Abstract:

The accelerated integration of Generative Artificial Intelligence (GenAI) within higher education settings has fundamentally transformed student interactions with technology-mediated educational platforms. Despite this growing trend, limited research has examined the determinants of students’ willingness to continue using GenAI tools, particularly within Arab higher education contexts. The investigation addresses existing research limitations through the integration of the DeLone and McLean Information Systems Success Model (D&M IS) with the Expectation-Confirmation Model (ECM), while incorporating additional constructs such as trust, perceived risk, and price value. Data gathered via web-based questionnaires from a sample of 594 undergraduate and postgraduate students underwent analysis using partial least squares structural equation modelling (PLS-SEM) to validate the proposed theoretical model. The findings revealed that satisfaction emerged as the primary predictor driving continuance intention, with price value ranking second in importance. Despite this, performance expectancy showed no significant effect on continuance intention. The study also confirmed that system quality, information quality, and service quality positively influenced both student trust and satisfaction. Perceived risk demonstrated a positive relationship with trust in GenAI tools, while confirmation significantly shaped both satisfaction and performance expectancy.