The Role of Generative Large Language Models in Enhancing Student Engagement in Higher Education: A Systematic Literature Review

The rapid advancement of generative large language models (LLMs) has sparked significant interest in their poten- tial to transform higher education, particularly in fostering student engagement. While these models offer novelopportunities for personalized learning and interactive experiences, their integration into academic settings remains underexplored, with varying implications for pedagogy, ethics, and institutional policy. This systematic literaturereview examines the role of generative LLMs in enhancing student engagement across multiple dimensions, includ- ing their impact on learning outcomes, academic writing, subject-specific applications, and ethical considerations.We synthesize existing research to identify key trends, challenges, and gaps in the current understanding of how these technologies are reshaping educational practices. A rigorous methodological approach was employed to select and analyze relevant studies, ensuring a comprehensive evaluation of the field. The findings reveal that generative LLMs can significantly influence student engagement by facilitating adaptive learning environments and supporting creative problem-solving; however, concerns about academic integrity, equitable access, and pedagogical alignment persist. The review also highlights emerging tools and systems designed to integrate LLMs into education, alongside institutional and student perspectives on adoption. Based on the synthesized evidence, we discuss future directions for research and policy, emphasizing the need for balanced frameworks that harness the benefits of generative AI while addressing its risks A. Chowdhury et al., 2025. This study contributes a structured overview of the current landscape, offering insights for educators, researchers, and policymakers navigating the evolving intersection of AI and higher education.