Impact of Generative Artificial Intelligence on Student Engagement, Self-Regulated Learning, and Academic Integrity in Higher Education

The rapid advancement of generative artificial intelligence (GAI) is transforming teaching and learning practices in higher education through automated content generation, personalized feedback, and interactive academic support. Despite its increasing adoption, empirical evidence regarding the pedagogical benefits and ethical implications of generative AI remains limited. The objective of this study is to examine the effects of generative AI use on student engagement, self-regulated learning, and academic integrity concerns in higher education. A quantitative cross-sectional research design was employed, and data were collected from 380 university students with prior experience using generative AI tools for academic purposes. Structural equation modeling (SEM) was used to test the proposed research model. The results indicate that generative AI use has a strong positive effect on student engagement (β = 0.52, p < 0.001) and a significant positive effect on self-regulated learning (β = 0.41, p < 0.001), demonstrating its potential to enhance active learning and learner autonomy. However, generative AI use also shows a significant positive relationship with academic integrity concerns (β = 0.46, p < 0.001), highlighting ethical challenges associated with AI-assisted learning environments. Furthermore, student engagement partially mediates the relationship between generative AI use and self-regulated learning (indirect effect β = 0.21, p < 0.01), indicating that engagement is a key mechanism through which generative AI influences deeper learning outcomes. These findings provide robust empirical evidence that generative AI plays a dual pedagogical and ethical role in higher education and offer practical implications for the responsible integration of AI technologies in educational contexts.