Examining the educational use of generative AI by integrating ECT and TTF using a hybrid SEM-ANN approach

Abstract

This cross-cultural study aimed to investigate the critical factors that predict the use of generative AI technology for educational purposes. In this context, a robust conceptual framework is developed based on “Task Technology Fit” (TTF) and “Expectancy Confirmation Theory” (ECT). The research model, a key component of this study, is rigorously validated using a “Structural Equation Modeling” (SEM) and “Artificial Neural Network” (ANN) approach based on data from 376 undergraduate students in Türkiye and Kuwait. The PLS-SEM results showed that task technology fit, expectation-confirmation, and individual technology fit significantly predicted satisfaction with AI tools. These three independent variables explain 67% of the variation in satisfaction, indicating a strong predictive power of the model. The multigroup analysis showed a significant difference in the relationship between continuance intention and satisfaction in both countries. Further, the ANN results indicated that expectation confirmation is the most critical factor in predicting satisfaction. The findings provide novel theoretical contributions to the AI literature, particularly in understanding the cultural factors that influence the adoption of AI. They also offer practical implications for educators, guiding the responsible adoption and use of generative AI technologies in higher education institutions.

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