Abstract

The integration of chatbots in the field of education has shown promising results in facilitating learning and improving educational outcomes. This quantitative, exploratory study aims to explore chatbots acceptance among teachers of special education in Saudi Arabia. Maximum-Likelihood (ML) Structural equation modeling (SEM) was performed to test hypothesized relations that exist among the six research constructs. A total of one hundred complete responses were received. The findings shows that special education’ teachers in Saudi Arabia have a weak degree of perceived usefulness (PU) of chatbots in education. While they reported a moderate degree of perceived ease of use, attitudes toward chatbots, self-efficacy, perceived risk, and behavioral intention (BI) to use chatbots. Findings also shows that the structural model fitted the data by supporting that Attitude (I), and BI are affected by PU, Perceived Ease of Use (PE), Self-Efficacy (SE), and Perceived risk (PR). As the paths coefficients >0.10, the direct effects of the Perceived Use, Perceived Ease of Use, SE, and PR are considered important to the Attitudes, and BI to use chatbots. Furthermore, the indirect effects of these variables on BI through attitudes were significant. The percentage of variance associated with attitudes was 70 and 79 percent for BI squared multiple correlations results indicate a strong relationship between the constructs and their factors and demonstrate the greater explanatory power of these factors in predicting the BI of special education teachers to use chatbots in education. Therefore, all the research hypotheses were supported by the data.