Generative AI in Higher Education: Investigating How Perceived Usefulness and Usage Patterns Influence Student Engagement and Academic Performance
Keywords:
Generative AI; Higher Education; Technology Integration; Cognition and Learning Outcomes; Perceived UsefulnessAbstract
This study examines the influence of generative Artificial Intelligence (AI) tools on student learning outcomes in higher education. A questionnaire developed using two validated instruments (perceived usefulness of generative AI tools among students and the level of interest of students’ interaction with study materials after using generative AI tools) was used to collect primary data from 208 students across four institutions. The collected data were analyzed using descriptive statistics, inferential statistical methods, and Partial Least Squares Structural Equation Modeling (PLS-SEM). The outcome of the analysis highlights the ability of generative AI to enhance academic engagement, intellectual curiosity, and personalized learning experiences. Key findings include the high perceived usefulness of generative AI in understanding complex topics and connecting coursework to real-world applications. Also, generative AI has the potential to support active knowledge construction and cognitive development, offering actionable insights for educators and policymakers. Challenges such as limited technical training for academics and data privacy concerns are identified as factors that reduce the positive impact of generative AI in student learning outcomes. Conclusively, generative AI could be used to enhance learning outcomes and streamline educational processes.
https://doi.org/10.26803/ijlter.24.11.32
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