Investigating ChatGPT Methods in the Higher Education Form a Student Engagement Perspective: Based on a Descriptive-Cross-Sectional Study

Abstract

A significant gap exists in the current body of knowledge regarding the acceptance and utilization of AI tools, especially ChatGPT among students to adopt this technology for higher education. This study examines student involvement in higher education using ChatGPT in a developed and developing countries. The study examines how system quality, information quality, and facilitating conditions affect students’ ChatGPT engagement and intention to use. Quantitative data from US and Bangladeshi university students was used. Structural equation modelling (SEM) was used to evaluate the associations between student interest, behavioral intention, and privacy concerns in 452 online questionnaire responses. All construct elements have VIF values between 1.6333 and 3.085, indicating no multicollinearity with explanatory variables. The KMO and Bartlett score of .965 with the significance of .000 suggests good statistical dependability for data analysis. The results show that information, system, and supporting conditions greatly affect student ChatGPT involvement. The developed and developing environments differed in technology infrastructure and privacy issues, with Bangladeshi students expressing greater data security concerns. The report helps educators and policymakers in developed and emerging nations improve student engagement with AI technologies in higher education. To maximize benefits, institutions must handle privacy and security concerns and provide strong technological support. This research provides comparative insights into how emerging AI technologies are shaping student engagement in higher education across diverse economic settings through SmartPLS, SPSS, and Python software to offer the contextual factors of ChatGPT adoption in education.

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