Factors Influencing Intention to Teach Artificial Intelligence in Saudi Universities: A Structural Equation Model

Abstract

This study aims to investigate factors affecting behavioral intention towards teaching AI in Saudi universities. A random sampling method was used. A total of 430 responses were received. There were 330 males (76.74%), and 100 females (32.25%). All participants were of Saudi nationality and spoke Arabic as their mother tongue. To investigate sample data and assess model fit, this study employs structural equation modeling (SEM). A self-report 15- item survey instrument was developed specifically for this research study, based on Technology Acceptance Model (TAM). Study variables showed significant correlations at the .01 level. BI correlates positively with Performance expectancy (PE), Effort expectancy (EE), Social influence (SI)and Facilitating conditions (FC) (r = .555, .655, .630 and .615 respectively). Each of PE, EE, SI made significant individual contributions to the prediction of BI. The results indicated that the following beta weights which represented the relative contribution of PE, EE, SI and FC to the prediction were observed. PE (b = .411, t = 5.890, P < 0.01), EE (b = .333, t = 5.780, P < 0.01), SI (b = .297, t = 5.230, P < 0.01), and FC (b = .299, t = 5.232, P < 0.01). Together they yielded a coefficient of multiple regression (R) of 0.788 and a multiple correlation square of 0.784. With regard to the academic contribution, this work builds upon previously established and validated literature while simultaneously providing a new conceptual model.

Keywords

• performance expectancy
• effort expectancy
• social influence
• facilitating conditions
• artificial intelligence