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Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model
- Source :
- Computers and Education: Artificial Intelligence, Vol 7, Iss , Pp 100328- (2024)
- Publication Year :
- 2024
- Publisher :
- Elsevier, 2024.
-
Abstract
- The rapid development of generative artificial intelligence (GenAI) tools has given rise to a growing discussion of the potential challenges and benefits that the use of these technologies may present in the field of education. This study examines the acceptance of the use of GenAI tools for teaching and learning among primary and secondary school teachers in Hong Kong. It uses an extension of the technology acceptance model (TAM) with a modified framework that incorporates two key factors: self-efficacy and subjective norm. Data were collected from a sample of 367 primary and secondary school teachers in Hong Kong using questionnaires containing items for six constructs: self-efficacy, perceived usefulness, perceived ease of use, attitude towards using, subjective norm, and behavioural intention. The results show that fostering teachers' self-efficacy, perceived usefulness, and attitude is essential for successfully increasing their behavioural intention to use GenAI tools. Subjective norm was also found to influence teachers' behavioural intention. To enhance teachers' effective use of GenAI for teaching, teacher development programmes should focus on equipping teachers with comprehensive conceptual knowledge and skills and an understanding of the application of these tools to teaching and learning. Policy support to create a conducive environment for the use of GenAI in teaching and learning would also be beneficial. The study has theoretical implications in its extension of the TAM model as well as implications for enhancing teachers’ AI literacy and developing pedagogies for the meaningful use of GenAI tools for teaching and learning in K–12 settings.
Details
- Language :
- English
- ISSN :
- 2666920X
- Volume :
- 7
- Issue :
- 100328-
- Database :
- Directory of Open Access Journals
- Journal :
- Computers and Education: Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.8a9c0f6a61424b2e80961714836183e6
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.caeai.2024.100328