Factors Associated with AI Competency among University Students in Learning Mathematics and English Language

Abstract

The study explored the role of factors such as subjects of study (i.e., Mathematics and English Language learning), level of study (Undergraduate and postgraduate), access to digital resources, and AI engagement in shaping AI competency of university students in Nepal. A cross-sectional survey design was employed among 377 students from the undergraduate and postgraduate level of three constituent campuses of Tribhuvan University, Nepal. Independent sample t-test, ANOVA, and path analysis under structural equation modeling were the major statistical techniques applied to analyze the data. The findings revealed that students’ AI competency is moderate, with significant differences based on the subject of study and device access. Access to laptop and engagement in AI were important predictors of the students’ AI competency. The findings of the study imply that strengthening access to digital devices and promoting meaningful AI engagement across disciplines is essential in enhancing students’ AI use competency in higher education.