The Effect of Using Artificial Intelligence on Learning Performance in Iraq: The Dual Factor Theory Perspective

The adoption of artificial intelligence applications in higher education plays an important role in the improvement of the quality of education and learning practices and overcomes many educational' issues. The purpose of this study is to examine the factors influencing learning performance by using artificial intelligence in the educational process. The research model has been developed based on dual factor concept through examining “enablers” and “inhibitors” factors of artificial intelligence adoption in higher education toward improving the Learning Performance. The research model has been built based on a combination of the following theories, constructivism, TAM3, UTAUT, BM, status quo bias theory. The hypothesized model is validated empirically via a questionnaire including 57-item based on 5-point Likert scales completed by 383 respondents (random sampled). Structural equation modeling was used to evaluate the proposed model by analyzing the confirmatory factor and path effects across the AMOS software. The results demonstrate that the indicators of model fitness showed good fit. As for the results of the hypothesis test, it is clear that the results of the analysis show that the interaction and the engagement of learning have a significant effect on the collaboration for learning and thus have a significant effect on the learning performance. Perceived enjoyment, perceived usefulness, and perceived ease of use have a significant effect on using artificial intelligence. Facilitating conditions have no significant impact on using artificial intelligence. Consciousness has a positive effect on use while perceived risks and resistance do not significantly affect use and learning performance. The use of artificial intelligence has a positive and significant effect on learning performance.