The increasing integration of Artificial Intelligence (AI) tools in higher education presents new opportunities and challenges in shaping undergraduate learning experiences. However, limited empirical evidence exists on how such tools influence academic performance, learning efficiency, and student engagement. This study aims to evaluate the role of AI tools in enhancing undergraduate students' learning by examining the relationships among engagement, learning efficiency, and academic performance. Using a quantitative approach, data were collected through structured questionnaires from undergraduate students and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results reveal that student engagement has a significant positive effect on learning efficiency but does not directly affect academic performance. Likewise, learning efficiency showed no significant effect on academic performance. Despite the high internal consistency and validity of the model, the findings suggest that while AI tools enhance engagement and efficiency, these improvements do not automatically lead to better academic outcomes. The study concludes that to fully realize the benefits of AI in education, it must be supported by pedagogical strategies that bridge the gap between engagement, efficient learning, and measurable academic achievement.
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