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Numerous studies have highlighted the transformative role of AI tools in education; however, little is known about how digital distraction impacts students’ actual use of ChatGPT. This study aimed to develop a model that examined five predictors of reflective ChatGPT use among Indian English for Science and Technology (EST) students. The proposed model was based on UTAUT2 and the Expectation–Confirmation Model. Learning Support, Academic Confidence, Academic Integrity, Dialogic Engagement with AI, and Peer Influence were selected as predictors to explain reflective use. The model also examined how reflective use contributed to digital distraction and self-perceived academic outcomes. Data were collected from 596 engineering undergraduates, and Partial Least Squares Structural Equation Modelling and machine learning classifiers were used for analysis. Four predictors showed positive effects on Reflective AI Use, while Dialogic Engagement showed a negative effect. Reflective AI Use increased Digital Distraction Susceptibility, which paradoxically related positively to Self-Perceived Academic Performance. The J48 classifier demonstrated the strongest predictive accuracy.
Qamar et al. (Mon,) studied this question.