An exploratory machine learning approach to understanding determinants of future ChatGPT use in higher education

Abstract

AI tools such as ChatGPT are used in the higher education increasingly yet the limited empirical evidence explains how the students incorporate these tools into their academic work. This study examines how students’ perceptions and demographic characteristics relate to their intended use of ChatGPT academically. The study focuses on understanding the educational meaning rather than maximizing the algorithmic performance. The survey data were collected from 166 university students for this purpose. It is analyzed to identify the key learning related constructs associated with ChatGPT use. An exploratory machine learning (ML) framework was applied to examine the relationships among these constructs while maintaining the interpretability.

The analysis suggests that learning support, accessibility and academic engagement are the important factors that were associated with students’ intentions to use ChatGPT in academic contexts. These elements indicate that students often view AI tools as resources for clarifying the concepts, supporting coursework and enhancing the learning efficiency. At the same time, the concerns related to reliability, accuracy, and academic integrity influence how students evaluate the appropriate use of such tools. Overall, the findings suggest that ChatGPT adoption in higher education is shaped largely by the pedagogical and contextual conditions rather than technological performance alone. The study provides the exploratory insights that may inform the instructional design, AI literacy initiatives, and the responsible integration of generative AI (GAI) within the higher education environments.