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An Intelligent Tutoring System (ITS) is a computer-based software designed to replicate the guidance and behaviour of a human tutor. By adapting its instructional approach, the ITS functions as a personalized tutor for each learner. Despite the various intelligent features integrated into existing ITS frameworks, previous studies have identified several challenges, including limitations in accurately measuring and predicting students’ performance, as well as handling uncertainty in learner interactions. This study introduces an enhanced student model aimed at addressing these issues. New criteria and formulas for assessing and predicting students’ performance or knowledge states have been developed and embedded within the student module engine of the proposed ITS. The system was designed with improved functionalities and an interactive interface, followed by comprehensive testing to evaluate its performance and prediction accuracy. Experimental results demonstrated that the system achieved a prediction accuracy rate of 95% for most students. These findings confirm the reliability of the proposed performance measurement and prediction mechanisms. Furthermore, this innovation contributes to sustainable tutoring by reducing dropout rates, promoting equal learning opportunities, fostering adaptability and lifelong learning, strengthening communities, and ultimately advancing social sustainability.