Uses in Education; Education; Education, Higher; Educational aspects; Foreign Countries; Higher Education; Intention; Modelling; Self Efficacy; Special Issue Articles; Structural equation modeling; Structural Equation Models; Teacher Attitudes; Teachers; Teaching; technology acceptance model; Technology application; Usability; China; Technology acceptance model; Anxiety; Higher education; Artificial intelligence; Self-efficacy
• DOI: 10.30191/ETS.202107_24(3).0009
• ISSN: 1176-3647; 1436-4522

Owing to the rapid advancements in artificial intelligence (AI) technologies, there has been increasing concern about how to promote the use of AI technologies in school settings to enhance students' learning performance. Teachers' intention to adopt AI tools in their classes plays a crucial role in this regard. Therefore, it is important to explore factors affecting teachers' intention to incorporate AI technologies or applications into course designs in higher education. In this study, a structural equation modeling approach was employed to investigate teachers' continuance intention to teach with AI. In the proposed model, 10 hypotheses regarding anxiety (AN), self-efficacy (SE), attitude towards AI (ATU), perceived ease of use (PEU) and perceived usefulness (PU) were tested, and this study explored how these factors worked together to influence teachers' continuance intention. A total of 311 teachers in higher education participated in the study. Based on the SEM analytical results and the research model, the five endogenous constructs of PU, PEU, SN, and ATU explained 70.4% of the changes in BI. In this model, SN and PEU were the determining factors of BI. The total effect of ATU was 0.793, followed by SE, with a total effect of 0.554. As a result, the intentions of teachers to learn to use AI-based applications in their teaching can be predicted by ATU, SE, PEU, PU and AN. Among them, teachers' SE positively influenced teachers' PEU and ATU towards adopting AI-based applications, and also influenced PU through PEU. In addition, the relationship between teachers' SE and AN was negatively correlated, which indicated that enhancing teachers' SE could reduce their AN towards using AI-based applications in their teaching. Accordingly, implications and suggestions for researchers and school teachers are provided.